Research Article5 G WiFi Signal-Based Indoor Localization SystemUsing Cluster 119896-Nearest Neighbor Algorithm

Feng Yu1 Minghua Jiang1 Jing Liang1 Xiao Qin2 Ming Hu1 Tao Peng1 and Xinrong Hu1

1School of Electronic and Electrical Engineering Wuhan Textile University Wuhan 430200 China2Department of Computer Science and Software Engineering Auburn University Auburn AL 36849 USA

Correspondence should be addressed to Minghua Jiang jmhwtueducn

Received 7 August 2014 Revised 16 November 2014 Accepted 30 November 2014 Published 17 December 2014

Academic Editor Wen-Hwa Liao

Copyright copy 2014 Feng Yu et al This is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Indoor localization based on existent WiFi signal strength is becoming more and more prevalent and ubiquitous Unfortunatelythe WiFi received signal strength (RSS) is susceptible by multipath signal attenuation and environmental changes which isthe major challenge for accurate indoor localization To overcome these limitations we propose the cluster 119896-nearest neighbor(KNN) algorithmwith 5GWiFi signal to reduce the environmental interference and improve the localization performance withoutadditional equipment In this paper we propose three approaches to improve the performance of localization algorithm For onething we reduce the computation effort based on the coarse localization algorithm For another according to the detailed analysisof the 24G and 5G signal fluctuation we expand the real-time measurement RSS before matching the fingerprint map Moreimportantly we select the optimal nearest neighbor points based on the proposed cluster KNN algorithm We have implementedthe proposed algorithm and evaluated the performance with existent popular algorithms Experimental results demonstrate thatthe proposed algorithm can effectively improve localization accuracy and exhibit superior performance in terms of localizationstabilization and computation effort

1 Introduction

Recently indoor localization achieved through wireless net-work signal and mobile devices has received much attentionIt can provide an accurate localization in indoor environ-ment such as shopping malls airports hospitals subwaysand university campuses For outdoor localization environ-ment global navigation satellite systems (GNSS) such as GPS[1 2] have been used in a wide range of applications includingtracking transport navigation and guidance Although GPSworks extremely well in outdoor localization unfortunatelyit does not perform well in indoors urban canyons con-struction and basement and places close to the wall as thesignal from the GPS satellites is too weak to come acrossmost construction thus making GPS hard for indoor local-ization Attempting to find the accurate indoor localizationmany indoor localization technologies are proposed suchas infrared [3] ultrawideband (UWB) [4] ultrasonic [5]Bluetooth [6] Radio Frequency Identification (RFID) [7]

Zigbee [8] frequency modulation (FM) broadcast [9 10]geomagnetism [11] and Wireless Fidelity (WiFi) [12ndash16]

The mobile computing technology and the increasingavailability of WiFi networks have enabled more accu-rate localization in indoor environments Most WiFi-basedindoor localization models for commercial uses are basedon received signal strength WiFi-based localization systemshave several advantages Firstly in terms of cost effect theWLAN deployment of localization algorithms does not needany additional hardware as network interface cards measuresignal strength values from all wireless access points (APs) inrange of the receiver Therefore RSS needed for localizationcan be obtained directly fromnetwork interface cards that areincluded in most mobile devices Due to the ubiquity ofWiFinetworks this mode of localization provides a particularlycost-effective solution for offering location based service(LBS) in commercial and residential indoor environmentsFor the low cost and wide availability without the need for

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2014 Article ID 247525 12 pageshttpdxdoiorg1011552014247525

2 International Journal of Distributed Sensor Networks

additional hardware the WiFi indoor localization is becom-ing increasingly prevalent and ubiquitous

There are different choices for the localization measure-ment methods such as time of arrival (TOA) [17 18] timedifference of arrival (TDOA) [19] angle of arrival (AOA) [20]channel state information (CSI) [21 22] and received signalstrength (RSS) [23ndash26] RSS is generally the feature of choicefor indoor WiFi localization due to its low cost without theneed for additional hardware at our mobile devices

Most RSS-based indoor localization approaches adoptfingerprint algorithm as the basic scheme of indoor localiza-tion Even though fingerprint algorithm has been successfulinmany localization systems [14 24 27 28] it exhibits severalchallenges when considering real indoor environment

(i) Environmental variations and interferences ideallysignal variations should only be affected by distanceThe longer the distance to an access point (AP) thesmaller the RSS However interferences and someundesired effects such as reflection can make thesystem detect a signal at a smaller RSS being closer tothe AP These may be caused by different set of con-ditions The fingerprint is sensitive to environmentalchanges such as an object moving into the buildingdiffraction and reflection which result in changes insignal propagation

(ii) 24G as an unlicensed spectrum this means thatdifferent hardware and applications apart from WiFican freely use this spectrum to bring interference andnoise The most common electrical equipment whichuses this frequency and can interfere with WiFi ismicrowaves that are the household electronic devicesto emit interference in the 24G band Bluetoothdevices telecommunications devices wireless secu-rity cameras wireless speakers and so on

(iii) The time-consuming offline training phase to buildthe fingerprint map during the fingerprint collectionphase there are enough empirical manual measure-ments to build fingerprint map

In this work in order to reduce the manual effort duringthe calibration phase we have exploited a computer-aided software which allows us to automatically build the fingerprintmap This software consists of leading in the indoor layoutand then exploiting fingerprint automatically at each refer-ence point based on the application software For solvingthe problem of the signal is unsteadiness we propose anindoor localization system using the cluster KNN algorithmit aims at both reducing the interference to improve local-ization accuracy based on 5G signal and using cluster KNNalgorithm to reduce the computation effort but improve thelocalization accuracy and stabilization

The remainder of this paper is organized as followsSection 2 discusses how related work available in the liter-ature approaches such issues A brief description of indoorlocalization system is presented in Section 3 Section 4describes the coarse and precise localization algorithm indetail The characteristics of 24G and 5G band signal areanalysed in Section 5The experiment results and evaluations

through implementation are listed in Section 6 FinallySection 7 concludes the paper

2 Related Work

21 Fingerprint Collection Themajor problem of fingerprintalgorithm is the exhaustive survey needed to build thefingerprintmap a task that requires substantial cost and timeAnother important issue of this fingerprint map is that arecalibration is needed every time the environment changes

There are mainly two methods to build fingerprintmap empirical manual measurement [14 15] and computedanalytically based on the signal propagation model [12 2529] Because signal propagation model is easily affectedby environmental changes normally the fingerprint mapis built with manual effort In this phase the fingerprintmap is surveyed for all the reference points (RPs) Basicallyfingerprint map is a database of reference points at prede-fined points (coordinates) coupled with various radio signalstrength characteristics for example RSS signal angles orpropagation time called signal fingerprint Step by step forevery fingerprint there must be a measurement that includesthe information about all positions and their received signalstrength

The popular researches highlight the strong needs ofapproaches aiming at reducing the time associated with theoffline training phase of fingerprint algorithm In [30] theneed of an approach capable of reducing the heavy effort ofthe training phase is indicated as one of the key challenges infingerprint In [31] it is proposed that a valid training phaseis hardly bearable since it requires collecting a large numberof fingerprints To reduce such fingerprint it is presented totrade localization error against time thus reducing the timeneeded to train the fingerprint map [31] Homoplastically ithas been proposed in [32] that a huge amount of receivedsignal strength is usually required for training and typicallymuch time is necessary to collect such amount of trainingfingerprint For this reason it has been stated in [32] that areduction of themanual effort can be achieved byminimizingthe sampling time at each reference point (RP) and bylimiting the number of positions to sample Nevertheless thissimple approach makes inaccurate fingerprint map whichdecreases the accuracy of the location estimation [32]

Attempting to develop training methods that try toreduce the training phase of fingerprint map have beenproposed in [33 34] Some works also propose training thefingerprint by using a mobile device such as a smartphoneemployingWiFi scans transparently to the user [35] In orderto reduce the manual effort during the calibration phase wehave defined a computer-aided approach which allows usto automatically build the fingerprint map In this systema software application is developed to build the fingerprintmap It is developed based on Android Java development kit170 and Android software development kit 44 The generalsteps of the software application are as follows

(i) Open the software application and import indoorworkspace plan

(ii) Select points as reference points in the indoor mapcollect real-time RSS and store it in fingerprint map

International Journal of Distributed Sensor Networks 3

(iii) After collecting fingerprint information of all refer-ence points if the indoor environment changes weshould reconstruct fingerprint map only selectingbrush-fire reference points and renovating the finger-print map

(iv) Click the ldquolocalizationrdquo button and we should obtainour immediate position

22MatchingAlgorithm During the last years several finger-print localization algorithms have been proposed The keyidea of fingerprint algorithm is to find the optimal nearestneighbor points In an attempt to find the best matchingalgorithm and try to improve the localization accuracy manyresearchers propose the nearest neighbor (NN) 119896-nearestneighbor (KNN) [36] weighted 119896-nearest neighbor (WKNN)[37] Bayesian probabilistic model (BPM) [38] artificialneural network (ANN) [39] and support vector machine(SVM) [40 41] to obtain the optimal nearest neighbor points

Although the above algorithms can achieve adequatelocalization performance the computing and memoryrequirements have to be taken into consideration While itis true that smart mobile devices are high capable machinesthe users themselves do not want an application that takesgigabytes of data just to improve accuracy in localizationSo a tradeoff between accuracy and complexity of algorithmis needed In order to satisfy the acceptable localizationaccuracy but with low computation effort we propose anefficient indoor localization system

In our proposed system the localization includes coarseand precise localization phases The coarse phase is toreduce the computation and then further localization usescluster KNN algorithm With the society developing andwireless network popularizing the constructions are largerand more complex it indicates that the fingerprint map islarger and larger During the online localization phase if wematch the real-time RSS with the whole fingerprint mapthe computation is too large for efficient localization Forreducing the computation effort and localization time thecoarse localization algorithm is proposed to reduce matchingfingerprint data by the detected APs Besides by realisticexperiment we find that the same value RSS points arescattered sometimes This phenomenon should cause largelocalization error By comparison with current localizationalgorithms the cluster KNN algorithm can remove thediscrete nearest neighbor points and obtain optimal nearestneighbor points

3 System Architecture

As in most fingerprint approaches the proposed system con-sists of an offline training and an online localization phaseThe offline training phase is responsible for collecting signalstrength from each reference point (RP) and then recordingit in the fingerprint database At the online localization phasethe mobile devices collect the real-time measurement RSSand compare it with the available fingerprint in the database

31 Offline Training Phase During the offline training phasea designer of a fingerprint localization system has to deal withtwo distinct problems as follows

(i) Fingerprint collection this includes how to efficientlyobtain fingerprint map in the large and complexindoor construction

(ii) Search of the optimal APs placement this includeshow many APs are there and where they have to beplaced to obtain an optimal localization accuracy

There are three distinct problems that we should considerin the fingerprint collection first how to reduce the time ofcollection second how to reconstruct the fingerprint mapefficiently when the indoor environment changes third howto extract the fingerprint characteristic of reference pointsIn our system the fingerprint map is built by the developedsoftware application and the detailed steps are showed inSection 21 The software application not only builds finger-print map conveniently but also renews partial fingerprintinformation of reference points without reconstructing thewhole fingerprint map During the fingerprint collection weselect RSS as fingerprint characteristic and choose mediaaccess control (MAC) as identity of different APs Becausethe service set identifier (SSID) of AP may change artificiallyin realistic life the MAC is sole for every AP The raw setof RSS time samples are collected from AP119894 at RP119895 withorientation 119900 by software automatically Since RSS variesnoticeably due to interferences and environment conditionsseveral consecutive RSSmeasurements need to be collected ateach reference point with a period of time Then the averageof the RSS time samples is computed and stored in fingerprintdatabase Such fingerprint map can be represented by Ω(119900)

Ω(119900)=(((

(

120574(119900)11 120574(119900)12 sdot sdot sdot 120574

(119900)

1119873

120574(119900)21 120574(119900)22 sdot sdot sdot 120574

(119900)

2119873

d

120574(119900)

1198711120574(119900)

1198712sdot sdot sdot 120574(119900)

119871119873

)))

)

(1)

where 120574(119900)119894119895

is a fingerprint from AP119894 at RP119895 with orientation119900 for 119894 = 1 2 119871 119895 = 1 2 119873 and 119900 isin 120590 =

119891119900119903119908119886119903119889 119887119886119888119896 119871 is the total number of APs and 119873 is thenumber of RPs

The placement of the APs consists of identifying theoptimal APs placement which achieves a reasonable radiosignal coverage of the workspace In general increasing thenumber of APs can improve the localization accuracy but interms of economic investment it increases deployment costsand the amount of time for the RSS collection from all APswhile establishing localization infrastructure and fingerprintmap However the goodness of a placement pattern highlydepends on the specific workspace conditions such as wallpositions andmaterials space topography noise sources andthe stream of people in the workspace

In [42] the experimental performance shows that ifwe place the APs in symmetric positions distributed over

4 International Journal of Distributed Sensor Networks

Require fingerprint mapΩ(119900)Require a real time measurement RSS 120574(119900)

119896119904 120574(119900)

119896119904isin 120574(119900)

119894119895 119894 = 1 119871 119895 = 1 119873Ensure coarse localization regionΩ(119900)

1015840

(1) the undetected AP 120574(119900)119894119895 larr 100(2) while 120574(119900)

119896119904= 119899119906119897119897 do

(3) if 120574(119900)119896119904= 100 then

(4) the unknowed point is not adjacent to 119896-st AP(5) delete 119896(6) else(7) the unknowed point is adjacent to 119896-st AP(8) store 119896(9) end if(10) end while(11) count all 119896(12) return coarse localization region Ω(119900)

1015840

Algorithm 1 Coarse localization algorithm

the experimental workspace in such a way that the averagesignal power is high it is likely to be one of the best choicesto reduce localization error In order to reduce the noise as faras possible if the APs can be deployed by ourselves it is betterto be distributed evenly around the workspace as frontal way

32 Online Localization Phase The online localization phaseconsists of coarse localization and precise localization Thegoal of the coarse localization phase is to reduce the impossi-ble regions from all the fingerprintmapThus it removes par-tial impossible fingerprint and reduces the computation effortof the precise localization phase The greater the fingerprintmap is the bigger the reduction impossible regions are as isshown in Section 62 In the precise localization phase thelocalization is computed by using the cluster KNN algorithmA detailed introduction is given in the following

4 The Efficiency of Cluster KNN

41 Coarse Localization Due to the wide deployment of APsthe total number of detectable APs is generally much greaterthan that required for localization which leads to redundantcomputations Furthermore unreliable APs with large RSSvariances may also lead to biased estimation and reduce thestability of the localization accuracyThismotivates the use ofAPs selection techniques to select a subset of available APs forcoarse localization Since the mobile devices detect differentnumber of APs in different region the detected APs can beused to coarse localization

The coarse localization is processed by comparing thedetected APs to infer the rough localization region The real-time measurement RSS can be represented as the columns of120574(119900)

119896119904= [120574(119900)1119904 120574(119900)2119904 120574

(119900)

119871119904]119879 119904 isin 119873 where the superscript 119879

denotes transposition Search for the 120574(119900)119896119904

from the fingerprintmapΩ(119900) based on the detected APs If a fingerprint includesall the detected APs we can infer that the mobile devices arein this regionThe algorithm is represented as in Alogrithm 1

5 10 15 20 25 30 35minus10

minus505

1015

Reference points

Fluc

tuat

ion

24G WiFi signal5G WiFi signal

Figure 1 Signal fluctuation

42 Precise Localization The major challenge for accurateRSS-based localization comes from the variations of RSS dueto the dynamic and unpredictable nature of radio channeland the RSS is easily affected by environmental changessuch as shadowing and multipath It is variable that the RSSis received from the same point at different time so thereal-time measurement RSS is not too credible for accuratelocalization To decrease the effect with variational signal andanalysis upon 24G and 5GWiFi signal we get that absolutevalue of signal fluctuation is under 5 dBm as is shown inFigure 1 and about 93 fluctuation is within 5 dBm in all thefluctuations of measurement (24G and 5G band signal) Inour experiment the 5G signal fluctuation is all within 5 dBmand about 86 fluctuation is within 5 dBm in the 24G signalTo avoid the inaccurate nearest neighbor points match withunauthentic real-time RSS prior to conducting localizationalgorithm the RSS is expanded by plusmn5 dBm and then it usedfor localization

In addition the ecumenic signal propagation model is

119871119901 (dBm) = 119871119901 (119889119900) + 10119899log10 (119889

1198890

) + 119883120590 (2)

where119871119901(119889119900)parameter represents the path loss at a referencedistance 1198890 typically onemeter 119899 is the constant propagation

International Journal of Distributed Sensor Networks 5

Figure 2 The distribution of qualified nearest neighbor points

Require coarse localization region Ω(119900)1015840

Require a real time measurement RSS 120574(119900)

119896119904 120574(119900)

119896119904isin 120574(119900)

119894119895 119894 = 1 119871 119895 = 1 119873Ensure precise localization coordinate (119909 119910)(1) for all 120574(119900)1015840

119896119904= 120574(119900)

119896119904plusmn 5 dBm do

(2) end for(3) for all ManDist (120574(119900)

1015840

119896119904 Ω(119900)1015840

) =sum119899

119894=1

100381610038161003816100381610038161003816120574(119900)1015840

119896119904minus Ω(119900)1015840 100381610038161003816100381610038161003816do

(4) end for(5) obtain nearest neighbor points set 120574(119900)

119896119899

(6) while Θ = (120574(119900)

119896119899119886 120574(119900)

119896119899119887

120574(119900)

119896119899119888) = 119899119906119897119897 do

(7) if min (119899120572 119899120573 119899120579) = (1003816100381610038161003816119899119886 minus 119899119887

1003816100381610038161003816 +1003816100381610038161003816119899119887 minus 119899119888

1003816100381610038161003816) le (10038161003816100381610038161003816119899120572 minus 119899120573

10038161003816100381610038161003816+10038161003816100381610038161003816119899120573 minus 119899120579

10038161003816100381610038161003816) (120572 120573 120579) isin 119899 then

(8) min (119904 (119899119886 119899119887 119899119888)) = (1003816100381610038161003816119904 minus 119899119886

1003816100381610038161003816 +1003816100381610038161003816119904 minus 119899119887

1003816100381610038161003816 +1003816100381610038161003816119904 minus 119899119888

1003816100381610038161003816) le (1003816100381610038161003816119904 minus 119899120572

1003816100381610038161003816 +10038161003816100381610038161003816119904 minus 119899120573

10038161003816100381610038161003816+1003816100381610038161003816119904 minus 119899120579

1003816100381610038161003816) (120572 120573 120579) isin 119899(9) obtain the optimal nearest neighbor points set Θ = (120574

(119900)

119896119899119886 120574(119900)

119896119899119887

120574(119900)

119896119899119888)

(10) else(11) continue(12) end if(13) end while(14) return precise localization coordinate (119909 119910) =((119909119899119886 + 119909119899119887 + 119909119899119888) 3 (119910119899119886 + 119910119899119887 + 119910119899119888) 3)

Algorithm 2 Precise localization algorithm

value 119889 is the distance between the transmitter and thereceiver devices and 119883120590 is a Gaussian random variablewith mean 0 and standard deviation 120590 From the signalpropagation model when the mobile devices are equidistantfrom the AP in line of sight the mobile devices receivethe same signal strength This phenomenon should causelarge error for localization In Figure 2 the same colorpoints represent the qualified nearest neighbor points if thelocalization algorithm is processed based on all the qualifiedpoints the localization error will be very large

In our experiment the range of signal strength is betweenminus30 dBm and minus99 dBm and all the RSS of undetected APsrepresentsminus100 dBmMany different RPs have the same value

of RSS in the fingerprint map if we use traditional nearestneighbor algorithms to find the optimal matching RPs itwill produce large error for localization For the sake ofsearching the optimalmatchingRPs not only the value of RSSbut also the relationship of RPs is considered In the offlinetraining phase the fingerprint map is collected in sequentialorder and not only the subscript of RPs does represent theserial number but also the 119895 implies the relationship of RPsGenerally the coterminous RPs have analogical value of RSSand in the process of searching for the nearest neighbor pointsthe real-time RSS should be matched with the coterminousRPs that have the analogical value of RSS The detailedalgorithm is represented as in Algorithm 2

6 International Journal of Distributed Sensor Networks

According to precise localization algorithm we selectthree optimal nearest neighbor points 119899119886 119899119887 and 119899119888 as local-ization the ultimate localization estimation is represented as(119909 119910)

119909 =1

3(119909119899119886 + 119909119899119887 + 119909119899119888)

119910 =1

3(119910119899119886 + 119910119899119887 + 119910119899119888)

(3)

5 Accurate Localization Based on 5 GWiFi Signal

The indoor radio environment is quite complex Becausethe 24G is no permit limitation band a wide variety ofequipment use the band at the same time It is unavoidableto be interfered by the same frequency equipment that hasbeen introduced in Section 1 The interference signal willgenerate signal fluctuation when we detect the WiFi signalfrom surrounding access points

The entry level speed of 5GWiFi is 433Mbps which is atleast three times compared to that of the 24G WiFi and thehigh performance of 5G WiFi can reach more than 1GbpsThe high transmission rate can satisfy usersrsquo daily surfingneeds and provide stable and high quality signal as well

In this section we explore the impact of frequencyband (24G and 5G) to evaluate the localization accuracyWhile 24G signal is the only band originally used for WiFiincreasingly 5G signal is also used despite of its poorerpropagation characteristics resulting from higher frequencyoperation As the 5G frequency band is less crowded thereis far more spectrum available in 5G band From a WiFifingerprint localization system perspective in a typical envi-ronment today with APs using both 24G and 5G bands ameasurement RSS collected during either the offline trainingphase or the online localization phase will likely include amixof 24G and 5G APs

Figure 3 shows that the signal strength of 5G is strongerthan that of 24G Generally stronger signal is more stableand stable signal can guarantee the high localization accuracy

Besides this from Figures 4 and 5 we can get that the5G signal is more stable than that of 24G in the sameenvironment From 30-st RP to 35-st RP the locus of 30-st RPsim35-st RP in Figures 4 and 5 indicates the 5G signalhas poorer propagation characteristics resulting from higherfrequency operation

Today more and more APs have double frequency bandand the 5G signal seems to be muchmore suitable for indoorlocalization than 24G signal Due to a lack of cochannelinterference it can be feasible to use more stable RSS for thepurpose of accurate localization So the 5G signal is selectedas localization estimation in our localization system

6 Experiments and Evaluations

61 Experiments Setup In this section we present the imple-mentation and experimental evaluations of the proposed sys-temThefingerprint database is collected by the smartphones

its network card can detect both 24G and 5G signals Theworkspace is equipped with WiFi environment by five TP-LINK WDR6300 routers that can emit 24G and 5G WiFisignal The total area is 36 times 258 meters consisting ofhallways and some classroomsWe collect 40 reference pointsevenly from the hallways region and obtain WiFi fingerprintdatabase using smartphone with the application software thatis our developed mobile software application to collect RSSand build the fingerprint map automaticallyWhen collectingthe fingerprint map the smartphones are kept at the sameheight of approximately an adultrsquos breast and sometimesrotated horizontally at the same position to face differentdirections In particular for the RPs data 20 samples at eachRP with a rate of 1 samplesecond are collected by a userwalking through the hallway area

611 Performance Metric Besides that according to [9 43]Manhattan distance performs slightly better than Euclideandistance and our workspace is a regular rectangle we usethe Manhattan distance as the standard of error analysisTheManhattan distance is an expression of geometric metricspace It is defined as the sum of the absolute differences ofvalues in a real-time measurement RSS from fingerprint asindicated by the following equation

ManDist (120574(119900)119896119904 120574(119900)

119894119895 ) =

119873

sum

119895=1

10038161003816100381610038161003816120574(119900)

119896119904minus 120574(119900)

119894119895

10038161003816100381610038161003816 (4)

where ManDist(sdotsdot) is the Manhattan distance function 120574(119900)119896119904

is the real-time measurement RSS 120574(119900)119894119895

is one of fingerprintdatabases

612 Algorithms Compared We run the following algo-rithms for comparison

(i) 119896-nearest neighbor (KNN) this is the most popularused algorithm due to its excellent tradeoff betweenaccuracy and computation complexity It obtains the 119896nearest neighbors in the online localization phase insignal space among the known fingerprint maps ldquo119896rdquois a parameter adapted to each localization system toobtain better performance

(ii) Weighted 119896-nearest neighbor (WKNN) the proce-dure is similar to the 119896-nearest neighbor The onlydifference is that the average of the coordinates is aweighted average

(iii) Fuzzy logic it is used to select which points are themost important to calculate the final coordinates ofthe current position and to assess their correspondingweight in the average As for the other algorithmsthe first step after acquiring the current value of thereceived signal strength is to determine the distancein the signal domain between the current position andall the points that make part of the fingerprint mapThe next step is to transform these distance valuesinto grades of membership that is the fuzzificationis made

International Journal of Distributed Sensor Networks 7

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP1 (24G)RSS from AP1 (5G)

(a) RSS from AP1

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50minus40

Reference points

RSS

RSS from AP2 (24G)RSS from AP2 (5G)

(b) RSS from AP2

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP3 (24G)RSS from AP3 (5G)

(c) RSS from AP3

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference pointsRS

S

RSS from AP4 (24G)RSS from AP4 (5G)

(d) RSS from AP4

0 5 10 15 20 25 30 35 40minus100minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP5 (24G)RSS from AP5 (5G)

(e) RSS from AP5

Figure 3 Received signal strength from different APs

(iv) Bayesian histogrammethod the probabilisticmethodis more complex is and based on the Bayes ruleIt is classified as the kernel method and histogrammethod generally In the kernel method a proba-bility mass is assigned to a kernel around the dataobserved the probability is then computed usinga kernel function The histogram method (used inour experiments) uses bins or value categorization tocover all measurement range according to these binswe can then calculate the probability (existence of anAP in a certain position) thus each AP will appearwith different probability and we can estimate thelocation according to the probabilities

62 The Performance of Computation Effort In the coarselocalization phase the approximate localization region isinferred by the detected APs According to the detected APs

remaining fingerprint database cannot include the detectedAPs so we do not need to match the remaining fingerprintdatabase which can reduce the computation effort heavily inthe precise localization phase And the larger the fingerprintdatabase is the more efficient it is From Figures 6 and 7 wecan find that not all the APs can be detected in each point

Supposing that every unknown point can detect 5 APsand there are 40 RPs as the number of RPs increases thealgorithm shows greater efficiency And the computationeffort is reduced dramatically while the fingerprint mapincreases as is shown in Figure 8

63 The Performance of Localization Accuracy FromSection 4 the 5G signal is more stable than 24G if bothsignals can be received by our mobile devices the 5GHzsignal should have priority to be used for localizationestimation To fair evaluate the performance of our proposed

8 International Journal of Distributed Sensor Networks

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40

minus20RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40

minus20RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP5

RP

RSS

Figure 4 Signal fluctuation at all the RPs for 24GWiFi signal

algorithm as is described in detail in Section 3 we comparethe proposed algorithm with KNN WKNN fuzzy logicand histogram algorithm and the localization error of CDF(cumulative distribution function) is showed in Figures 9and 10 It demonstrates that the cluster KNN algorithmobtains the superior localization accuracy in both 24G and5GWiFi signals

From Figure 11 regardless of 24G and 5G signal theaverage error of cluster KNN is the smallest of all algorithmsThe 24G average errors of KNN WKNN fuzzy logichistogram and cluster KNN algorithm are 45700 4384046213 45483 and 14700 respectivelyThe 5G average errorsof the above algorithms are 27475 32122 25450 37895 and11500 respectively (In this study in order to simplify theexpression of error the distance between two neighbor RPsis defined as one unit error and all the error data omit unit)

64 The Performance of Localization Stabilization In addi-tion we also analyse the variance to evaluate the localization

stabilization Besides localization accuracy the localizationstabilization is also important for localization system Inour experiment the 24G variances of KNN WKNN fuzzylogic histogram and cluster KNN algorithm are 116129115713 116833 76819 and 18560 respectively The 5Gvariances of the above algorithms are 93477 94714 9310275370 and 11500 respectively as is showed in Figure 12The smaller the variance is the more stable the localizationpresents It indicates that not only localization accuracy butalso localization stabilization is improved with cluster KNNalgorithm (In this study in order to simplify the expression ofvariance the distance between two neighbor RPs is defined asone unit error and all the variance data omit unit)

7 Conclusion

This paper focuses on improving the localization accuracystabilization and reducing the computation effort by the pro-posed localization system that consists of coarse and precise

International Journal of Distributed Sensor Networks 9

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70RSS from AP5

RP

RSS

Figure 5 Signal fluctuation at all the RPs for 5GWiFi signal

1 5 10 15 20 25 30 35 40Reference points

AP1 (24G)AP2 (24G)AP3 (24G)

AP4 (24G)AP5 (24G)

Figure 6 Detected APs from all RPs (24G)

localization In the coarse localization we use the detectedAPs to infer the coarse localization region It can eliminate the

1 5 10 15 20 25 30 35 40Reference points

AP1 (5G)AP2 (5G)AP3 (5G)

AP4 (5G)AP5 (5G)

Figure 7 Detected APs from all RPs (5G)

impossible fingerprint database to reduce the computationeffort Besides the fingerprint map is built by smartphone

10 International Journal of Distributed Sensor Networks

0 50 100 150 200 250 300 350 400 450 5000

02040608

1

The number of RPs

Effici

ency

Figure 8 Matching efficiency with the increased RPs

0 2 4 6 8 10 12 140010203040506070809

1

Error distance

CDF

KNN (24G)WKNN (24G)Fuzzy logic (24G)

Histogram (24G)Cluster KNN (24G)

Figure 9Distribution of the localization error for 24GWiFi signal

0 2 4 6 8 10 12 14 16 18 200

010203040506070809

1

Error distance

CDF

KNN (5G)WKNN (5G)Fuzzy logic (5G)

Histogram (5G)Cluster KNN (5G)

Figure 10 Distribution of the localization error for 5GWiFi signal

with our developed software application automatically Itimproved efficiency of system operation enormously In theprecise localization we propose two approaches to improvethe localization accuracy and stabilization By analysing thechange rule of WiFi signal about plusmn5 dBm of fluctuation isin the 24G and 5G signal so the real-time measurementRSS is not so accurate as localization estimation The real-time measurement RSS is firstly expanded by plusmn5 dBm andthen used for thematching with the fingerprint map In orderto select the optimal nearest neighbor points we use therelationship of RPs to eliminate the discrete nearest neigh-bor points Experimental results indicate that the proposed

005

115

225

335

445

5

Different algorithms

Aver

age e

rror

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 11 Localization mean error for 24G and 5GWiFi signal

0

2

4

6

8

10

12

Different algorithms

Varia

nce

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 12 Variance of localization error for 24G and 5G WiFisignal

algorithm is the most accurate and stable of all comparativealgorithms In addition our experiment also demonstratesthat the 5GWiFi signal is more stable for indoor localization

In the future we intend to perform this system to applyit in larger indoor construction as well as to investigatingthe performance of the proposed algorithm in more complexenvironment and the multilayered buildings

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This work was supported by the National Natural ScienceFoundation of China under Grant Project nos 61232004 and61103085

International Journal of Distributed Sensor Networks 11

References

[1] B Hofmann-Wellenhof H Lichtenegger and J Collins ldquoGlobalpositioning system theory and practicerdquo in Global PositioningSystem Theory and Practice B Hofmann-Wellenhof H Licht-enegger and J Collins Eds vol 1 p 387 Springer ViennaAustria 1993

[2] N Bulusu J Heidemann and D Estrin ldquoGPS-less low-costoutdoor localization for very small devicesrdquo IEEE PersonalCommunications vol 7 no 5 pp 28ndash34 2000

[3] C Lee Y Chang G Park et al ldquoIndoor positioning systembased on incident angles of infrared emittersrdquo in Proceedings ofthe 30thAnnual Conference of IEEE Industrial Electronics Society(IECON rsquo04) vol 3 pp 2218ndash2222 Busan Republic of KoreaNovember 2004

[4] A Shahi A Aryan J S West C T Haas and R C G HaasldquoDeterioration of UWB positioning during constructionrdquoAutomation in Construction vol 24 pp 72ndash80 2012

[5] V Filonenko C Cullen and J Carswell ldquoInvestigating ultra-sonic positioning on mobile phonesrdquo in Proceedings of theInternational Conference on Indoor Positioning and IndoorNavigation (IPIN rsquo10) pp 1ndash8 IEEE September 2010

[6] S Li Y Lou and B Liu ldquoBluetooth aided mobile phone local-ization a nonlinear neural circuit approachrdquoACMTransactionson Embedded Computing Systems vol 13 no 4 p 78 2014

[7] N Li and B Becerik-Gerber ldquoPerformance-based evaluationof RFID-based indoor location sensing solutions for the builtenvironmentrdquo Advanced Engineering Informatics vol 25 no 3pp 535ndash546 2011

[8] L Luoh ldquoZigBee-based intelligent indoor positioning systemsoft computingrdquo Soft Computing vol 18 no 3 pp 443ndash4562014

[9] Y Chen D Lymberopoulos J Liu and B Priyantha ldquoIndoorlocalization using FM signalsrdquo IEEE Transactions on MobileComputing vol 12 no 8 pp 1502ndash1517 2013

[10] V Moghtadaiee and A G Dempster ldquoIndoor location finger-printing using fm radio signalsrdquo IEEE Transactions on Broad-casting vol 60 no 2 pp 336ndash346 2014

[11] J Chung M Donahoe C Schmandt I-J Kim P Razavai andM Wiseman ldquoIndoor location sensing using geo-magnetismrdquoin Proceedings of the 9th International Conference on MobileSystems Applications and Services (MobiSys rsquo11) pp 141ndash154July 2011

[12] I Bisio M Cerruti F Lavagetto et al ldquoA trainingless WiFifingerprint positioning approach over mobile devicesrdquo IEEEAntennas and Wireless Propagation Letters vol 13 pp 832ndash8352014

[13] N Alsindi Z Chaloupka N AlKhanbashi and J Aweya ldquoAnempirical evaluation of a probabilistic rf signature for wlanlocation fingerprintingrdquo IEEETransactions onWireless Commu-nications vol 13 no 6 pp 3257ndash3268 2014

[14] CWu Z Yang and Y Liu ldquoSmartphones based crowdsourcingfor indoor localizationrdquo 2014

[15] ADanalet B Farooq andM Bierlaire ldquoABayesian approach todetect pedestrian destination-sequences fromWiFi signaturesrdquoTransportation Research Part C Emerging Technologies vol 44pp 146ndash170 2014

[16] R S Campos L Lovisolo and M L R De Campos ldquoWi-Fimulti-floor indoor positioning considering architectural aspectsand controlled computational complexityrdquo Expert Systems withApplications vol 41 no 14 pp 6211ndash6223 2014

[17] M Heidari and K Pahlavan ldquoA new statistical model for thebehavior of ranging errors in TOA-based indoor localiza-tionrdquo in Proceedings of the IEEE Wireless Communications andNetworking Conference (WCNC rsquo07) pp 2564ndash2569 IEEEKowloon Hong Kong March 2007

[18] P-H Tseng K-T Feng Y-C Lin and C-L Chen ldquoWirelesslocation tracking algorithms for environments with insufficientsignal sourcesrdquo IEEE Transactions on Mobile Computing vol 8no 12 pp 1676ndash1689 2009

[19] M Bocquet C Loyez andA Benlarbi-Delaı ldquoUsing enhanced-TDOA measurement for indoor positioningrdquo IEEE Microwaveand Wireless Components Letters vol 15 no 10 pp 612ndash6142005

[20] C-H Lim B P Ng and D Da ldquoRobust methods for AOA geo-location in a real-time indoorWiFi systemrdquo Journal of LocationBased Services vol 2 no 2 pp 112ndash121 2008

[21] K Wu J Xiao Y Yi D Chen X Luo and L M Ni ldquoCSI-basedindoor localizationrdquo IEEE Transactions on Parallel and Dis-tributed Systems vol 24 no 7 pp 1300ndash1309 2013

[22] Z Yang Z Zhou and Y Liu ldquoFrom RSSI to CSI indoorlocalization via channel responserdquo ACM Computing Surveysvol 46 no 2 article 25 2013

[23] A Jaffe and M Wax ldquoSingle-site localization via maximumdiscrimination multipath fingerprintingrdquo IEEE Transactions onSignal Processing vol 62 no 7 pp 1718ndash1728 2014

[24] C Feng W S A Au S Valaee and Z Tan ldquoReceived-signal-strength-based indoor positioning using compressive sensingrdquoIEEETransactions onMobile Computing vol 11 no 12 pp 1983ndash1993 2012

[25] M Ficco C Esposito and A Napolitano ldquoCalibrating indoorpositioning systemswith low effortsrdquo IEEE Transactions onMobile Computing vol 13 no 4 pp 737ndash751 2014

[26] F Yu M H Jiang J Liang et al ldquoAn improved indoor local-ization of wifi based on support vector machinesrdquo InternationalJournal of Future Generation Communication and Networkingvol 7 pp 191ndash206 2014

[27] S Shekhar and H Xiong ldquoIndoor positioning systemrdquo inEncyclopedia of GIS p 566 Springer NewYork NY USA 2008

[28] H Liu H Darabi P Banerjee and J Liu ldquoSurvey of wirelessindoor positioning techniques and systemsrdquo IEEE Transactionson Systems Man and Cybernetics Part C Applications andReviews vol 37 no 6 pp 1067ndash1080 2007

[29] M Molina-Garcıa J Calle-Sanchez J I Alonso A Fernandez-Duran and F B Barba ldquoEnhanced in-building fingerprint posi-tioning using femtocell networksrdquo Bell Labs Technical Journalvol 18 no 2 pp 195ndash211 2013

[30] A Kushki K N Plataniotis and A N VenetsanopoulosldquoKernel-based positioning in wireless local area networksrdquoIEEE Transactions on Mobile Computing vol 6 no 6 pp 689ndash705 2007

[31] T King T Haenselmann andW Effelsberg ldquoDeployment cal-ibration and measurement factors for position errors in 80211-based indoor positioning systemsrdquo in Location-and Context-Awareness pp 17ndash34 Springer 2007

[32] X Chai and Q Yang ldquoReducing the calibration effort forprobabilistic indoor location estimationrdquo IEEE Transactions onMobile Computing vol 6 no 6 pp 649ndash662 2007

[33] C Steiner and A Wittneben ldquoEfficient training phase forultrawideband-based location fingerprinting systemsrdquo IEEETransactions on Signal Processing vol 59 no 12 pp 6021ndash60322011

12 International Journal of Distributed Sensor Networks

[34] Z Yang C Wu and Y Liu ldquoLocating in fingerprint spacewireless indoor localization with little human interventionrdquoin Proceedings of the 18th Annual International Conference onMobile Computing and Networking pp 269ndash280 August 2012

[35] Y Chon N D Lane F Li H Cha and F Zhao ldquoAutomaticallycharacterizing places with opportunistic crowdsensing usingsmartphonesrdquo in Proceedings of the 14th International Confer-ence on Ubiquitous Computing (UbiComp rsquo12) pp 481ndash490Pittsburgh Pa USA September 2012

[36] P Torteeka and X Chundi ldquoIndoor positioning based on Wi-Fi fingerprint technique using fuzzy K-nearest neighborrdquo inProceedings of the 11th International Bhurban Conference onApplied Sciences and Technology (IBCAST rsquo14) pp 461ndash465Islamabad Pakistan January 2014

[37] B Shin J H Lee T Lee and H S Kim ldquoEnhanced weightedK-nearest neighbor algorithm for indoor Wi-Fi positioningsystemsrdquo in Proceedings of the 8th International Conference onComputing Technology and Information Management (ICCMrsquo12) pp 574ndash577 April 2012

[38] D Madigan E Einahrawy R P Martin W-H Ju P Krishnanand A Krishnakumar ldquoBayesian indoor positioning systemsrdquoin Proceedings of the 24th Annual Joint Conference of the IEEEComputer and Communications Societies (INFOCOM rsquo05) vol2 pp 1217ndash1227 IEEE 2005

[39] M Zhou Y Xu and L Tang ldquoMultilayer ANN indoor locationsystem with area division in WLAN environmentrdquo Journal ofSystems Engineering and Electronics vol 21 no 5 pp 914ndash9262010

[40] Z-L Wu C-H Li J K-Y Ng and K R P H Leung ldquoLocationestimation via support vector regressionrdquo IEEE Transactions onMobile Computing vol 6 no 3 pp 311ndash321 2007

[41] F Yu M H Jiang J Liang et al ldquoAn indoor localization ofwifi based on support vector machinesrdquo in Advanced MaterialsResearch vol 926 pp 2438ndash2441 Trans Tech Publications 2014

[42] O Baala Y Zheng and A Caminada ldquoThe impact of APplacement in WLAN-based indoor positioning systemrdquo inProceedings of the 8th International Conference on Networks(ICN rsquo09) pp 12ndash17 Gosier France March 2009

[43] A Farshad J Li M K Marina and F J Garcia ldquoA microscopiclook at wifi fingerprinting for indoor mobile phone localizationin diverse environmentsrdquo in Proceedings of the InternationalConference on Indoor Positioning and Indoor Navigation (IPINrsquo13) pp 1ndash10 IEEE 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

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Active and Passive Electronic Components

Control Scienceand Engineering

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Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

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Electrical and Computer Engineering

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SensorsJournal of

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Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

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Chemical EngineeringInternational Journal of Antennas and

Propagation

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DistributedSensor Networks

International Journal of

2 International Journal of Distributed Sensor Networks

additional hardware the WiFi indoor localization is becom-ing increasingly prevalent and ubiquitous

There are different choices for the localization measure-ment methods such as time of arrival (TOA) [17 18] timedifference of arrival (TDOA) [19] angle of arrival (AOA) [20]channel state information (CSI) [21 22] and received signalstrength (RSS) [23ndash26] RSS is generally the feature of choicefor indoor WiFi localization due to its low cost without theneed for additional hardware at our mobile devices

Most RSS-based indoor localization approaches adoptfingerprint algorithm as the basic scheme of indoor localiza-tion Even though fingerprint algorithm has been successfulinmany localization systems [14 24 27 28] it exhibits severalchallenges when considering real indoor environment

(i) Environmental variations and interferences ideallysignal variations should only be affected by distanceThe longer the distance to an access point (AP) thesmaller the RSS However interferences and someundesired effects such as reflection can make thesystem detect a signal at a smaller RSS being closer tothe AP These may be caused by different set of con-ditions The fingerprint is sensitive to environmentalchanges such as an object moving into the buildingdiffraction and reflection which result in changes insignal propagation

(ii) 24G as an unlicensed spectrum this means thatdifferent hardware and applications apart from WiFican freely use this spectrum to bring interference andnoise The most common electrical equipment whichuses this frequency and can interfere with WiFi ismicrowaves that are the household electronic devicesto emit interference in the 24G band Bluetoothdevices telecommunications devices wireless secu-rity cameras wireless speakers and so on

(iii) The time-consuming offline training phase to buildthe fingerprint map during the fingerprint collectionphase there are enough empirical manual measure-ments to build fingerprint map

In this work in order to reduce the manual effort duringthe calibration phase we have exploited a computer-aided software which allows us to automatically build the fingerprintmap This software consists of leading in the indoor layoutand then exploiting fingerprint automatically at each refer-ence point based on the application software For solvingthe problem of the signal is unsteadiness we propose anindoor localization system using the cluster KNN algorithmit aims at both reducing the interference to improve local-ization accuracy based on 5G signal and using cluster KNNalgorithm to reduce the computation effort but improve thelocalization accuracy and stabilization

The remainder of this paper is organized as followsSection 2 discusses how related work available in the liter-ature approaches such issues A brief description of indoorlocalization system is presented in Section 3 Section 4describes the coarse and precise localization algorithm indetail The characteristics of 24G and 5G band signal areanalysed in Section 5The experiment results and evaluations

through implementation are listed in Section 6 FinallySection 7 concludes the paper

2 Related Work

21 Fingerprint Collection Themajor problem of fingerprintalgorithm is the exhaustive survey needed to build thefingerprintmap a task that requires substantial cost and timeAnother important issue of this fingerprint map is that arecalibration is needed every time the environment changes

There are mainly two methods to build fingerprintmap empirical manual measurement [14 15] and computedanalytically based on the signal propagation model [12 2529] Because signal propagation model is easily affectedby environmental changes normally the fingerprint mapis built with manual effort In this phase the fingerprintmap is surveyed for all the reference points (RPs) Basicallyfingerprint map is a database of reference points at prede-fined points (coordinates) coupled with various radio signalstrength characteristics for example RSS signal angles orpropagation time called signal fingerprint Step by step forevery fingerprint there must be a measurement that includesthe information about all positions and their received signalstrength

The popular researches highlight the strong needs ofapproaches aiming at reducing the time associated with theoffline training phase of fingerprint algorithm In [30] theneed of an approach capable of reducing the heavy effort ofthe training phase is indicated as one of the key challenges infingerprint In [31] it is proposed that a valid training phaseis hardly bearable since it requires collecting a large numberof fingerprints To reduce such fingerprint it is presented totrade localization error against time thus reducing the timeneeded to train the fingerprint map [31] Homoplastically ithas been proposed in [32] that a huge amount of receivedsignal strength is usually required for training and typicallymuch time is necessary to collect such amount of trainingfingerprint For this reason it has been stated in [32] that areduction of themanual effort can be achieved byminimizingthe sampling time at each reference point (RP) and bylimiting the number of positions to sample Nevertheless thissimple approach makes inaccurate fingerprint map whichdecreases the accuracy of the location estimation [32]

Attempting to develop training methods that try toreduce the training phase of fingerprint map have beenproposed in [33 34] Some works also propose training thefingerprint by using a mobile device such as a smartphoneemployingWiFi scans transparently to the user [35] In orderto reduce the manual effort during the calibration phase wehave defined a computer-aided approach which allows usto automatically build the fingerprint map In this systema software application is developed to build the fingerprintmap It is developed based on Android Java development kit170 and Android software development kit 44 The generalsteps of the software application are as follows

(i) Open the software application and import indoorworkspace plan

(ii) Select points as reference points in the indoor mapcollect real-time RSS and store it in fingerprint map

International Journal of Distributed Sensor Networks 3

(iii) After collecting fingerprint information of all refer-ence points if the indoor environment changes weshould reconstruct fingerprint map only selectingbrush-fire reference points and renovating the finger-print map

(iv) Click the ldquolocalizationrdquo button and we should obtainour immediate position

22MatchingAlgorithm During the last years several finger-print localization algorithms have been proposed The keyidea of fingerprint algorithm is to find the optimal nearestneighbor points In an attempt to find the best matchingalgorithm and try to improve the localization accuracy manyresearchers propose the nearest neighbor (NN) 119896-nearestneighbor (KNN) [36] weighted 119896-nearest neighbor (WKNN)[37] Bayesian probabilistic model (BPM) [38] artificialneural network (ANN) [39] and support vector machine(SVM) [40 41] to obtain the optimal nearest neighbor points

Although the above algorithms can achieve adequatelocalization performance the computing and memoryrequirements have to be taken into consideration While itis true that smart mobile devices are high capable machinesthe users themselves do not want an application that takesgigabytes of data just to improve accuracy in localizationSo a tradeoff between accuracy and complexity of algorithmis needed In order to satisfy the acceptable localizationaccuracy but with low computation effort we propose anefficient indoor localization system

In our proposed system the localization includes coarseand precise localization phases The coarse phase is toreduce the computation and then further localization usescluster KNN algorithm With the society developing andwireless network popularizing the constructions are largerand more complex it indicates that the fingerprint map islarger and larger During the online localization phase if wematch the real-time RSS with the whole fingerprint mapthe computation is too large for efficient localization Forreducing the computation effort and localization time thecoarse localization algorithm is proposed to reduce matchingfingerprint data by the detected APs Besides by realisticexperiment we find that the same value RSS points arescattered sometimes This phenomenon should cause largelocalization error By comparison with current localizationalgorithms the cluster KNN algorithm can remove thediscrete nearest neighbor points and obtain optimal nearestneighbor points

3 System Architecture

As in most fingerprint approaches the proposed system con-sists of an offline training and an online localization phaseThe offline training phase is responsible for collecting signalstrength from each reference point (RP) and then recordingit in the fingerprint database At the online localization phasethe mobile devices collect the real-time measurement RSSand compare it with the available fingerprint in the database

31 Offline Training Phase During the offline training phasea designer of a fingerprint localization system has to deal withtwo distinct problems as follows

(i) Fingerprint collection this includes how to efficientlyobtain fingerprint map in the large and complexindoor construction

(ii) Search of the optimal APs placement this includeshow many APs are there and where they have to beplaced to obtain an optimal localization accuracy

There are three distinct problems that we should considerin the fingerprint collection first how to reduce the time ofcollection second how to reconstruct the fingerprint mapefficiently when the indoor environment changes third howto extract the fingerprint characteristic of reference pointsIn our system the fingerprint map is built by the developedsoftware application and the detailed steps are showed inSection 21 The software application not only builds finger-print map conveniently but also renews partial fingerprintinformation of reference points without reconstructing thewhole fingerprint map During the fingerprint collection weselect RSS as fingerprint characteristic and choose mediaaccess control (MAC) as identity of different APs Becausethe service set identifier (SSID) of AP may change artificiallyin realistic life the MAC is sole for every AP The raw setof RSS time samples are collected from AP119894 at RP119895 withorientation 119900 by software automatically Since RSS variesnoticeably due to interferences and environment conditionsseveral consecutive RSSmeasurements need to be collected ateach reference point with a period of time Then the averageof the RSS time samples is computed and stored in fingerprintdatabase Such fingerprint map can be represented by Ω(119900)

Ω(119900)=(((

(

120574(119900)11 120574(119900)12 sdot sdot sdot 120574

(119900)

1119873

120574(119900)21 120574(119900)22 sdot sdot sdot 120574

(119900)

2119873

d

120574(119900)

1198711120574(119900)

1198712sdot sdot sdot 120574(119900)

119871119873

)))

)

(1)

where 120574(119900)119894119895

is a fingerprint from AP119894 at RP119895 with orientation119900 for 119894 = 1 2 119871 119895 = 1 2 119873 and 119900 isin 120590 =

119891119900119903119908119886119903119889 119887119886119888119896 119871 is the total number of APs and 119873 is thenumber of RPs

The placement of the APs consists of identifying theoptimal APs placement which achieves a reasonable radiosignal coverage of the workspace In general increasing thenumber of APs can improve the localization accuracy but interms of economic investment it increases deployment costsand the amount of time for the RSS collection from all APswhile establishing localization infrastructure and fingerprintmap However the goodness of a placement pattern highlydepends on the specific workspace conditions such as wallpositions andmaterials space topography noise sources andthe stream of people in the workspace

In [42] the experimental performance shows that ifwe place the APs in symmetric positions distributed over

4 International Journal of Distributed Sensor Networks

Require fingerprint mapΩ(119900)Require a real time measurement RSS 120574(119900)

119896119904 120574(119900)

119896119904isin 120574(119900)

119894119895 119894 = 1 119871 119895 = 1 119873Ensure coarse localization regionΩ(119900)

1015840

(1) the undetected AP 120574(119900)119894119895 larr 100(2) while 120574(119900)

119896119904= 119899119906119897119897 do

(3) if 120574(119900)119896119904= 100 then

(4) the unknowed point is not adjacent to 119896-st AP(5) delete 119896(6) else(7) the unknowed point is adjacent to 119896-st AP(8) store 119896(9) end if(10) end while(11) count all 119896(12) return coarse localization region Ω(119900)

1015840

Algorithm 1 Coarse localization algorithm

the experimental workspace in such a way that the averagesignal power is high it is likely to be one of the best choicesto reduce localization error In order to reduce the noise as faras possible if the APs can be deployed by ourselves it is betterto be distributed evenly around the workspace as frontal way

32 Online Localization Phase The online localization phaseconsists of coarse localization and precise localization Thegoal of the coarse localization phase is to reduce the impossi-ble regions from all the fingerprintmapThus it removes par-tial impossible fingerprint and reduces the computation effortof the precise localization phase The greater the fingerprintmap is the bigger the reduction impossible regions are as isshown in Section 62 In the precise localization phase thelocalization is computed by using the cluster KNN algorithmA detailed introduction is given in the following

4 The Efficiency of Cluster KNN

41 Coarse Localization Due to the wide deployment of APsthe total number of detectable APs is generally much greaterthan that required for localization which leads to redundantcomputations Furthermore unreliable APs with large RSSvariances may also lead to biased estimation and reduce thestability of the localization accuracyThismotivates the use ofAPs selection techniques to select a subset of available APs forcoarse localization Since the mobile devices detect differentnumber of APs in different region the detected APs can beused to coarse localization

The coarse localization is processed by comparing thedetected APs to infer the rough localization region The real-time measurement RSS can be represented as the columns of120574(119900)

119896119904= [120574(119900)1119904 120574(119900)2119904 120574

(119900)

119871119904]119879 119904 isin 119873 where the superscript 119879

denotes transposition Search for the 120574(119900)119896119904

from the fingerprintmapΩ(119900) based on the detected APs If a fingerprint includesall the detected APs we can infer that the mobile devices arein this regionThe algorithm is represented as in Alogrithm 1

5 10 15 20 25 30 35minus10

minus505

1015

Reference points

Fluc

tuat

ion

24G WiFi signal5G WiFi signal

Figure 1 Signal fluctuation

42 Precise Localization The major challenge for accurateRSS-based localization comes from the variations of RSS dueto the dynamic and unpredictable nature of radio channeland the RSS is easily affected by environmental changessuch as shadowing and multipath It is variable that the RSSis received from the same point at different time so thereal-time measurement RSS is not too credible for accuratelocalization To decrease the effect with variational signal andanalysis upon 24G and 5GWiFi signal we get that absolutevalue of signal fluctuation is under 5 dBm as is shown inFigure 1 and about 93 fluctuation is within 5 dBm in all thefluctuations of measurement (24G and 5G band signal) Inour experiment the 5G signal fluctuation is all within 5 dBmand about 86 fluctuation is within 5 dBm in the 24G signalTo avoid the inaccurate nearest neighbor points match withunauthentic real-time RSS prior to conducting localizationalgorithm the RSS is expanded by plusmn5 dBm and then it usedfor localization

In addition the ecumenic signal propagation model is

119871119901 (dBm) = 119871119901 (119889119900) + 10119899log10 (119889

1198890

) + 119883120590 (2)

where119871119901(119889119900)parameter represents the path loss at a referencedistance 1198890 typically onemeter 119899 is the constant propagation

International Journal of Distributed Sensor Networks 5

Figure 2 The distribution of qualified nearest neighbor points

Require coarse localization region Ω(119900)1015840

Require a real time measurement RSS 120574(119900)

119896119904 120574(119900)

119896119904isin 120574(119900)

119894119895 119894 = 1 119871 119895 = 1 119873Ensure precise localization coordinate (119909 119910)(1) for all 120574(119900)1015840

119896119904= 120574(119900)

119896119904plusmn 5 dBm do

(2) end for(3) for all ManDist (120574(119900)

1015840

119896119904 Ω(119900)1015840

) =sum119899

119894=1

100381610038161003816100381610038161003816120574(119900)1015840

119896119904minus Ω(119900)1015840 100381610038161003816100381610038161003816do

(4) end for(5) obtain nearest neighbor points set 120574(119900)

119896119899

(6) while Θ = (120574(119900)

119896119899119886 120574(119900)

119896119899119887

120574(119900)

119896119899119888) = 119899119906119897119897 do

(7) if min (119899120572 119899120573 119899120579) = (1003816100381610038161003816119899119886 minus 119899119887

1003816100381610038161003816 +1003816100381610038161003816119899119887 minus 119899119888

1003816100381610038161003816) le (10038161003816100381610038161003816119899120572 minus 119899120573

10038161003816100381610038161003816+10038161003816100381610038161003816119899120573 minus 119899120579

10038161003816100381610038161003816) (120572 120573 120579) isin 119899 then

(8) min (119904 (119899119886 119899119887 119899119888)) = (1003816100381610038161003816119904 minus 119899119886

1003816100381610038161003816 +1003816100381610038161003816119904 minus 119899119887

1003816100381610038161003816 +1003816100381610038161003816119904 minus 119899119888

1003816100381610038161003816) le (1003816100381610038161003816119904 minus 119899120572

1003816100381610038161003816 +10038161003816100381610038161003816119904 minus 119899120573

10038161003816100381610038161003816+1003816100381610038161003816119904 minus 119899120579

1003816100381610038161003816) (120572 120573 120579) isin 119899(9) obtain the optimal nearest neighbor points set Θ = (120574

(119900)

119896119899119886 120574(119900)

119896119899119887

120574(119900)

119896119899119888)

(10) else(11) continue(12) end if(13) end while(14) return precise localization coordinate (119909 119910) =((119909119899119886 + 119909119899119887 + 119909119899119888) 3 (119910119899119886 + 119910119899119887 + 119910119899119888) 3)

Algorithm 2 Precise localization algorithm

value 119889 is the distance between the transmitter and thereceiver devices and 119883120590 is a Gaussian random variablewith mean 0 and standard deviation 120590 From the signalpropagation model when the mobile devices are equidistantfrom the AP in line of sight the mobile devices receivethe same signal strength This phenomenon should causelarge error for localization In Figure 2 the same colorpoints represent the qualified nearest neighbor points if thelocalization algorithm is processed based on all the qualifiedpoints the localization error will be very large

In our experiment the range of signal strength is betweenminus30 dBm and minus99 dBm and all the RSS of undetected APsrepresentsminus100 dBmMany different RPs have the same value

of RSS in the fingerprint map if we use traditional nearestneighbor algorithms to find the optimal matching RPs itwill produce large error for localization For the sake ofsearching the optimalmatchingRPs not only the value of RSSbut also the relationship of RPs is considered In the offlinetraining phase the fingerprint map is collected in sequentialorder and not only the subscript of RPs does represent theserial number but also the 119895 implies the relationship of RPsGenerally the coterminous RPs have analogical value of RSSand in the process of searching for the nearest neighbor pointsthe real-time RSS should be matched with the coterminousRPs that have the analogical value of RSS The detailedalgorithm is represented as in Algorithm 2

6 International Journal of Distributed Sensor Networks

According to precise localization algorithm we selectthree optimal nearest neighbor points 119899119886 119899119887 and 119899119888 as local-ization the ultimate localization estimation is represented as(119909 119910)

119909 =1

3(119909119899119886 + 119909119899119887 + 119909119899119888)

119910 =1

3(119910119899119886 + 119910119899119887 + 119910119899119888)

(3)

5 Accurate Localization Based on 5 GWiFi Signal

The indoor radio environment is quite complex Becausethe 24G is no permit limitation band a wide variety ofequipment use the band at the same time It is unavoidableto be interfered by the same frequency equipment that hasbeen introduced in Section 1 The interference signal willgenerate signal fluctuation when we detect the WiFi signalfrom surrounding access points

The entry level speed of 5GWiFi is 433Mbps which is atleast three times compared to that of the 24G WiFi and thehigh performance of 5G WiFi can reach more than 1GbpsThe high transmission rate can satisfy usersrsquo daily surfingneeds and provide stable and high quality signal as well

In this section we explore the impact of frequencyband (24G and 5G) to evaluate the localization accuracyWhile 24G signal is the only band originally used for WiFiincreasingly 5G signal is also used despite of its poorerpropagation characteristics resulting from higher frequencyoperation As the 5G frequency band is less crowded thereis far more spectrum available in 5G band From a WiFifingerprint localization system perspective in a typical envi-ronment today with APs using both 24G and 5G bands ameasurement RSS collected during either the offline trainingphase or the online localization phase will likely include amixof 24G and 5G APs

Figure 3 shows that the signal strength of 5G is strongerthan that of 24G Generally stronger signal is more stableand stable signal can guarantee the high localization accuracy

Besides this from Figures 4 and 5 we can get that the5G signal is more stable than that of 24G in the sameenvironment From 30-st RP to 35-st RP the locus of 30-st RPsim35-st RP in Figures 4 and 5 indicates the 5G signalhas poorer propagation characteristics resulting from higherfrequency operation

Today more and more APs have double frequency bandand the 5G signal seems to be muchmore suitable for indoorlocalization than 24G signal Due to a lack of cochannelinterference it can be feasible to use more stable RSS for thepurpose of accurate localization So the 5G signal is selectedas localization estimation in our localization system

6 Experiments and Evaluations

61 Experiments Setup In this section we present the imple-mentation and experimental evaluations of the proposed sys-temThefingerprint database is collected by the smartphones

its network card can detect both 24G and 5G signals Theworkspace is equipped with WiFi environment by five TP-LINK WDR6300 routers that can emit 24G and 5G WiFisignal The total area is 36 times 258 meters consisting ofhallways and some classroomsWe collect 40 reference pointsevenly from the hallways region and obtain WiFi fingerprintdatabase using smartphone with the application software thatis our developed mobile software application to collect RSSand build the fingerprint map automaticallyWhen collectingthe fingerprint map the smartphones are kept at the sameheight of approximately an adultrsquos breast and sometimesrotated horizontally at the same position to face differentdirections In particular for the RPs data 20 samples at eachRP with a rate of 1 samplesecond are collected by a userwalking through the hallway area

611 Performance Metric Besides that according to [9 43]Manhattan distance performs slightly better than Euclideandistance and our workspace is a regular rectangle we usethe Manhattan distance as the standard of error analysisTheManhattan distance is an expression of geometric metricspace It is defined as the sum of the absolute differences ofvalues in a real-time measurement RSS from fingerprint asindicated by the following equation

ManDist (120574(119900)119896119904 120574(119900)

119894119895 ) =

119873

sum

119895=1

10038161003816100381610038161003816120574(119900)

119896119904minus 120574(119900)

119894119895

10038161003816100381610038161003816 (4)

where ManDist(sdotsdot) is the Manhattan distance function 120574(119900)119896119904

is the real-time measurement RSS 120574(119900)119894119895

is one of fingerprintdatabases

612 Algorithms Compared We run the following algo-rithms for comparison

(i) 119896-nearest neighbor (KNN) this is the most popularused algorithm due to its excellent tradeoff betweenaccuracy and computation complexity It obtains the 119896nearest neighbors in the online localization phase insignal space among the known fingerprint maps ldquo119896rdquois a parameter adapted to each localization system toobtain better performance

(ii) Weighted 119896-nearest neighbor (WKNN) the proce-dure is similar to the 119896-nearest neighbor The onlydifference is that the average of the coordinates is aweighted average

(iii) Fuzzy logic it is used to select which points are themost important to calculate the final coordinates ofthe current position and to assess their correspondingweight in the average As for the other algorithmsthe first step after acquiring the current value of thereceived signal strength is to determine the distancein the signal domain between the current position andall the points that make part of the fingerprint mapThe next step is to transform these distance valuesinto grades of membership that is the fuzzificationis made

International Journal of Distributed Sensor Networks 7

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP1 (24G)RSS from AP1 (5G)

(a) RSS from AP1

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50minus40

Reference points

RSS

RSS from AP2 (24G)RSS from AP2 (5G)

(b) RSS from AP2

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP3 (24G)RSS from AP3 (5G)

(c) RSS from AP3

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference pointsRS

S

RSS from AP4 (24G)RSS from AP4 (5G)

(d) RSS from AP4

0 5 10 15 20 25 30 35 40minus100minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP5 (24G)RSS from AP5 (5G)

(e) RSS from AP5

Figure 3 Received signal strength from different APs

(iv) Bayesian histogrammethod the probabilisticmethodis more complex is and based on the Bayes ruleIt is classified as the kernel method and histogrammethod generally In the kernel method a proba-bility mass is assigned to a kernel around the dataobserved the probability is then computed usinga kernel function The histogram method (used inour experiments) uses bins or value categorization tocover all measurement range according to these binswe can then calculate the probability (existence of anAP in a certain position) thus each AP will appearwith different probability and we can estimate thelocation according to the probabilities

62 The Performance of Computation Effort In the coarselocalization phase the approximate localization region isinferred by the detected APs According to the detected APs

remaining fingerprint database cannot include the detectedAPs so we do not need to match the remaining fingerprintdatabase which can reduce the computation effort heavily inthe precise localization phase And the larger the fingerprintdatabase is the more efficient it is From Figures 6 and 7 wecan find that not all the APs can be detected in each point

Supposing that every unknown point can detect 5 APsand there are 40 RPs as the number of RPs increases thealgorithm shows greater efficiency And the computationeffort is reduced dramatically while the fingerprint mapincreases as is shown in Figure 8

63 The Performance of Localization Accuracy FromSection 4 the 5G signal is more stable than 24G if bothsignals can be received by our mobile devices the 5GHzsignal should have priority to be used for localizationestimation To fair evaluate the performance of our proposed

8 International Journal of Distributed Sensor Networks

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40

minus20RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40

minus20RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP5

RP

RSS

Figure 4 Signal fluctuation at all the RPs for 24GWiFi signal

algorithm as is described in detail in Section 3 we comparethe proposed algorithm with KNN WKNN fuzzy logicand histogram algorithm and the localization error of CDF(cumulative distribution function) is showed in Figures 9and 10 It demonstrates that the cluster KNN algorithmobtains the superior localization accuracy in both 24G and5GWiFi signals

From Figure 11 regardless of 24G and 5G signal theaverage error of cluster KNN is the smallest of all algorithmsThe 24G average errors of KNN WKNN fuzzy logichistogram and cluster KNN algorithm are 45700 4384046213 45483 and 14700 respectivelyThe 5G average errorsof the above algorithms are 27475 32122 25450 37895 and11500 respectively (In this study in order to simplify theexpression of error the distance between two neighbor RPsis defined as one unit error and all the error data omit unit)

64 The Performance of Localization Stabilization In addi-tion we also analyse the variance to evaluate the localization

stabilization Besides localization accuracy the localizationstabilization is also important for localization system Inour experiment the 24G variances of KNN WKNN fuzzylogic histogram and cluster KNN algorithm are 116129115713 116833 76819 and 18560 respectively The 5Gvariances of the above algorithms are 93477 94714 9310275370 and 11500 respectively as is showed in Figure 12The smaller the variance is the more stable the localizationpresents It indicates that not only localization accuracy butalso localization stabilization is improved with cluster KNNalgorithm (In this study in order to simplify the expression ofvariance the distance between two neighbor RPs is defined asone unit error and all the variance data omit unit)

7 Conclusion

This paper focuses on improving the localization accuracystabilization and reducing the computation effort by the pro-posed localization system that consists of coarse and precise

International Journal of Distributed Sensor Networks 9

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70RSS from AP5

RP

RSS

Figure 5 Signal fluctuation at all the RPs for 5GWiFi signal

1 5 10 15 20 25 30 35 40Reference points

AP1 (24G)AP2 (24G)AP3 (24G)

AP4 (24G)AP5 (24G)

Figure 6 Detected APs from all RPs (24G)

localization In the coarse localization we use the detectedAPs to infer the coarse localization region It can eliminate the

1 5 10 15 20 25 30 35 40Reference points

AP1 (5G)AP2 (5G)AP3 (5G)

AP4 (5G)AP5 (5G)

Figure 7 Detected APs from all RPs (5G)

impossible fingerprint database to reduce the computationeffort Besides the fingerprint map is built by smartphone

10 International Journal of Distributed Sensor Networks

0 50 100 150 200 250 300 350 400 450 5000

02040608

1

The number of RPs

Effici

ency

Figure 8 Matching efficiency with the increased RPs

0 2 4 6 8 10 12 140010203040506070809

1

Error distance

CDF

KNN (24G)WKNN (24G)Fuzzy logic (24G)

Histogram (24G)Cluster KNN (24G)

Figure 9Distribution of the localization error for 24GWiFi signal

0 2 4 6 8 10 12 14 16 18 200

010203040506070809

1

Error distance

CDF

KNN (5G)WKNN (5G)Fuzzy logic (5G)

Histogram (5G)Cluster KNN (5G)

Figure 10 Distribution of the localization error for 5GWiFi signal

with our developed software application automatically Itimproved efficiency of system operation enormously In theprecise localization we propose two approaches to improvethe localization accuracy and stabilization By analysing thechange rule of WiFi signal about plusmn5 dBm of fluctuation isin the 24G and 5G signal so the real-time measurementRSS is not so accurate as localization estimation The real-time measurement RSS is firstly expanded by plusmn5 dBm andthen used for thematching with the fingerprint map In orderto select the optimal nearest neighbor points we use therelationship of RPs to eliminate the discrete nearest neigh-bor points Experimental results indicate that the proposed

005

115

225

335

445

5

Different algorithms

Aver

age e

rror

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 11 Localization mean error for 24G and 5GWiFi signal

0

2

4

6

8

10

12

Different algorithms

Varia

nce

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 12 Variance of localization error for 24G and 5G WiFisignal

algorithm is the most accurate and stable of all comparativealgorithms In addition our experiment also demonstratesthat the 5GWiFi signal is more stable for indoor localization

In the future we intend to perform this system to applyit in larger indoor construction as well as to investigatingthe performance of the proposed algorithm in more complexenvironment and the multilayered buildings

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This work was supported by the National Natural ScienceFoundation of China under Grant Project nos 61232004 and61103085

International Journal of Distributed Sensor Networks 11

References

[1] B Hofmann-Wellenhof H Lichtenegger and J Collins ldquoGlobalpositioning system theory and practicerdquo in Global PositioningSystem Theory and Practice B Hofmann-Wellenhof H Licht-enegger and J Collins Eds vol 1 p 387 Springer ViennaAustria 1993

[2] N Bulusu J Heidemann and D Estrin ldquoGPS-less low-costoutdoor localization for very small devicesrdquo IEEE PersonalCommunications vol 7 no 5 pp 28ndash34 2000

[3] C Lee Y Chang G Park et al ldquoIndoor positioning systembased on incident angles of infrared emittersrdquo in Proceedings ofthe 30thAnnual Conference of IEEE Industrial Electronics Society(IECON rsquo04) vol 3 pp 2218ndash2222 Busan Republic of KoreaNovember 2004

[4] A Shahi A Aryan J S West C T Haas and R C G HaasldquoDeterioration of UWB positioning during constructionrdquoAutomation in Construction vol 24 pp 72ndash80 2012

[5] V Filonenko C Cullen and J Carswell ldquoInvestigating ultra-sonic positioning on mobile phonesrdquo in Proceedings of theInternational Conference on Indoor Positioning and IndoorNavigation (IPIN rsquo10) pp 1ndash8 IEEE September 2010

[6] S Li Y Lou and B Liu ldquoBluetooth aided mobile phone local-ization a nonlinear neural circuit approachrdquoACMTransactionson Embedded Computing Systems vol 13 no 4 p 78 2014

[7] N Li and B Becerik-Gerber ldquoPerformance-based evaluationof RFID-based indoor location sensing solutions for the builtenvironmentrdquo Advanced Engineering Informatics vol 25 no 3pp 535ndash546 2011

[8] L Luoh ldquoZigBee-based intelligent indoor positioning systemsoft computingrdquo Soft Computing vol 18 no 3 pp 443ndash4562014

[9] Y Chen D Lymberopoulos J Liu and B Priyantha ldquoIndoorlocalization using FM signalsrdquo IEEE Transactions on MobileComputing vol 12 no 8 pp 1502ndash1517 2013

[10] V Moghtadaiee and A G Dempster ldquoIndoor location finger-printing using fm radio signalsrdquo IEEE Transactions on Broad-casting vol 60 no 2 pp 336ndash346 2014

[11] J Chung M Donahoe C Schmandt I-J Kim P Razavai andM Wiseman ldquoIndoor location sensing using geo-magnetismrdquoin Proceedings of the 9th International Conference on MobileSystems Applications and Services (MobiSys rsquo11) pp 141ndash154July 2011

[12] I Bisio M Cerruti F Lavagetto et al ldquoA trainingless WiFifingerprint positioning approach over mobile devicesrdquo IEEEAntennas and Wireless Propagation Letters vol 13 pp 832ndash8352014

[13] N Alsindi Z Chaloupka N AlKhanbashi and J Aweya ldquoAnempirical evaluation of a probabilistic rf signature for wlanlocation fingerprintingrdquo IEEETransactions onWireless Commu-nications vol 13 no 6 pp 3257ndash3268 2014

[14] CWu Z Yang and Y Liu ldquoSmartphones based crowdsourcingfor indoor localizationrdquo 2014

[15] ADanalet B Farooq andM Bierlaire ldquoABayesian approach todetect pedestrian destination-sequences fromWiFi signaturesrdquoTransportation Research Part C Emerging Technologies vol 44pp 146ndash170 2014

[16] R S Campos L Lovisolo and M L R De Campos ldquoWi-Fimulti-floor indoor positioning considering architectural aspectsand controlled computational complexityrdquo Expert Systems withApplications vol 41 no 14 pp 6211ndash6223 2014

[17] M Heidari and K Pahlavan ldquoA new statistical model for thebehavior of ranging errors in TOA-based indoor localiza-tionrdquo in Proceedings of the IEEE Wireless Communications andNetworking Conference (WCNC rsquo07) pp 2564ndash2569 IEEEKowloon Hong Kong March 2007

[18] P-H Tseng K-T Feng Y-C Lin and C-L Chen ldquoWirelesslocation tracking algorithms for environments with insufficientsignal sourcesrdquo IEEE Transactions on Mobile Computing vol 8no 12 pp 1676ndash1689 2009

[19] M Bocquet C Loyez andA Benlarbi-Delaı ldquoUsing enhanced-TDOA measurement for indoor positioningrdquo IEEE Microwaveand Wireless Components Letters vol 15 no 10 pp 612ndash6142005

[20] C-H Lim B P Ng and D Da ldquoRobust methods for AOA geo-location in a real-time indoorWiFi systemrdquo Journal of LocationBased Services vol 2 no 2 pp 112ndash121 2008

[21] K Wu J Xiao Y Yi D Chen X Luo and L M Ni ldquoCSI-basedindoor localizationrdquo IEEE Transactions on Parallel and Dis-tributed Systems vol 24 no 7 pp 1300ndash1309 2013

[22] Z Yang Z Zhou and Y Liu ldquoFrom RSSI to CSI indoorlocalization via channel responserdquo ACM Computing Surveysvol 46 no 2 article 25 2013

[23] A Jaffe and M Wax ldquoSingle-site localization via maximumdiscrimination multipath fingerprintingrdquo IEEE Transactions onSignal Processing vol 62 no 7 pp 1718ndash1728 2014

[24] C Feng W S A Au S Valaee and Z Tan ldquoReceived-signal-strength-based indoor positioning using compressive sensingrdquoIEEETransactions onMobile Computing vol 11 no 12 pp 1983ndash1993 2012

[25] M Ficco C Esposito and A Napolitano ldquoCalibrating indoorpositioning systemswith low effortsrdquo IEEE Transactions onMobile Computing vol 13 no 4 pp 737ndash751 2014

[26] F Yu M H Jiang J Liang et al ldquoAn improved indoor local-ization of wifi based on support vector machinesrdquo InternationalJournal of Future Generation Communication and Networkingvol 7 pp 191ndash206 2014

[27] S Shekhar and H Xiong ldquoIndoor positioning systemrdquo inEncyclopedia of GIS p 566 Springer NewYork NY USA 2008

[28] H Liu H Darabi P Banerjee and J Liu ldquoSurvey of wirelessindoor positioning techniques and systemsrdquo IEEE Transactionson Systems Man and Cybernetics Part C Applications andReviews vol 37 no 6 pp 1067ndash1080 2007

[29] M Molina-Garcıa J Calle-Sanchez J I Alonso A Fernandez-Duran and F B Barba ldquoEnhanced in-building fingerprint posi-tioning using femtocell networksrdquo Bell Labs Technical Journalvol 18 no 2 pp 195ndash211 2013

[30] A Kushki K N Plataniotis and A N VenetsanopoulosldquoKernel-based positioning in wireless local area networksrdquoIEEE Transactions on Mobile Computing vol 6 no 6 pp 689ndash705 2007

[31] T King T Haenselmann andW Effelsberg ldquoDeployment cal-ibration and measurement factors for position errors in 80211-based indoor positioning systemsrdquo in Location-and Context-Awareness pp 17ndash34 Springer 2007

[32] X Chai and Q Yang ldquoReducing the calibration effort forprobabilistic indoor location estimationrdquo IEEE Transactions onMobile Computing vol 6 no 6 pp 649ndash662 2007

[33] C Steiner and A Wittneben ldquoEfficient training phase forultrawideband-based location fingerprinting systemsrdquo IEEETransactions on Signal Processing vol 59 no 12 pp 6021ndash60322011

12 International Journal of Distributed Sensor Networks

[34] Z Yang C Wu and Y Liu ldquoLocating in fingerprint spacewireless indoor localization with little human interventionrdquoin Proceedings of the 18th Annual International Conference onMobile Computing and Networking pp 269ndash280 August 2012

[35] Y Chon N D Lane F Li H Cha and F Zhao ldquoAutomaticallycharacterizing places with opportunistic crowdsensing usingsmartphonesrdquo in Proceedings of the 14th International Confer-ence on Ubiquitous Computing (UbiComp rsquo12) pp 481ndash490Pittsburgh Pa USA September 2012

[36] P Torteeka and X Chundi ldquoIndoor positioning based on Wi-Fi fingerprint technique using fuzzy K-nearest neighborrdquo inProceedings of the 11th International Bhurban Conference onApplied Sciences and Technology (IBCAST rsquo14) pp 461ndash465Islamabad Pakistan January 2014

[37] B Shin J H Lee T Lee and H S Kim ldquoEnhanced weightedK-nearest neighbor algorithm for indoor Wi-Fi positioningsystemsrdquo in Proceedings of the 8th International Conference onComputing Technology and Information Management (ICCMrsquo12) pp 574ndash577 April 2012

[38] D Madigan E Einahrawy R P Martin W-H Ju P Krishnanand A Krishnakumar ldquoBayesian indoor positioning systemsrdquoin Proceedings of the 24th Annual Joint Conference of the IEEEComputer and Communications Societies (INFOCOM rsquo05) vol2 pp 1217ndash1227 IEEE 2005

[39] M Zhou Y Xu and L Tang ldquoMultilayer ANN indoor locationsystem with area division in WLAN environmentrdquo Journal ofSystems Engineering and Electronics vol 21 no 5 pp 914ndash9262010

[40] Z-L Wu C-H Li J K-Y Ng and K R P H Leung ldquoLocationestimation via support vector regressionrdquo IEEE Transactions onMobile Computing vol 6 no 3 pp 311ndash321 2007

[41] F Yu M H Jiang J Liang et al ldquoAn indoor localization ofwifi based on support vector machinesrdquo in Advanced MaterialsResearch vol 926 pp 2438ndash2441 Trans Tech Publications 2014

[42] O Baala Y Zheng and A Caminada ldquoThe impact of APplacement in WLAN-based indoor positioning systemrdquo inProceedings of the 8th International Conference on Networks(ICN rsquo09) pp 12ndash17 Gosier France March 2009

[43] A Farshad J Li M K Marina and F J Garcia ldquoA microscopiclook at wifi fingerprinting for indoor mobile phone localizationin diverse environmentsrdquo in Proceedings of the InternationalConference on Indoor Positioning and Indoor Navigation (IPINrsquo13) pp 1ndash10 IEEE 2013

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DistributedSensor Networks

International Journal of

International Journal of Distributed Sensor Networks 3

(iii) After collecting fingerprint information of all refer-ence points if the indoor environment changes weshould reconstruct fingerprint map only selectingbrush-fire reference points and renovating the finger-print map

(iv) Click the ldquolocalizationrdquo button and we should obtainour immediate position

22MatchingAlgorithm During the last years several finger-print localization algorithms have been proposed The keyidea of fingerprint algorithm is to find the optimal nearestneighbor points In an attempt to find the best matchingalgorithm and try to improve the localization accuracy manyresearchers propose the nearest neighbor (NN) 119896-nearestneighbor (KNN) [36] weighted 119896-nearest neighbor (WKNN)[37] Bayesian probabilistic model (BPM) [38] artificialneural network (ANN) [39] and support vector machine(SVM) [40 41] to obtain the optimal nearest neighbor points

Although the above algorithms can achieve adequatelocalization performance the computing and memoryrequirements have to be taken into consideration While itis true that smart mobile devices are high capable machinesthe users themselves do not want an application that takesgigabytes of data just to improve accuracy in localizationSo a tradeoff between accuracy and complexity of algorithmis needed In order to satisfy the acceptable localizationaccuracy but with low computation effort we propose anefficient indoor localization system

In our proposed system the localization includes coarseand precise localization phases The coarse phase is toreduce the computation and then further localization usescluster KNN algorithm With the society developing andwireless network popularizing the constructions are largerand more complex it indicates that the fingerprint map islarger and larger During the online localization phase if wematch the real-time RSS with the whole fingerprint mapthe computation is too large for efficient localization Forreducing the computation effort and localization time thecoarse localization algorithm is proposed to reduce matchingfingerprint data by the detected APs Besides by realisticexperiment we find that the same value RSS points arescattered sometimes This phenomenon should cause largelocalization error By comparison with current localizationalgorithms the cluster KNN algorithm can remove thediscrete nearest neighbor points and obtain optimal nearestneighbor points

3 System Architecture

As in most fingerprint approaches the proposed system con-sists of an offline training and an online localization phaseThe offline training phase is responsible for collecting signalstrength from each reference point (RP) and then recordingit in the fingerprint database At the online localization phasethe mobile devices collect the real-time measurement RSSand compare it with the available fingerprint in the database

31 Offline Training Phase During the offline training phasea designer of a fingerprint localization system has to deal withtwo distinct problems as follows

(i) Fingerprint collection this includes how to efficientlyobtain fingerprint map in the large and complexindoor construction

(ii) Search of the optimal APs placement this includeshow many APs are there and where they have to beplaced to obtain an optimal localization accuracy

There are three distinct problems that we should considerin the fingerprint collection first how to reduce the time ofcollection second how to reconstruct the fingerprint mapefficiently when the indoor environment changes third howto extract the fingerprint characteristic of reference pointsIn our system the fingerprint map is built by the developedsoftware application and the detailed steps are showed inSection 21 The software application not only builds finger-print map conveniently but also renews partial fingerprintinformation of reference points without reconstructing thewhole fingerprint map During the fingerprint collection weselect RSS as fingerprint characteristic and choose mediaaccess control (MAC) as identity of different APs Becausethe service set identifier (SSID) of AP may change artificiallyin realistic life the MAC is sole for every AP The raw setof RSS time samples are collected from AP119894 at RP119895 withorientation 119900 by software automatically Since RSS variesnoticeably due to interferences and environment conditionsseveral consecutive RSSmeasurements need to be collected ateach reference point with a period of time Then the averageof the RSS time samples is computed and stored in fingerprintdatabase Such fingerprint map can be represented by Ω(119900)

Ω(119900)=(((

(

120574(119900)11 120574(119900)12 sdot sdot sdot 120574

(119900)

1119873

120574(119900)21 120574(119900)22 sdot sdot sdot 120574

(119900)

2119873

d

120574(119900)

1198711120574(119900)

1198712sdot sdot sdot 120574(119900)

119871119873

)))

)

(1)

where 120574(119900)119894119895

is a fingerprint from AP119894 at RP119895 with orientation119900 for 119894 = 1 2 119871 119895 = 1 2 119873 and 119900 isin 120590 =

119891119900119903119908119886119903119889 119887119886119888119896 119871 is the total number of APs and 119873 is thenumber of RPs

The placement of the APs consists of identifying theoptimal APs placement which achieves a reasonable radiosignal coverage of the workspace In general increasing thenumber of APs can improve the localization accuracy but interms of economic investment it increases deployment costsand the amount of time for the RSS collection from all APswhile establishing localization infrastructure and fingerprintmap However the goodness of a placement pattern highlydepends on the specific workspace conditions such as wallpositions andmaterials space topography noise sources andthe stream of people in the workspace

In [42] the experimental performance shows that ifwe place the APs in symmetric positions distributed over

4 International Journal of Distributed Sensor Networks

Require fingerprint mapΩ(119900)Require a real time measurement RSS 120574(119900)

119896119904 120574(119900)

119896119904isin 120574(119900)

119894119895 119894 = 1 119871 119895 = 1 119873Ensure coarse localization regionΩ(119900)

1015840

(1) the undetected AP 120574(119900)119894119895 larr 100(2) while 120574(119900)

119896119904= 119899119906119897119897 do

(3) if 120574(119900)119896119904= 100 then

(4) the unknowed point is not adjacent to 119896-st AP(5) delete 119896(6) else(7) the unknowed point is adjacent to 119896-st AP(8) store 119896(9) end if(10) end while(11) count all 119896(12) return coarse localization region Ω(119900)

1015840

Algorithm 1 Coarse localization algorithm

the experimental workspace in such a way that the averagesignal power is high it is likely to be one of the best choicesto reduce localization error In order to reduce the noise as faras possible if the APs can be deployed by ourselves it is betterto be distributed evenly around the workspace as frontal way

32 Online Localization Phase The online localization phaseconsists of coarse localization and precise localization Thegoal of the coarse localization phase is to reduce the impossi-ble regions from all the fingerprintmapThus it removes par-tial impossible fingerprint and reduces the computation effortof the precise localization phase The greater the fingerprintmap is the bigger the reduction impossible regions are as isshown in Section 62 In the precise localization phase thelocalization is computed by using the cluster KNN algorithmA detailed introduction is given in the following

4 The Efficiency of Cluster KNN

41 Coarse Localization Due to the wide deployment of APsthe total number of detectable APs is generally much greaterthan that required for localization which leads to redundantcomputations Furthermore unreliable APs with large RSSvariances may also lead to biased estimation and reduce thestability of the localization accuracyThismotivates the use ofAPs selection techniques to select a subset of available APs forcoarse localization Since the mobile devices detect differentnumber of APs in different region the detected APs can beused to coarse localization

The coarse localization is processed by comparing thedetected APs to infer the rough localization region The real-time measurement RSS can be represented as the columns of120574(119900)

119896119904= [120574(119900)1119904 120574(119900)2119904 120574

(119900)

119871119904]119879 119904 isin 119873 where the superscript 119879

denotes transposition Search for the 120574(119900)119896119904

from the fingerprintmapΩ(119900) based on the detected APs If a fingerprint includesall the detected APs we can infer that the mobile devices arein this regionThe algorithm is represented as in Alogrithm 1

5 10 15 20 25 30 35minus10

minus505

1015

Reference points

Fluc

tuat

ion

24G WiFi signal5G WiFi signal

Figure 1 Signal fluctuation

42 Precise Localization The major challenge for accurateRSS-based localization comes from the variations of RSS dueto the dynamic and unpredictable nature of radio channeland the RSS is easily affected by environmental changessuch as shadowing and multipath It is variable that the RSSis received from the same point at different time so thereal-time measurement RSS is not too credible for accuratelocalization To decrease the effect with variational signal andanalysis upon 24G and 5GWiFi signal we get that absolutevalue of signal fluctuation is under 5 dBm as is shown inFigure 1 and about 93 fluctuation is within 5 dBm in all thefluctuations of measurement (24G and 5G band signal) Inour experiment the 5G signal fluctuation is all within 5 dBmand about 86 fluctuation is within 5 dBm in the 24G signalTo avoid the inaccurate nearest neighbor points match withunauthentic real-time RSS prior to conducting localizationalgorithm the RSS is expanded by plusmn5 dBm and then it usedfor localization

In addition the ecumenic signal propagation model is

119871119901 (dBm) = 119871119901 (119889119900) + 10119899log10 (119889

1198890

) + 119883120590 (2)

where119871119901(119889119900)parameter represents the path loss at a referencedistance 1198890 typically onemeter 119899 is the constant propagation

International Journal of Distributed Sensor Networks 5

Figure 2 The distribution of qualified nearest neighbor points

Require coarse localization region Ω(119900)1015840

Require a real time measurement RSS 120574(119900)

119896119904 120574(119900)

119896119904isin 120574(119900)

119894119895 119894 = 1 119871 119895 = 1 119873Ensure precise localization coordinate (119909 119910)(1) for all 120574(119900)1015840

119896119904= 120574(119900)

119896119904plusmn 5 dBm do

(2) end for(3) for all ManDist (120574(119900)

1015840

119896119904 Ω(119900)1015840

) =sum119899

119894=1

100381610038161003816100381610038161003816120574(119900)1015840

119896119904minus Ω(119900)1015840 100381610038161003816100381610038161003816do

(4) end for(5) obtain nearest neighbor points set 120574(119900)

119896119899

(6) while Θ = (120574(119900)

119896119899119886 120574(119900)

119896119899119887

120574(119900)

119896119899119888) = 119899119906119897119897 do

(7) if min (119899120572 119899120573 119899120579) = (1003816100381610038161003816119899119886 minus 119899119887

1003816100381610038161003816 +1003816100381610038161003816119899119887 minus 119899119888

1003816100381610038161003816) le (10038161003816100381610038161003816119899120572 minus 119899120573

10038161003816100381610038161003816+10038161003816100381610038161003816119899120573 minus 119899120579

10038161003816100381610038161003816) (120572 120573 120579) isin 119899 then

(8) min (119904 (119899119886 119899119887 119899119888)) = (1003816100381610038161003816119904 minus 119899119886

1003816100381610038161003816 +1003816100381610038161003816119904 minus 119899119887

1003816100381610038161003816 +1003816100381610038161003816119904 minus 119899119888

1003816100381610038161003816) le (1003816100381610038161003816119904 minus 119899120572

1003816100381610038161003816 +10038161003816100381610038161003816119904 minus 119899120573

10038161003816100381610038161003816+1003816100381610038161003816119904 minus 119899120579

1003816100381610038161003816) (120572 120573 120579) isin 119899(9) obtain the optimal nearest neighbor points set Θ = (120574

(119900)

119896119899119886 120574(119900)

119896119899119887

120574(119900)

119896119899119888)

(10) else(11) continue(12) end if(13) end while(14) return precise localization coordinate (119909 119910) =((119909119899119886 + 119909119899119887 + 119909119899119888) 3 (119910119899119886 + 119910119899119887 + 119910119899119888) 3)

Algorithm 2 Precise localization algorithm

value 119889 is the distance between the transmitter and thereceiver devices and 119883120590 is a Gaussian random variablewith mean 0 and standard deviation 120590 From the signalpropagation model when the mobile devices are equidistantfrom the AP in line of sight the mobile devices receivethe same signal strength This phenomenon should causelarge error for localization In Figure 2 the same colorpoints represent the qualified nearest neighbor points if thelocalization algorithm is processed based on all the qualifiedpoints the localization error will be very large

In our experiment the range of signal strength is betweenminus30 dBm and minus99 dBm and all the RSS of undetected APsrepresentsminus100 dBmMany different RPs have the same value

of RSS in the fingerprint map if we use traditional nearestneighbor algorithms to find the optimal matching RPs itwill produce large error for localization For the sake ofsearching the optimalmatchingRPs not only the value of RSSbut also the relationship of RPs is considered In the offlinetraining phase the fingerprint map is collected in sequentialorder and not only the subscript of RPs does represent theserial number but also the 119895 implies the relationship of RPsGenerally the coterminous RPs have analogical value of RSSand in the process of searching for the nearest neighbor pointsthe real-time RSS should be matched with the coterminousRPs that have the analogical value of RSS The detailedalgorithm is represented as in Algorithm 2

6 International Journal of Distributed Sensor Networks

According to precise localization algorithm we selectthree optimal nearest neighbor points 119899119886 119899119887 and 119899119888 as local-ization the ultimate localization estimation is represented as(119909 119910)

119909 =1

3(119909119899119886 + 119909119899119887 + 119909119899119888)

119910 =1

3(119910119899119886 + 119910119899119887 + 119910119899119888)

(3)

5 Accurate Localization Based on 5 GWiFi Signal

The indoor radio environment is quite complex Becausethe 24G is no permit limitation band a wide variety ofequipment use the band at the same time It is unavoidableto be interfered by the same frequency equipment that hasbeen introduced in Section 1 The interference signal willgenerate signal fluctuation when we detect the WiFi signalfrom surrounding access points

The entry level speed of 5GWiFi is 433Mbps which is atleast three times compared to that of the 24G WiFi and thehigh performance of 5G WiFi can reach more than 1GbpsThe high transmission rate can satisfy usersrsquo daily surfingneeds and provide stable and high quality signal as well

In this section we explore the impact of frequencyband (24G and 5G) to evaluate the localization accuracyWhile 24G signal is the only band originally used for WiFiincreasingly 5G signal is also used despite of its poorerpropagation characteristics resulting from higher frequencyoperation As the 5G frequency band is less crowded thereis far more spectrum available in 5G band From a WiFifingerprint localization system perspective in a typical envi-ronment today with APs using both 24G and 5G bands ameasurement RSS collected during either the offline trainingphase or the online localization phase will likely include amixof 24G and 5G APs

Figure 3 shows that the signal strength of 5G is strongerthan that of 24G Generally stronger signal is more stableand stable signal can guarantee the high localization accuracy

Besides this from Figures 4 and 5 we can get that the5G signal is more stable than that of 24G in the sameenvironment From 30-st RP to 35-st RP the locus of 30-st RPsim35-st RP in Figures 4 and 5 indicates the 5G signalhas poorer propagation characteristics resulting from higherfrequency operation

Today more and more APs have double frequency bandand the 5G signal seems to be muchmore suitable for indoorlocalization than 24G signal Due to a lack of cochannelinterference it can be feasible to use more stable RSS for thepurpose of accurate localization So the 5G signal is selectedas localization estimation in our localization system

6 Experiments and Evaluations

61 Experiments Setup In this section we present the imple-mentation and experimental evaluations of the proposed sys-temThefingerprint database is collected by the smartphones

its network card can detect both 24G and 5G signals Theworkspace is equipped with WiFi environment by five TP-LINK WDR6300 routers that can emit 24G and 5G WiFisignal The total area is 36 times 258 meters consisting ofhallways and some classroomsWe collect 40 reference pointsevenly from the hallways region and obtain WiFi fingerprintdatabase using smartphone with the application software thatis our developed mobile software application to collect RSSand build the fingerprint map automaticallyWhen collectingthe fingerprint map the smartphones are kept at the sameheight of approximately an adultrsquos breast and sometimesrotated horizontally at the same position to face differentdirections In particular for the RPs data 20 samples at eachRP with a rate of 1 samplesecond are collected by a userwalking through the hallway area

611 Performance Metric Besides that according to [9 43]Manhattan distance performs slightly better than Euclideandistance and our workspace is a regular rectangle we usethe Manhattan distance as the standard of error analysisTheManhattan distance is an expression of geometric metricspace It is defined as the sum of the absolute differences ofvalues in a real-time measurement RSS from fingerprint asindicated by the following equation

ManDist (120574(119900)119896119904 120574(119900)

119894119895 ) =

119873

sum

119895=1

10038161003816100381610038161003816120574(119900)

119896119904minus 120574(119900)

119894119895

10038161003816100381610038161003816 (4)

where ManDist(sdotsdot) is the Manhattan distance function 120574(119900)119896119904

is the real-time measurement RSS 120574(119900)119894119895

is one of fingerprintdatabases

612 Algorithms Compared We run the following algo-rithms for comparison

(i) 119896-nearest neighbor (KNN) this is the most popularused algorithm due to its excellent tradeoff betweenaccuracy and computation complexity It obtains the 119896nearest neighbors in the online localization phase insignal space among the known fingerprint maps ldquo119896rdquois a parameter adapted to each localization system toobtain better performance

(ii) Weighted 119896-nearest neighbor (WKNN) the proce-dure is similar to the 119896-nearest neighbor The onlydifference is that the average of the coordinates is aweighted average

(iii) Fuzzy logic it is used to select which points are themost important to calculate the final coordinates ofthe current position and to assess their correspondingweight in the average As for the other algorithmsthe first step after acquiring the current value of thereceived signal strength is to determine the distancein the signal domain between the current position andall the points that make part of the fingerprint mapThe next step is to transform these distance valuesinto grades of membership that is the fuzzificationis made

International Journal of Distributed Sensor Networks 7

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP1 (24G)RSS from AP1 (5G)

(a) RSS from AP1

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50minus40

Reference points

RSS

RSS from AP2 (24G)RSS from AP2 (5G)

(b) RSS from AP2

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP3 (24G)RSS from AP3 (5G)

(c) RSS from AP3

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference pointsRS

S

RSS from AP4 (24G)RSS from AP4 (5G)

(d) RSS from AP4

0 5 10 15 20 25 30 35 40minus100minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP5 (24G)RSS from AP5 (5G)

(e) RSS from AP5

Figure 3 Received signal strength from different APs

(iv) Bayesian histogrammethod the probabilisticmethodis more complex is and based on the Bayes ruleIt is classified as the kernel method and histogrammethod generally In the kernel method a proba-bility mass is assigned to a kernel around the dataobserved the probability is then computed usinga kernel function The histogram method (used inour experiments) uses bins or value categorization tocover all measurement range according to these binswe can then calculate the probability (existence of anAP in a certain position) thus each AP will appearwith different probability and we can estimate thelocation according to the probabilities

62 The Performance of Computation Effort In the coarselocalization phase the approximate localization region isinferred by the detected APs According to the detected APs

remaining fingerprint database cannot include the detectedAPs so we do not need to match the remaining fingerprintdatabase which can reduce the computation effort heavily inthe precise localization phase And the larger the fingerprintdatabase is the more efficient it is From Figures 6 and 7 wecan find that not all the APs can be detected in each point

Supposing that every unknown point can detect 5 APsand there are 40 RPs as the number of RPs increases thealgorithm shows greater efficiency And the computationeffort is reduced dramatically while the fingerprint mapincreases as is shown in Figure 8

63 The Performance of Localization Accuracy FromSection 4 the 5G signal is more stable than 24G if bothsignals can be received by our mobile devices the 5GHzsignal should have priority to be used for localizationestimation To fair evaluate the performance of our proposed

8 International Journal of Distributed Sensor Networks

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40

minus20RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40

minus20RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP5

RP

RSS

Figure 4 Signal fluctuation at all the RPs for 24GWiFi signal

algorithm as is described in detail in Section 3 we comparethe proposed algorithm with KNN WKNN fuzzy logicand histogram algorithm and the localization error of CDF(cumulative distribution function) is showed in Figures 9and 10 It demonstrates that the cluster KNN algorithmobtains the superior localization accuracy in both 24G and5GWiFi signals

From Figure 11 regardless of 24G and 5G signal theaverage error of cluster KNN is the smallest of all algorithmsThe 24G average errors of KNN WKNN fuzzy logichistogram and cluster KNN algorithm are 45700 4384046213 45483 and 14700 respectivelyThe 5G average errorsof the above algorithms are 27475 32122 25450 37895 and11500 respectively (In this study in order to simplify theexpression of error the distance between two neighbor RPsis defined as one unit error and all the error data omit unit)

64 The Performance of Localization Stabilization In addi-tion we also analyse the variance to evaluate the localization

stabilization Besides localization accuracy the localizationstabilization is also important for localization system Inour experiment the 24G variances of KNN WKNN fuzzylogic histogram and cluster KNN algorithm are 116129115713 116833 76819 and 18560 respectively The 5Gvariances of the above algorithms are 93477 94714 9310275370 and 11500 respectively as is showed in Figure 12The smaller the variance is the more stable the localizationpresents It indicates that not only localization accuracy butalso localization stabilization is improved with cluster KNNalgorithm (In this study in order to simplify the expression ofvariance the distance between two neighbor RPs is defined asone unit error and all the variance data omit unit)

7 Conclusion

This paper focuses on improving the localization accuracystabilization and reducing the computation effort by the pro-posed localization system that consists of coarse and precise

International Journal of Distributed Sensor Networks 9

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70RSS from AP5

RP

RSS

Figure 5 Signal fluctuation at all the RPs for 5GWiFi signal

1 5 10 15 20 25 30 35 40Reference points

AP1 (24G)AP2 (24G)AP3 (24G)

AP4 (24G)AP5 (24G)

Figure 6 Detected APs from all RPs (24G)

localization In the coarse localization we use the detectedAPs to infer the coarse localization region It can eliminate the

1 5 10 15 20 25 30 35 40Reference points

AP1 (5G)AP2 (5G)AP3 (5G)

AP4 (5G)AP5 (5G)

Figure 7 Detected APs from all RPs (5G)

impossible fingerprint database to reduce the computationeffort Besides the fingerprint map is built by smartphone

10 International Journal of Distributed Sensor Networks

0 50 100 150 200 250 300 350 400 450 5000

02040608

1

The number of RPs

Effici

ency

Figure 8 Matching efficiency with the increased RPs

0 2 4 6 8 10 12 140010203040506070809

1

Error distance

CDF

KNN (24G)WKNN (24G)Fuzzy logic (24G)

Histogram (24G)Cluster KNN (24G)

Figure 9Distribution of the localization error for 24GWiFi signal

0 2 4 6 8 10 12 14 16 18 200

010203040506070809

1

Error distance

CDF

KNN (5G)WKNN (5G)Fuzzy logic (5G)

Histogram (5G)Cluster KNN (5G)

Figure 10 Distribution of the localization error for 5GWiFi signal

with our developed software application automatically Itimproved efficiency of system operation enormously In theprecise localization we propose two approaches to improvethe localization accuracy and stabilization By analysing thechange rule of WiFi signal about plusmn5 dBm of fluctuation isin the 24G and 5G signal so the real-time measurementRSS is not so accurate as localization estimation The real-time measurement RSS is firstly expanded by plusmn5 dBm andthen used for thematching with the fingerprint map In orderto select the optimal nearest neighbor points we use therelationship of RPs to eliminate the discrete nearest neigh-bor points Experimental results indicate that the proposed

005

115

225

335

445

5

Different algorithms

Aver

age e

rror

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 11 Localization mean error for 24G and 5GWiFi signal

0

2

4

6

8

10

12

Different algorithms

Varia

nce

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 12 Variance of localization error for 24G and 5G WiFisignal

algorithm is the most accurate and stable of all comparativealgorithms In addition our experiment also demonstratesthat the 5GWiFi signal is more stable for indoor localization

In the future we intend to perform this system to applyit in larger indoor construction as well as to investigatingthe performance of the proposed algorithm in more complexenvironment and the multilayered buildings

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This work was supported by the National Natural ScienceFoundation of China under Grant Project nos 61232004 and61103085

International Journal of Distributed Sensor Networks 11

References

[1] B Hofmann-Wellenhof H Lichtenegger and J Collins ldquoGlobalpositioning system theory and practicerdquo in Global PositioningSystem Theory and Practice B Hofmann-Wellenhof H Licht-enegger and J Collins Eds vol 1 p 387 Springer ViennaAustria 1993

[2] N Bulusu J Heidemann and D Estrin ldquoGPS-less low-costoutdoor localization for very small devicesrdquo IEEE PersonalCommunications vol 7 no 5 pp 28ndash34 2000

[3] C Lee Y Chang G Park et al ldquoIndoor positioning systembased on incident angles of infrared emittersrdquo in Proceedings ofthe 30thAnnual Conference of IEEE Industrial Electronics Society(IECON rsquo04) vol 3 pp 2218ndash2222 Busan Republic of KoreaNovember 2004

[4] A Shahi A Aryan J S West C T Haas and R C G HaasldquoDeterioration of UWB positioning during constructionrdquoAutomation in Construction vol 24 pp 72ndash80 2012

[5] V Filonenko C Cullen and J Carswell ldquoInvestigating ultra-sonic positioning on mobile phonesrdquo in Proceedings of theInternational Conference on Indoor Positioning and IndoorNavigation (IPIN rsquo10) pp 1ndash8 IEEE September 2010

[6] S Li Y Lou and B Liu ldquoBluetooth aided mobile phone local-ization a nonlinear neural circuit approachrdquoACMTransactionson Embedded Computing Systems vol 13 no 4 p 78 2014

[7] N Li and B Becerik-Gerber ldquoPerformance-based evaluationof RFID-based indoor location sensing solutions for the builtenvironmentrdquo Advanced Engineering Informatics vol 25 no 3pp 535ndash546 2011

[8] L Luoh ldquoZigBee-based intelligent indoor positioning systemsoft computingrdquo Soft Computing vol 18 no 3 pp 443ndash4562014

[9] Y Chen D Lymberopoulos J Liu and B Priyantha ldquoIndoorlocalization using FM signalsrdquo IEEE Transactions on MobileComputing vol 12 no 8 pp 1502ndash1517 2013

[10] V Moghtadaiee and A G Dempster ldquoIndoor location finger-printing using fm radio signalsrdquo IEEE Transactions on Broad-casting vol 60 no 2 pp 336ndash346 2014

[11] J Chung M Donahoe C Schmandt I-J Kim P Razavai andM Wiseman ldquoIndoor location sensing using geo-magnetismrdquoin Proceedings of the 9th International Conference on MobileSystems Applications and Services (MobiSys rsquo11) pp 141ndash154July 2011

[12] I Bisio M Cerruti F Lavagetto et al ldquoA trainingless WiFifingerprint positioning approach over mobile devicesrdquo IEEEAntennas and Wireless Propagation Letters vol 13 pp 832ndash8352014

[13] N Alsindi Z Chaloupka N AlKhanbashi and J Aweya ldquoAnempirical evaluation of a probabilistic rf signature for wlanlocation fingerprintingrdquo IEEETransactions onWireless Commu-nications vol 13 no 6 pp 3257ndash3268 2014

[14] CWu Z Yang and Y Liu ldquoSmartphones based crowdsourcingfor indoor localizationrdquo 2014

[15] ADanalet B Farooq andM Bierlaire ldquoABayesian approach todetect pedestrian destination-sequences fromWiFi signaturesrdquoTransportation Research Part C Emerging Technologies vol 44pp 146ndash170 2014

[16] R S Campos L Lovisolo and M L R De Campos ldquoWi-Fimulti-floor indoor positioning considering architectural aspectsand controlled computational complexityrdquo Expert Systems withApplications vol 41 no 14 pp 6211ndash6223 2014

[17] M Heidari and K Pahlavan ldquoA new statistical model for thebehavior of ranging errors in TOA-based indoor localiza-tionrdquo in Proceedings of the IEEE Wireless Communications andNetworking Conference (WCNC rsquo07) pp 2564ndash2569 IEEEKowloon Hong Kong March 2007

[18] P-H Tseng K-T Feng Y-C Lin and C-L Chen ldquoWirelesslocation tracking algorithms for environments with insufficientsignal sourcesrdquo IEEE Transactions on Mobile Computing vol 8no 12 pp 1676ndash1689 2009

[19] M Bocquet C Loyez andA Benlarbi-Delaı ldquoUsing enhanced-TDOA measurement for indoor positioningrdquo IEEE Microwaveand Wireless Components Letters vol 15 no 10 pp 612ndash6142005

[20] C-H Lim B P Ng and D Da ldquoRobust methods for AOA geo-location in a real-time indoorWiFi systemrdquo Journal of LocationBased Services vol 2 no 2 pp 112ndash121 2008

[21] K Wu J Xiao Y Yi D Chen X Luo and L M Ni ldquoCSI-basedindoor localizationrdquo IEEE Transactions on Parallel and Dis-tributed Systems vol 24 no 7 pp 1300ndash1309 2013

[22] Z Yang Z Zhou and Y Liu ldquoFrom RSSI to CSI indoorlocalization via channel responserdquo ACM Computing Surveysvol 46 no 2 article 25 2013

[23] A Jaffe and M Wax ldquoSingle-site localization via maximumdiscrimination multipath fingerprintingrdquo IEEE Transactions onSignal Processing vol 62 no 7 pp 1718ndash1728 2014

[24] C Feng W S A Au S Valaee and Z Tan ldquoReceived-signal-strength-based indoor positioning using compressive sensingrdquoIEEETransactions onMobile Computing vol 11 no 12 pp 1983ndash1993 2012

[25] M Ficco C Esposito and A Napolitano ldquoCalibrating indoorpositioning systemswith low effortsrdquo IEEE Transactions onMobile Computing vol 13 no 4 pp 737ndash751 2014

[26] F Yu M H Jiang J Liang et al ldquoAn improved indoor local-ization of wifi based on support vector machinesrdquo InternationalJournal of Future Generation Communication and Networkingvol 7 pp 191ndash206 2014

[27] S Shekhar and H Xiong ldquoIndoor positioning systemrdquo inEncyclopedia of GIS p 566 Springer NewYork NY USA 2008

[28] H Liu H Darabi P Banerjee and J Liu ldquoSurvey of wirelessindoor positioning techniques and systemsrdquo IEEE Transactionson Systems Man and Cybernetics Part C Applications andReviews vol 37 no 6 pp 1067ndash1080 2007

[29] M Molina-Garcıa J Calle-Sanchez J I Alonso A Fernandez-Duran and F B Barba ldquoEnhanced in-building fingerprint posi-tioning using femtocell networksrdquo Bell Labs Technical Journalvol 18 no 2 pp 195ndash211 2013

[30] A Kushki K N Plataniotis and A N VenetsanopoulosldquoKernel-based positioning in wireless local area networksrdquoIEEE Transactions on Mobile Computing vol 6 no 6 pp 689ndash705 2007

[31] T King T Haenselmann andW Effelsberg ldquoDeployment cal-ibration and measurement factors for position errors in 80211-based indoor positioning systemsrdquo in Location-and Context-Awareness pp 17ndash34 Springer 2007

[32] X Chai and Q Yang ldquoReducing the calibration effort forprobabilistic indoor location estimationrdquo IEEE Transactions onMobile Computing vol 6 no 6 pp 649ndash662 2007

[33] C Steiner and A Wittneben ldquoEfficient training phase forultrawideband-based location fingerprinting systemsrdquo IEEETransactions on Signal Processing vol 59 no 12 pp 6021ndash60322011

12 International Journal of Distributed Sensor Networks

[34] Z Yang C Wu and Y Liu ldquoLocating in fingerprint spacewireless indoor localization with little human interventionrdquoin Proceedings of the 18th Annual International Conference onMobile Computing and Networking pp 269ndash280 August 2012

[35] Y Chon N D Lane F Li H Cha and F Zhao ldquoAutomaticallycharacterizing places with opportunistic crowdsensing usingsmartphonesrdquo in Proceedings of the 14th International Confer-ence on Ubiquitous Computing (UbiComp rsquo12) pp 481ndash490Pittsburgh Pa USA September 2012

[36] P Torteeka and X Chundi ldquoIndoor positioning based on Wi-Fi fingerprint technique using fuzzy K-nearest neighborrdquo inProceedings of the 11th International Bhurban Conference onApplied Sciences and Technology (IBCAST rsquo14) pp 461ndash465Islamabad Pakistan January 2014

[37] B Shin J H Lee T Lee and H S Kim ldquoEnhanced weightedK-nearest neighbor algorithm for indoor Wi-Fi positioningsystemsrdquo in Proceedings of the 8th International Conference onComputing Technology and Information Management (ICCMrsquo12) pp 574ndash577 April 2012

[38] D Madigan E Einahrawy R P Martin W-H Ju P Krishnanand A Krishnakumar ldquoBayesian indoor positioning systemsrdquoin Proceedings of the 24th Annual Joint Conference of the IEEEComputer and Communications Societies (INFOCOM rsquo05) vol2 pp 1217ndash1227 IEEE 2005

[39] M Zhou Y Xu and L Tang ldquoMultilayer ANN indoor locationsystem with area division in WLAN environmentrdquo Journal ofSystems Engineering and Electronics vol 21 no 5 pp 914ndash9262010

[40] Z-L Wu C-H Li J K-Y Ng and K R P H Leung ldquoLocationestimation via support vector regressionrdquo IEEE Transactions onMobile Computing vol 6 no 3 pp 311ndash321 2007

[41] F Yu M H Jiang J Liang et al ldquoAn indoor localization ofwifi based on support vector machinesrdquo in Advanced MaterialsResearch vol 926 pp 2438ndash2441 Trans Tech Publications 2014

[42] O Baala Y Zheng and A Caminada ldquoThe impact of APplacement in WLAN-based indoor positioning systemrdquo inProceedings of the 8th International Conference on Networks(ICN rsquo09) pp 12ndash17 Gosier France March 2009

[43] A Farshad J Li M K Marina and F J Garcia ldquoA microscopiclook at wifi fingerprinting for indoor mobile phone localizationin diverse environmentsrdquo in Proceedings of the InternationalConference on Indoor Positioning and Indoor Navigation (IPINrsquo13) pp 1ndash10 IEEE 2013

International Journal of

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Active and Passive Electronic Components

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RotatingMachinery

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Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Shock and Vibration

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Civil EngineeringAdvances in

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Electrical and Computer Engineering

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Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

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Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

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Navigation and Observation

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DistributedSensor Networks

International Journal of

4 International Journal of Distributed Sensor Networks

Require fingerprint mapΩ(119900)Require a real time measurement RSS 120574(119900)

119896119904 120574(119900)

119896119904isin 120574(119900)

119894119895 119894 = 1 119871 119895 = 1 119873Ensure coarse localization regionΩ(119900)

1015840

(1) the undetected AP 120574(119900)119894119895 larr 100(2) while 120574(119900)

119896119904= 119899119906119897119897 do

(3) if 120574(119900)119896119904= 100 then

(4) the unknowed point is not adjacent to 119896-st AP(5) delete 119896(6) else(7) the unknowed point is adjacent to 119896-st AP(8) store 119896(9) end if(10) end while(11) count all 119896(12) return coarse localization region Ω(119900)

1015840

Algorithm 1 Coarse localization algorithm

the experimental workspace in such a way that the averagesignal power is high it is likely to be one of the best choicesto reduce localization error In order to reduce the noise as faras possible if the APs can be deployed by ourselves it is betterto be distributed evenly around the workspace as frontal way

32 Online Localization Phase The online localization phaseconsists of coarse localization and precise localization Thegoal of the coarse localization phase is to reduce the impossi-ble regions from all the fingerprintmapThus it removes par-tial impossible fingerprint and reduces the computation effortof the precise localization phase The greater the fingerprintmap is the bigger the reduction impossible regions are as isshown in Section 62 In the precise localization phase thelocalization is computed by using the cluster KNN algorithmA detailed introduction is given in the following

4 The Efficiency of Cluster KNN

41 Coarse Localization Due to the wide deployment of APsthe total number of detectable APs is generally much greaterthan that required for localization which leads to redundantcomputations Furthermore unreliable APs with large RSSvariances may also lead to biased estimation and reduce thestability of the localization accuracyThismotivates the use ofAPs selection techniques to select a subset of available APs forcoarse localization Since the mobile devices detect differentnumber of APs in different region the detected APs can beused to coarse localization

The coarse localization is processed by comparing thedetected APs to infer the rough localization region The real-time measurement RSS can be represented as the columns of120574(119900)

119896119904= [120574(119900)1119904 120574(119900)2119904 120574

(119900)

119871119904]119879 119904 isin 119873 where the superscript 119879

denotes transposition Search for the 120574(119900)119896119904

from the fingerprintmapΩ(119900) based on the detected APs If a fingerprint includesall the detected APs we can infer that the mobile devices arein this regionThe algorithm is represented as in Alogrithm 1

5 10 15 20 25 30 35minus10

minus505

1015

Reference points

Fluc

tuat

ion

24G WiFi signal5G WiFi signal

Figure 1 Signal fluctuation

42 Precise Localization The major challenge for accurateRSS-based localization comes from the variations of RSS dueto the dynamic and unpredictable nature of radio channeland the RSS is easily affected by environmental changessuch as shadowing and multipath It is variable that the RSSis received from the same point at different time so thereal-time measurement RSS is not too credible for accuratelocalization To decrease the effect with variational signal andanalysis upon 24G and 5GWiFi signal we get that absolutevalue of signal fluctuation is under 5 dBm as is shown inFigure 1 and about 93 fluctuation is within 5 dBm in all thefluctuations of measurement (24G and 5G band signal) Inour experiment the 5G signal fluctuation is all within 5 dBmand about 86 fluctuation is within 5 dBm in the 24G signalTo avoid the inaccurate nearest neighbor points match withunauthentic real-time RSS prior to conducting localizationalgorithm the RSS is expanded by plusmn5 dBm and then it usedfor localization

In addition the ecumenic signal propagation model is

119871119901 (dBm) = 119871119901 (119889119900) + 10119899log10 (119889

1198890

) + 119883120590 (2)

where119871119901(119889119900)parameter represents the path loss at a referencedistance 1198890 typically onemeter 119899 is the constant propagation

International Journal of Distributed Sensor Networks 5

Figure 2 The distribution of qualified nearest neighbor points

Require coarse localization region Ω(119900)1015840

Require a real time measurement RSS 120574(119900)

119896119904 120574(119900)

119896119904isin 120574(119900)

119894119895 119894 = 1 119871 119895 = 1 119873Ensure precise localization coordinate (119909 119910)(1) for all 120574(119900)1015840

119896119904= 120574(119900)

119896119904plusmn 5 dBm do

(2) end for(3) for all ManDist (120574(119900)

1015840

119896119904 Ω(119900)1015840

) =sum119899

119894=1

100381610038161003816100381610038161003816120574(119900)1015840

119896119904minus Ω(119900)1015840 100381610038161003816100381610038161003816do

(4) end for(5) obtain nearest neighbor points set 120574(119900)

119896119899

(6) while Θ = (120574(119900)

119896119899119886 120574(119900)

119896119899119887

120574(119900)

119896119899119888) = 119899119906119897119897 do

(7) if min (119899120572 119899120573 119899120579) = (1003816100381610038161003816119899119886 minus 119899119887

1003816100381610038161003816 +1003816100381610038161003816119899119887 minus 119899119888

1003816100381610038161003816) le (10038161003816100381610038161003816119899120572 minus 119899120573

10038161003816100381610038161003816+10038161003816100381610038161003816119899120573 minus 119899120579

10038161003816100381610038161003816) (120572 120573 120579) isin 119899 then

(8) min (119904 (119899119886 119899119887 119899119888)) = (1003816100381610038161003816119904 minus 119899119886

1003816100381610038161003816 +1003816100381610038161003816119904 minus 119899119887

1003816100381610038161003816 +1003816100381610038161003816119904 minus 119899119888

1003816100381610038161003816) le (1003816100381610038161003816119904 minus 119899120572

1003816100381610038161003816 +10038161003816100381610038161003816119904 minus 119899120573

10038161003816100381610038161003816+1003816100381610038161003816119904 minus 119899120579

1003816100381610038161003816) (120572 120573 120579) isin 119899(9) obtain the optimal nearest neighbor points set Θ = (120574

(119900)

119896119899119886 120574(119900)

119896119899119887

120574(119900)

119896119899119888)

(10) else(11) continue(12) end if(13) end while(14) return precise localization coordinate (119909 119910) =((119909119899119886 + 119909119899119887 + 119909119899119888) 3 (119910119899119886 + 119910119899119887 + 119910119899119888) 3)

Algorithm 2 Precise localization algorithm

value 119889 is the distance between the transmitter and thereceiver devices and 119883120590 is a Gaussian random variablewith mean 0 and standard deviation 120590 From the signalpropagation model when the mobile devices are equidistantfrom the AP in line of sight the mobile devices receivethe same signal strength This phenomenon should causelarge error for localization In Figure 2 the same colorpoints represent the qualified nearest neighbor points if thelocalization algorithm is processed based on all the qualifiedpoints the localization error will be very large

In our experiment the range of signal strength is betweenminus30 dBm and minus99 dBm and all the RSS of undetected APsrepresentsminus100 dBmMany different RPs have the same value

of RSS in the fingerprint map if we use traditional nearestneighbor algorithms to find the optimal matching RPs itwill produce large error for localization For the sake ofsearching the optimalmatchingRPs not only the value of RSSbut also the relationship of RPs is considered In the offlinetraining phase the fingerprint map is collected in sequentialorder and not only the subscript of RPs does represent theserial number but also the 119895 implies the relationship of RPsGenerally the coterminous RPs have analogical value of RSSand in the process of searching for the nearest neighbor pointsthe real-time RSS should be matched with the coterminousRPs that have the analogical value of RSS The detailedalgorithm is represented as in Algorithm 2

6 International Journal of Distributed Sensor Networks

According to precise localization algorithm we selectthree optimal nearest neighbor points 119899119886 119899119887 and 119899119888 as local-ization the ultimate localization estimation is represented as(119909 119910)

119909 =1

3(119909119899119886 + 119909119899119887 + 119909119899119888)

119910 =1

3(119910119899119886 + 119910119899119887 + 119910119899119888)

(3)

5 Accurate Localization Based on 5 GWiFi Signal

The indoor radio environment is quite complex Becausethe 24G is no permit limitation band a wide variety ofequipment use the band at the same time It is unavoidableto be interfered by the same frequency equipment that hasbeen introduced in Section 1 The interference signal willgenerate signal fluctuation when we detect the WiFi signalfrom surrounding access points

The entry level speed of 5GWiFi is 433Mbps which is atleast three times compared to that of the 24G WiFi and thehigh performance of 5G WiFi can reach more than 1GbpsThe high transmission rate can satisfy usersrsquo daily surfingneeds and provide stable and high quality signal as well

In this section we explore the impact of frequencyband (24G and 5G) to evaluate the localization accuracyWhile 24G signal is the only band originally used for WiFiincreasingly 5G signal is also used despite of its poorerpropagation characteristics resulting from higher frequencyoperation As the 5G frequency band is less crowded thereis far more spectrum available in 5G band From a WiFifingerprint localization system perspective in a typical envi-ronment today with APs using both 24G and 5G bands ameasurement RSS collected during either the offline trainingphase or the online localization phase will likely include amixof 24G and 5G APs

Figure 3 shows that the signal strength of 5G is strongerthan that of 24G Generally stronger signal is more stableand stable signal can guarantee the high localization accuracy

Besides this from Figures 4 and 5 we can get that the5G signal is more stable than that of 24G in the sameenvironment From 30-st RP to 35-st RP the locus of 30-st RPsim35-st RP in Figures 4 and 5 indicates the 5G signalhas poorer propagation characteristics resulting from higherfrequency operation

Today more and more APs have double frequency bandand the 5G signal seems to be muchmore suitable for indoorlocalization than 24G signal Due to a lack of cochannelinterference it can be feasible to use more stable RSS for thepurpose of accurate localization So the 5G signal is selectedas localization estimation in our localization system

6 Experiments and Evaluations

61 Experiments Setup In this section we present the imple-mentation and experimental evaluations of the proposed sys-temThefingerprint database is collected by the smartphones

its network card can detect both 24G and 5G signals Theworkspace is equipped with WiFi environment by five TP-LINK WDR6300 routers that can emit 24G and 5G WiFisignal The total area is 36 times 258 meters consisting ofhallways and some classroomsWe collect 40 reference pointsevenly from the hallways region and obtain WiFi fingerprintdatabase using smartphone with the application software thatis our developed mobile software application to collect RSSand build the fingerprint map automaticallyWhen collectingthe fingerprint map the smartphones are kept at the sameheight of approximately an adultrsquos breast and sometimesrotated horizontally at the same position to face differentdirections In particular for the RPs data 20 samples at eachRP with a rate of 1 samplesecond are collected by a userwalking through the hallway area

611 Performance Metric Besides that according to [9 43]Manhattan distance performs slightly better than Euclideandistance and our workspace is a regular rectangle we usethe Manhattan distance as the standard of error analysisTheManhattan distance is an expression of geometric metricspace It is defined as the sum of the absolute differences ofvalues in a real-time measurement RSS from fingerprint asindicated by the following equation

ManDist (120574(119900)119896119904 120574(119900)

119894119895 ) =

119873

sum

119895=1

10038161003816100381610038161003816120574(119900)

119896119904minus 120574(119900)

119894119895

10038161003816100381610038161003816 (4)

where ManDist(sdotsdot) is the Manhattan distance function 120574(119900)119896119904

is the real-time measurement RSS 120574(119900)119894119895

is one of fingerprintdatabases

612 Algorithms Compared We run the following algo-rithms for comparison

(i) 119896-nearest neighbor (KNN) this is the most popularused algorithm due to its excellent tradeoff betweenaccuracy and computation complexity It obtains the 119896nearest neighbors in the online localization phase insignal space among the known fingerprint maps ldquo119896rdquois a parameter adapted to each localization system toobtain better performance

(ii) Weighted 119896-nearest neighbor (WKNN) the proce-dure is similar to the 119896-nearest neighbor The onlydifference is that the average of the coordinates is aweighted average

(iii) Fuzzy logic it is used to select which points are themost important to calculate the final coordinates ofthe current position and to assess their correspondingweight in the average As for the other algorithmsthe first step after acquiring the current value of thereceived signal strength is to determine the distancein the signal domain between the current position andall the points that make part of the fingerprint mapThe next step is to transform these distance valuesinto grades of membership that is the fuzzificationis made

International Journal of Distributed Sensor Networks 7

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP1 (24G)RSS from AP1 (5G)

(a) RSS from AP1

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50minus40

Reference points

RSS

RSS from AP2 (24G)RSS from AP2 (5G)

(b) RSS from AP2

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP3 (24G)RSS from AP3 (5G)

(c) RSS from AP3

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference pointsRS

S

RSS from AP4 (24G)RSS from AP4 (5G)

(d) RSS from AP4

0 5 10 15 20 25 30 35 40minus100minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP5 (24G)RSS from AP5 (5G)

(e) RSS from AP5

Figure 3 Received signal strength from different APs

(iv) Bayesian histogrammethod the probabilisticmethodis more complex is and based on the Bayes ruleIt is classified as the kernel method and histogrammethod generally In the kernel method a proba-bility mass is assigned to a kernel around the dataobserved the probability is then computed usinga kernel function The histogram method (used inour experiments) uses bins or value categorization tocover all measurement range according to these binswe can then calculate the probability (existence of anAP in a certain position) thus each AP will appearwith different probability and we can estimate thelocation according to the probabilities

62 The Performance of Computation Effort In the coarselocalization phase the approximate localization region isinferred by the detected APs According to the detected APs

remaining fingerprint database cannot include the detectedAPs so we do not need to match the remaining fingerprintdatabase which can reduce the computation effort heavily inthe precise localization phase And the larger the fingerprintdatabase is the more efficient it is From Figures 6 and 7 wecan find that not all the APs can be detected in each point

Supposing that every unknown point can detect 5 APsand there are 40 RPs as the number of RPs increases thealgorithm shows greater efficiency And the computationeffort is reduced dramatically while the fingerprint mapincreases as is shown in Figure 8

63 The Performance of Localization Accuracy FromSection 4 the 5G signal is more stable than 24G if bothsignals can be received by our mobile devices the 5GHzsignal should have priority to be used for localizationestimation To fair evaluate the performance of our proposed

8 International Journal of Distributed Sensor Networks

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40

minus20RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40

minus20RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP5

RP

RSS

Figure 4 Signal fluctuation at all the RPs for 24GWiFi signal

algorithm as is described in detail in Section 3 we comparethe proposed algorithm with KNN WKNN fuzzy logicand histogram algorithm and the localization error of CDF(cumulative distribution function) is showed in Figures 9and 10 It demonstrates that the cluster KNN algorithmobtains the superior localization accuracy in both 24G and5GWiFi signals

From Figure 11 regardless of 24G and 5G signal theaverage error of cluster KNN is the smallest of all algorithmsThe 24G average errors of KNN WKNN fuzzy logichistogram and cluster KNN algorithm are 45700 4384046213 45483 and 14700 respectivelyThe 5G average errorsof the above algorithms are 27475 32122 25450 37895 and11500 respectively (In this study in order to simplify theexpression of error the distance between two neighbor RPsis defined as one unit error and all the error data omit unit)

64 The Performance of Localization Stabilization In addi-tion we also analyse the variance to evaluate the localization

stabilization Besides localization accuracy the localizationstabilization is also important for localization system Inour experiment the 24G variances of KNN WKNN fuzzylogic histogram and cluster KNN algorithm are 116129115713 116833 76819 and 18560 respectively The 5Gvariances of the above algorithms are 93477 94714 9310275370 and 11500 respectively as is showed in Figure 12The smaller the variance is the more stable the localizationpresents It indicates that not only localization accuracy butalso localization stabilization is improved with cluster KNNalgorithm (In this study in order to simplify the expression ofvariance the distance between two neighbor RPs is defined asone unit error and all the variance data omit unit)

7 Conclusion

This paper focuses on improving the localization accuracystabilization and reducing the computation effort by the pro-posed localization system that consists of coarse and precise

International Journal of Distributed Sensor Networks 9

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70RSS from AP5

RP

RSS

Figure 5 Signal fluctuation at all the RPs for 5GWiFi signal

1 5 10 15 20 25 30 35 40Reference points

AP1 (24G)AP2 (24G)AP3 (24G)

AP4 (24G)AP5 (24G)

Figure 6 Detected APs from all RPs (24G)

localization In the coarse localization we use the detectedAPs to infer the coarse localization region It can eliminate the

1 5 10 15 20 25 30 35 40Reference points

AP1 (5G)AP2 (5G)AP3 (5G)

AP4 (5G)AP5 (5G)

Figure 7 Detected APs from all RPs (5G)

impossible fingerprint database to reduce the computationeffort Besides the fingerprint map is built by smartphone

10 International Journal of Distributed Sensor Networks

0 50 100 150 200 250 300 350 400 450 5000

02040608

1

The number of RPs

Effici

ency

Figure 8 Matching efficiency with the increased RPs

0 2 4 6 8 10 12 140010203040506070809

1

Error distance

CDF

KNN (24G)WKNN (24G)Fuzzy logic (24G)

Histogram (24G)Cluster KNN (24G)

Figure 9Distribution of the localization error for 24GWiFi signal

0 2 4 6 8 10 12 14 16 18 200

010203040506070809

1

Error distance

CDF

KNN (5G)WKNN (5G)Fuzzy logic (5G)

Histogram (5G)Cluster KNN (5G)

Figure 10 Distribution of the localization error for 5GWiFi signal

with our developed software application automatically Itimproved efficiency of system operation enormously In theprecise localization we propose two approaches to improvethe localization accuracy and stabilization By analysing thechange rule of WiFi signal about plusmn5 dBm of fluctuation isin the 24G and 5G signal so the real-time measurementRSS is not so accurate as localization estimation The real-time measurement RSS is firstly expanded by plusmn5 dBm andthen used for thematching with the fingerprint map In orderto select the optimal nearest neighbor points we use therelationship of RPs to eliminate the discrete nearest neigh-bor points Experimental results indicate that the proposed

005

115

225

335

445

5

Different algorithms

Aver

age e

rror

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 11 Localization mean error for 24G and 5GWiFi signal

0

2

4

6

8

10

12

Different algorithms

Varia

nce

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 12 Variance of localization error for 24G and 5G WiFisignal

algorithm is the most accurate and stable of all comparativealgorithms In addition our experiment also demonstratesthat the 5GWiFi signal is more stable for indoor localization

In the future we intend to perform this system to applyit in larger indoor construction as well as to investigatingthe performance of the proposed algorithm in more complexenvironment and the multilayered buildings

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This work was supported by the National Natural ScienceFoundation of China under Grant Project nos 61232004 and61103085

International Journal of Distributed Sensor Networks 11

References

[1] B Hofmann-Wellenhof H Lichtenegger and J Collins ldquoGlobalpositioning system theory and practicerdquo in Global PositioningSystem Theory and Practice B Hofmann-Wellenhof H Licht-enegger and J Collins Eds vol 1 p 387 Springer ViennaAustria 1993

[2] N Bulusu J Heidemann and D Estrin ldquoGPS-less low-costoutdoor localization for very small devicesrdquo IEEE PersonalCommunications vol 7 no 5 pp 28ndash34 2000

[3] C Lee Y Chang G Park et al ldquoIndoor positioning systembased on incident angles of infrared emittersrdquo in Proceedings ofthe 30thAnnual Conference of IEEE Industrial Electronics Society(IECON rsquo04) vol 3 pp 2218ndash2222 Busan Republic of KoreaNovember 2004

[4] A Shahi A Aryan J S West C T Haas and R C G HaasldquoDeterioration of UWB positioning during constructionrdquoAutomation in Construction vol 24 pp 72ndash80 2012

[5] V Filonenko C Cullen and J Carswell ldquoInvestigating ultra-sonic positioning on mobile phonesrdquo in Proceedings of theInternational Conference on Indoor Positioning and IndoorNavigation (IPIN rsquo10) pp 1ndash8 IEEE September 2010

[6] S Li Y Lou and B Liu ldquoBluetooth aided mobile phone local-ization a nonlinear neural circuit approachrdquoACMTransactionson Embedded Computing Systems vol 13 no 4 p 78 2014

[7] N Li and B Becerik-Gerber ldquoPerformance-based evaluationof RFID-based indoor location sensing solutions for the builtenvironmentrdquo Advanced Engineering Informatics vol 25 no 3pp 535ndash546 2011

[8] L Luoh ldquoZigBee-based intelligent indoor positioning systemsoft computingrdquo Soft Computing vol 18 no 3 pp 443ndash4562014

[9] Y Chen D Lymberopoulos J Liu and B Priyantha ldquoIndoorlocalization using FM signalsrdquo IEEE Transactions on MobileComputing vol 12 no 8 pp 1502ndash1517 2013

[10] V Moghtadaiee and A G Dempster ldquoIndoor location finger-printing using fm radio signalsrdquo IEEE Transactions on Broad-casting vol 60 no 2 pp 336ndash346 2014

[11] J Chung M Donahoe C Schmandt I-J Kim P Razavai andM Wiseman ldquoIndoor location sensing using geo-magnetismrdquoin Proceedings of the 9th International Conference on MobileSystems Applications and Services (MobiSys rsquo11) pp 141ndash154July 2011

[12] I Bisio M Cerruti F Lavagetto et al ldquoA trainingless WiFifingerprint positioning approach over mobile devicesrdquo IEEEAntennas and Wireless Propagation Letters vol 13 pp 832ndash8352014

[13] N Alsindi Z Chaloupka N AlKhanbashi and J Aweya ldquoAnempirical evaluation of a probabilistic rf signature for wlanlocation fingerprintingrdquo IEEETransactions onWireless Commu-nications vol 13 no 6 pp 3257ndash3268 2014

[14] CWu Z Yang and Y Liu ldquoSmartphones based crowdsourcingfor indoor localizationrdquo 2014

[15] ADanalet B Farooq andM Bierlaire ldquoABayesian approach todetect pedestrian destination-sequences fromWiFi signaturesrdquoTransportation Research Part C Emerging Technologies vol 44pp 146ndash170 2014

[16] R S Campos L Lovisolo and M L R De Campos ldquoWi-Fimulti-floor indoor positioning considering architectural aspectsand controlled computational complexityrdquo Expert Systems withApplications vol 41 no 14 pp 6211ndash6223 2014

[17] M Heidari and K Pahlavan ldquoA new statistical model for thebehavior of ranging errors in TOA-based indoor localiza-tionrdquo in Proceedings of the IEEE Wireless Communications andNetworking Conference (WCNC rsquo07) pp 2564ndash2569 IEEEKowloon Hong Kong March 2007

[18] P-H Tseng K-T Feng Y-C Lin and C-L Chen ldquoWirelesslocation tracking algorithms for environments with insufficientsignal sourcesrdquo IEEE Transactions on Mobile Computing vol 8no 12 pp 1676ndash1689 2009

[19] M Bocquet C Loyez andA Benlarbi-Delaı ldquoUsing enhanced-TDOA measurement for indoor positioningrdquo IEEE Microwaveand Wireless Components Letters vol 15 no 10 pp 612ndash6142005

[20] C-H Lim B P Ng and D Da ldquoRobust methods for AOA geo-location in a real-time indoorWiFi systemrdquo Journal of LocationBased Services vol 2 no 2 pp 112ndash121 2008

[21] K Wu J Xiao Y Yi D Chen X Luo and L M Ni ldquoCSI-basedindoor localizationrdquo IEEE Transactions on Parallel and Dis-tributed Systems vol 24 no 7 pp 1300ndash1309 2013

[22] Z Yang Z Zhou and Y Liu ldquoFrom RSSI to CSI indoorlocalization via channel responserdquo ACM Computing Surveysvol 46 no 2 article 25 2013

[23] A Jaffe and M Wax ldquoSingle-site localization via maximumdiscrimination multipath fingerprintingrdquo IEEE Transactions onSignal Processing vol 62 no 7 pp 1718ndash1728 2014

[24] C Feng W S A Au S Valaee and Z Tan ldquoReceived-signal-strength-based indoor positioning using compressive sensingrdquoIEEETransactions onMobile Computing vol 11 no 12 pp 1983ndash1993 2012

[25] M Ficco C Esposito and A Napolitano ldquoCalibrating indoorpositioning systemswith low effortsrdquo IEEE Transactions onMobile Computing vol 13 no 4 pp 737ndash751 2014

[26] F Yu M H Jiang J Liang et al ldquoAn improved indoor local-ization of wifi based on support vector machinesrdquo InternationalJournal of Future Generation Communication and Networkingvol 7 pp 191ndash206 2014

[27] S Shekhar and H Xiong ldquoIndoor positioning systemrdquo inEncyclopedia of GIS p 566 Springer NewYork NY USA 2008

[28] H Liu H Darabi P Banerjee and J Liu ldquoSurvey of wirelessindoor positioning techniques and systemsrdquo IEEE Transactionson Systems Man and Cybernetics Part C Applications andReviews vol 37 no 6 pp 1067ndash1080 2007

[29] M Molina-Garcıa J Calle-Sanchez J I Alonso A Fernandez-Duran and F B Barba ldquoEnhanced in-building fingerprint posi-tioning using femtocell networksrdquo Bell Labs Technical Journalvol 18 no 2 pp 195ndash211 2013

[30] A Kushki K N Plataniotis and A N VenetsanopoulosldquoKernel-based positioning in wireless local area networksrdquoIEEE Transactions on Mobile Computing vol 6 no 6 pp 689ndash705 2007

[31] T King T Haenselmann andW Effelsberg ldquoDeployment cal-ibration and measurement factors for position errors in 80211-based indoor positioning systemsrdquo in Location-and Context-Awareness pp 17ndash34 Springer 2007

[32] X Chai and Q Yang ldquoReducing the calibration effort forprobabilistic indoor location estimationrdquo IEEE Transactions onMobile Computing vol 6 no 6 pp 649ndash662 2007

[33] C Steiner and A Wittneben ldquoEfficient training phase forultrawideband-based location fingerprinting systemsrdquo IEEETransactions on Signal Processing vol 59 no 12 pp 6021ndash60322011

12 International Journal of Distributed Sensor Networks

[34] Z Yang C Wu and Y Liu ldquoLocating in fingerprint spacewireless indoor localization with little human interventionrdquoin Proceedings of the 18th Annual International Conference onMobile Computing and Networking pp 269ndash280 August 2012

[35] Y Chon N D Lane F Li H Cha and F Zhao ldquoAutomaticallycharacterizing places with opportunistic crowdsensing usingsmartphonesrdquo in Proceedings of the 14th International Confer-ence on Ubiquitous Computing (UbiComp rsquo12) pp 481ndash490Pittsburgh Pa USA September 2012

[36] P Torteeka and X Chundi ldquoIndoor positioning based on Wi-Fi fingerprint technique using fuzzy K-nearest neighborrdquo inProceedings of the 11th International Bhurban Conference onApplied Sciences and Technology (IBCAST rsquo14) pp 461ndash465Islamabad Pakistan January 2014

[37] B Shin J H Lee T Lee and H S Kim ldquoEnhanced weightedK-nearest neighbor algorithm for indoor Wi-Fi positioningsystemsrdquo in Proceedings of the 8th International Conference onComputing Technology and Information Management (ICCMrsquo12) pp 574ndash577 April 2012

[38] D Madigan E Einahrawy R P Martin W-H Ju P Krishnanand A Krishnakumar ldquoBayesian indoor positioning systemsrdquoin Proceedings of the 24th Annual Joint Conference of the IEEEComputer and Communications Societies (INFOCOM rsquo05) vol2 pp 1217ndash1227 IEEE 2005

[39] M Zhou Y Xu and L Tang ldquoMultilayer ANN indoor locationsystem with area division in WLAN environmentrdquo Journal ofSystems Engineering and Electronics vol 21 no 5 pp 914ndash9262010

[40] Z-L Wu C-H Li J K-Y Ng and K R P H Leung ldquoLocationestimation via support vector regressionrdquo IEEE Transactions onMobile Computing vol 6 no 3 pp 311ndash321 2007

[41] F Yu M H Jiang J Liang et al ldquoAn indoor localization ofwifi based on support vector machinesrdquo in Advanced MaterialsResearch vol 926 pp 2438ndash2441 Trans Tech Publications 2014

[42] O Baala Y Zheng and A Caminada ldquoThe impact of APplacement in WLAN-based indoor positioning systemrdquo inProceedings of the 8th International Conference on Networks(ICN rsquo09) pp 12ndash17 Gosier France March 2009

[43] A Farshad J Li M K Marina and F J Garcia ldquoA microscopiclook at wifi fingerprinting for indoor mobile phone localizationin diverse environmentsrdquo in Proceedings of the InternationalConference on Indoor Positioning and Indoor Navigation (IPINrsquo13) pp 1ndash10 IEEE 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

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Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Shock and Vibration

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Civil EngineeringAdvances in

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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

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Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

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DistributedSensor Networks

International Journal of

International Journal of Distributed Sensor Networks 5

Figure 2 The distribution of qualified nearest neighbor points

Require coarse localization region Ω(119900)1015840

Require a real time measurement RSS 120574(119900)

119896119904 120574(119900)

119896119904isin 120574(119900)

119894119895 119894 = 1 119871 119895 = 1 119873Ensure precise localization coordinate (119909 119910)(1) for all 120574(119900)1015840

119896119904= 120574(119900)

119896119904plusmn 5 dBm do

(2) end for(3) for all ManDist (120574(119900)

1015840

119896119904 Ω(119900)1015840

) =sum119899

119894=1

100381610038161003816100381610038161003816120574(119900)1015840

119896119904minus Ω(119900)1015840 100381610038161003816100381610038161003816do

(4) end for(5) obtain nearest neighbor points set 120574(119900)

119896119899

(6) while Θ = (120574(119900)

119896119899119886 120574(119900)

119896119899119887

120574(119900)

119896119899119888) = 119899119906119897119897 do

(7) if min (119899120572 119899120573 119899120579) = (1003816100381610038161003816119899119886 minus 119899119887

1003816100381610038161003816 +1003816100381610038161003816119899119887 minus 119899119888

1003816100381610038161003816) le (10038161003816100381610038161003816119899120572 minus 119899120573

10038161003816100381610038161003816+10038161003816100381610038161003816119899120573 minus 119899120579

10038161003816100381610038161003816) (120572 120573 120579) isin 119899 then

(8) min (119904 (119899119886 119899119887 119899119888)) = (1003816100381610038161003816119904 minus 119899119886

1003816100381610038161003816 +1003816100381610038161003816119904 minus 119899119887

1003816100381610038161003816 +1003816100381610038161003816119904 minus 119899119888

1003816100381610038161003816) le (1003816100381610038161003816119904 minus 119899120572

1003816100381610038161003816 +10038161003816100381610038161003816119904 minus 119899120573

10038161003816100381610038161003816+1003816100381610038161003816119904 minus 119899120579

1003816100381610038161003816) (120572 120573 120579) isin 119899(9) obtain the optimal nearest neighbor points set Θ = (120574

(119900)

119896119899119886 120574(119900)

119896119899119887

120574(119900)

119896119899119888)

(10) else(11) continue(12) end if(13) end while(14) return precise localization coordinate (119909 119910) =((119909119899119886 + 119909119899119887 + 119909119899119888) 3 (119910119899119886 + 119910119899119887 + 119910119899119888) 3)

Algorithm 2 Precise localization algorithm

value 119889 is the distance between the transmitter and thereceiver devices and 119883120590 is a Gaussian random variablewith mean 0 and standard deviation 120590 From the signalpropagation model when the mobile devices are equidistantfrom the AP in line of sight the mobile devices receivethe same signal strength This phenomenon should causelarge error for localization In Figure 2 the same colorpoints represent the qualified nearest neighbor points if thelocalization algorithm is processed based on all the qualifiedpoints the localization error will be very large

In our experiment the range of signal strength is betweenminus30 dBm and minus99 dBm and all the RSS of undetected APsrepresentsminus100 dBmMany different RPs have the same value

of RSS in the fingerprint map if we use traditional nearestneighbor algorithms to find the optimal matching RPs itwill produce large error for localization For the sake ofsearching the optimalmatchingRPs not only the value of RSSbut also the relationship of RPs is considered In the offlinetraining phase the fingerprint map is collected in sequentialorder and not only the subscript of RPs does represent theserial number but also the 119895 implies the relationship of RPsGenerally the coterminous RPs have analogical value of RSSand in the process of searching for the nearest neighbor pointsthe real-time RSS should be matched with the coterminousRPs that have the analogical value of RSS The detailedalgorithm is represented as in Algorithm 2

6 International Journal of Distributed Sensor Networks

According to precise localization algorithm we selectthree optimal nearest neighbor points 119899119886 119899119887 and 119899119888 as local-ization the ultimate localization estimation is represented as(119909 119910)

119909 =1

3(119909119899119886 + 119909119899119887 + 119909119899119888)

119910 =1

3(119910119899119886 + 119910119899119887 + 119910119899119888)

(3)

5 Accurate Localization Based on 5 GWiFi Signal

The indoor radio environment is quite complex Becausethe 24G is no permit limitation band a wide variety ofequipment use the band at the same time It is unavoidableto be interfered by the same frequency equipment that hasbeen introduced in Section 1 The interference signal willgenerate signal fluctuation when we detect the WiFi signalfrom surrounding access points

The entry level speed of 5GWiFi is 433Mbps which is atleast three times compared to that of the 24G WiFi and thehigh performance of 5G WiFi can reach more than 1GbpsThe high transmission rate can satisfy usersrsquo daily surfingneeds and provide stable and high quality signal as well

In this section we explore the impact of frequencyband (24G and 5G) to evaluate the localization accuracyWhile 24G signal is the only band originally used for WiFiincreasingly 5G signal is also used despite of its poorerpropagation characteristics resulting from higher frequencyoperation As the 5G frequency band is less crowded thereis far more spectrum available in 5G band From a WiFifingerprint localization system perspective in a typical envi-ronment today with APs using both 24G and 5G bands ameasurement RSS collected during either the offline trainingphase or the online localization phase will likely include amixof 24G and 5G APs

Figure 3 shows that the signal strength of 5G is strongerthan that of 24G Generally stronger signal is more stableand stable signal can guarantee the high localization accuracy

Besides this from Figures 4 and 5 we can get that the5G signal is more stable than that of 24G in the sameenvironment From 30-st RP to 35-st RP the locus of 30-st RPsim35-st RP in Figures 4 and 5 indicates the 5G signalhas poorer propagation characteristics resulting from higherfrequency operation

Today more and more APs have double frequency bandand the 5G signal seems to be muchmore suitable for indoorlocalization than 24G signal Due to a lack of cochannelinterference it can be feasible to use more stable RSS for thepurpose of accurate localization So the 5G signal is selectedas localization estimation in our localization system

6 Experiments and Evaluations

61 Experiments Setup In this section we present the imple-mentation and experimental evaluations of the proposed sys-temThefingerprint database is collected by the smartphones

its network card can detect both 24G and 5G signals Theworkspace is equipped with WiFi environment by five TP-LINK WDR6300 routers that can emit 24G and 5G WiFisignal The total area is 36 times 258 meters consisting ofhallways and some classroomsWe collect 40 reference pointsevenly from the hallways region and obtain WiFi fingerprintdatabase using smartphone with the application software thatis our developed mobile software application to collect RSSand build the fingerprint map automaticallyWhen collectingthe fingerprint map the smartphones are kept at the sameheight of approximately an adultrsquos breast and sometimesrotated horizontally at the same position to face differentdirections In particular for the RPs data 20 samples at eachRP with a rate of 1 samplesecond are collected by a userwalking through the hallway area

611 Performance Metric Besides that according to [9 43]Manhattan distance performs slightly better than Euclideandistance and our workspace is a regular rectangle we usethe Manhattan distance as the standard of error analysisTheManhattan distance is an expression of geometric metricspace It is defined as the sum of the absolute differences ofvalues in a real-time measurement RSS from fingerprint asindicated by the following equation

ManDist (120574(119900)119896119904 120574(119900)

119894119895 ) =

119873

sum

119895=1

10038161003816100381610038161003816120574(119900)

119896119904minus 120574(119900)

119894119895

10038161003816100381610038161003816 (4)

where ManDist(sdotsdot) is the Manhattan distance function 120574(119900)119896119904

is the real-time measurement RSS 120574(119900)119894119895

is one of fingerprintdatabases

612 Algorithms Compared We run the following algo-rithms for comparison

(i) 119896-nearest neighbor (KNN) this is the most popularused algorithm due to its excellent tradeoff betweenaccuracy and computation complexity It obtains the 119896nearest neighbors in the online localization phase insignal space among the known fingerprint maps ldquo119896rdquois a parameter adapted to each localization system toobtain better performance

(ii) Weighted 119896-nearest neighbor (WKNN) the proce-dure is similar to the 119896-nearest neighbor The onlydifference is that the average of the coordinates is aweighted average

(iii) Fuzzy logic it is used to select which points are themost important to calculate the final coordinates ofthe current position and to assess their correspondingweight in the average As for the other algorithmsthe first step after acquiring the current value of thereceived signal strength is to determine the distancein the signal domain between the current position andall the points that make part of the fingerprint mapThe next step is to transform these distance valuesinto grades of membership that is the fuzzificationis made

International Journal of Distributed Sensor Networks 7

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP1 (24G)RSS from AP1 (5G)

(a) RSS from AP1

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50minus40

Reference points

RSS

RSS from AP2 (24G)RSS from AP2 (5G)

(b) RSS from AP2

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP3 (24G)RSS from AP3 (5G)

(c) RSS from AP3

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference pointsRS

S

RSS from AP4 (24G)RSS from AP4 (5G)

(d) RSS from AP4

0 5 10 15 20 25 30 35 40minus100minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP5 (24G)RSS from AP5 (5G)

(e) RSS from AP5

Figure 3 Received signal strength from different APs

(iv) Bayesian histogrammethod the probabilisticmethodis more complex is and based on the Bayes ruleIt is classified as the kernel method and histogrammethod generally In the kernel method a proba-bility mass is assigned to a kernel around the dataobserved the probability is then computed usinga kernel function The histogram method (used inour experiments) uses bins or value categorization tocover all measurement range according to these binswe can then calculate the probability (existence of anAP in a certain position) thus each AP will appearwith different probability and we can estimate thelocation according to the probabilities

62 The Performance of Computation Effort In the coarselocalization phase the approximate localization region isinferred by the detected APs According to the detected APs

remaining fingerprint database cannot include the detectedAPs so we do not need to match the remaining fingerprintdatabase which can reduce the computation effort heavily inthe precise localization phase And the larger the fingerprintdatabase is the more efficient it is From Figures 6 and 7 wecan find that not all the APs can be detected in each point

Supposing that every unknown point can detect 5 APsand there are 40 RPs as the number of RPs increases thealgorithm shows greater efficiency And the computationeffort is reduced dramatically while the fingerprint mapincreases as is shown in Figure 8

63 The Performance of Localization Accuracy FromSection 4 the 5G signal is more stable than 24G if bothsignals can be received by our mobile devices the 5GHzsignal should have priority to be used for localizationestimation To fair evaluate the performance of our proposed

8 International Journal of Distributed Sensor Networks

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40

minus20RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40

minus20RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP5

RP

RSS

Figure 4 Signal fluctuation at all the RPs for 24GWiFi signal

algorithm as is described in detail in Section 3 we comparethe proposed algorithm with KNN WKNN fuzzy logicand histogram algorithm and the localization error of CDF(cumulative distribution function) is showed in Figures 9and 10 It demonstrates that the cluster KNN algorithmobtains the superior localization accuracy in both 24G and5GWiFi signals

From Figure 11 regardless of 24G and 5G signal theaverage error of cluster KNN is the smallest of all algorithmsThe 24G average errors of KNN WKNN fuzzy logichistogram and cluster KNN algorithm are 45700 4384046213 45483 and 14700 respectivelyThe 5G average errorsof the above algorithms are 27475 32122 25450 37895 and11500 respectively (In this study in order to simplify theexpression of error the distance between two neighbor RPsis defined as one unit error and all the error data omit unit)

64 The Performance of Localization Stabilization In addi-tion we also analyse the variance to evaluate the localization

stabilization Besides localization accuracy the localizationstabilization is also important for localization system Inour experiment the 24G variances of KNN WKNN fuzzylogic histogram and cluster KNN algorithm are 116129115713 116833 76819 and 18560 respectively The 5Gvariances of the above algorithms are 93477 94714 9310275370 and 11500 respectively as is showed in Figure 12The smaller the variance is the more stable the localizationpresents It indicates that not only localization accuracy butalso localization stabilization is improved with cluster KNNalgorithm (In this study in order to simplify the expression ofvariance the distance between two neighbor RPs is defined asone unit error and all the variance data omit unit)

7 Conclusion

This paper focuses on improving the localization accuracystabilization and reducing the computation effort by the pro-posed localization system that consists of coarse and precise

International Journal of Distributed Sensor Networks 9

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70RSS from AP5

RP

RSS

Figure 5 Signal fluctuation at all the RPs for 5GWiFi signal

1 5 10 15 20 25 30 35 40Reference points

AP1 (24G)AP2 (24G)AP3 (24G)

AP4 (24G)AP5 (24G)

Figure 6 Detected APs from all RPs (24G)

localization In the coarse localization we use the detectedAPs to infer the coarse localization region It can eliminate the

1 5 10 15 20 25 30 35 40Reference points

AP1 (5G)AP2 (5G)AP3 (5G)

AP4 (5G)AP5 (5G)

Figure 7 Detected APs from all RPs (5G)

impossible fingerprint database to reduce the computationeffort Besides the fingerprint map is built by smartphone

10 International Journal of Distributed Sensor Networks

0 50 100 150 200 250 300 350 400 450 5000

02040608

1

The number of RPs

Effici

ency

Figure 8 Matching efficiency with the increased RPs

0 2 4 6 8 10 12 140010203040506070809

1

Error distance

CDF

KNN (24G)WKNN (24G)Fuzzy logic (24G)

Histogram (24G)Cluster KNN (24G)

Figure 9Distribution of the localization error for 24GWiFi signal

0 2 4 6 8 10 12 14 16 18 200

010203040506070809

1

Error distance

CDF

KNN (5G)WKNN (5G)Fuzzy logic (5G)

Histogram (5G)Cluster KNN (5G)

Figure 10 Distribution of the localization error for 5GWiFi signal

with our developed software application automatically Itimproved efficiency of system operation enormously In theprecise localization we propose two approaches to improvethe localization accuracy and stabilization By analysing thechange rule of WiFi signal about plusmn5 dBm of fluctuation isin the 24G and 5G signal so the real-time measurementRSS is not so accurate as localization estimation The real-time measurement RSS is firstly expanded by plusmn5 dBm andthen used for thematching with the fingerprint map In orderto select the optimal nearest neighbor points we use therelationship of RPs to eliminate the discrete nearest neigh-bor points Experimental results indicate that the proposed

005

115

225

335

445

5

Different algorithms

Aver

age e

rror

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 11 Localization mean error for 24G and 5GWiFi signal

0

2

4

6

8

10

12

Different algorithms

Varia

nce

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 12 Variance of localization error for 24G and 5G WiFisignal

algorithm is the most accurate and stable of all comparativealgorithms In addition our experiment also demonstratesthat the 5GWiFi signal is more stable for indoor localization

In the future we intend to perform this system to applyit in larger indoor construction as well as to investigatingthe performance of the proposed algorithm in more complexenvironment and the multilayered buildings

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This work was supported by the National Natural ScienceFoundation of China under Grant Project nos 61232004 and61103085

International Journal of Distributed Sensor Networks 11

References

[1] B Hofmann-Wellenhof H Lichtenegger and J Collins ldquoGlobalpositioning system theory and practicerdquo in Global PositioningSystem Theory and Practice B Hofmann-Wellenhof H Licht-enegger and J Collins Eds vol 1 p 387 Springer ViennaAustria 1993

[2] N Bulusu J Heidemann and D Estrin ldquoGPS-less low-costoutdoor localization for very small devicesrdquo IEEE PersonalCommunications vol 7 no 5 pp 28ndash34 2000

[3] C Lee Y Chang G Park et al ldquoIndoor positioning systembased on incident angles of infrared emittersrdquo in Proceedings ofthe 30thAnnual Conference of IEEE Industrial Electronics Society(IECON rsquo04) vol 3 pp 2218ndash2222 Busan Republic of KoreaNovember 2004

[4] A Shahi A Aryan J S West C T Haas and R C G HaasldquoDeterioration of UWB positioning during constructionrdquoAutomation in Construction vol 24 pp 72ndash80 2012

[5] V Filonenko C Cullen and J Carswell ldquoInvestigating ultra-sonic positioning on mobile phonesrdquo in Proceedings of theInternational Conference on Indoor Positioning and IndoorNavigation (IPIN rsquo10) pp 1ndash8 IEEE September 2010

[6] S Li Y Lou and B Liu ldquoBluetooth aided mobile phone local-ization a nonlinear neural circuit approachrdquoACMTransactionson Embedded Computing Systems vol 13 no 4 p 78 2014

[7] N Li and B Becerik-Gerber ldquoPerformance-based evaluationof RFID-based indoor location sensing solutions for the builtenvironmentrdquo Advanced Engineering Informatics vol 25 no 3pp 535ndash546 2011

[8] L Luoh ldquoZigBee-based intelligent indoor positioning systemsoft computingrdquo Soft Computing vol 18 no 3 pp 443ndash4562014

[9] Y Chen D Lymberopoulos J Liu and B Priyantha ldquoIndoorlocalization using FM signalsrdquo IEEE Transactions on MobileComputing vol 12 no 8 pp 1502ndash1517 2013

[10] V Moghtadaiee and A G Dempster ldquoIndoor location finger-printing using fm radio signalsrdquo IEEE Transactions on Broad-casting vol 60 no 2 pp 336ndash346 2014

[11] J Chung M Donahoe C Schmandt I-J Kim P Razavai andM Wiseman ldquoIndoor location sensing using geo-magnetismrdquoin Proceedings of the 9th International Conference on MobileSystems Applications and Services (MobiSys rsquo11) pp 141ndash154July 2011

[12] I Bisio M Cerruti F Lavagetto et al ldquoA trainingless WiFifingerprint positioning approach over mobile devicesrdquo IEEEAntennas and Wireless Propagation Letters vol 13 pp 832ndash8352014

[13] N Alsindi Z Chaloupka N AlKhanbashi and J Aweya ldquoAnempirical evaluation of a probabilistic rf signature for wlanlocation fingerprintingrdquo IEEETransactions onWireless Commu-nications vol 13 no 6 pp 3257ndash3268 2014

[14] CWu Z Yang and Y Liu ldquoSmartphones based crowdsourcingfor indoor localizationrdquo 2014

[15] ADanalet B Farooq andM Bierlaire ldquoABayesian approach todetect pedestrian destination-sequences fromWiFi signaturesrdquoTransportation Research Part C Emerging Technologies vol 44pp 146ndash170 2014

[16] R S Campos L Lovisolo and M L R De Campos ldquoWi-Fimulti-floor indoor positioning considering architectural aspectsand controlled computational complexityrdquo Expert Systems withApplications vol 41 no 14 pp 6211ndash6223 2014

[17] M Heidari and K Pahlavan ldquoA new statistical model for thebehavior of ranging errors in TOA-based indoor localiza-tionrdquo in Proceedings of the IEEE Wireless Communications andNetworking Conference (WCNC rsquo07) pp 2564ndash2569 IEEEKowloon Hong Kong March 2007

[18] P-H Tseng K-T Feng Y-C Lin and C-L Chen ldquoWirelesslocation tracking algorithms for environments with insufficientsignal sourcesrdquo IEEE Transactions on Mobile Computing vol 8no 12 pp 1676ndash1689 2009

[19] M Bocquet C Loyez andA Benlarbi-Delaı ldquoUsing enhanced-TDOA measurement for indoor positioningrdquo IEEE Microwaveand Wireless Components Letters vol 15 no 10 pp 612ndash6142005

[20] C-H Lim B P Ng and D Da ldquoRobust methods for AOA geo-location in a real-time indoorWiFi systemrdquo Journal of LocationBased Services vol 2 no 2 pp 112ndash121 2008

[21] K Wu J Xiao Y Yi D Chen X Luo and L M Ni ldquoCSI-basedindoor localizationrdquo IEEE Transactions on Parallel and Dis-tributed Systems vol 24 no 7 pp 1300ndash1309 2013

[22] Z Yang Z Zhou and Y Liu ldquoFrom RSSI to CSI indoorlocalization via channel responserdquo ACM Computing Surveysvol 46 no 2 article 25 2013

[23] A Jaffe and M Wax ldquoSingle-site localization via maximumdiscrimination multipath fingerprintingrdquo IEEE Transactions onSignal Processing vol 62 no 7 pp 1718ndash1728 2014

[24] C Feng W S A Au S Valaee and Z Tan ldquoReceived-signal-strength-based indoor positioning using compressive sensingrdquoIEEETransactions onMobile Computing vol 11 no 12 pp 1983ndash1993 2012

[25] M Ficco C Esposito and A Napolitano ldquoCalibrating indoorpositioning systemswith low effortsrdquo IEEE Transactions onMobile Computing vol 13 no 4 pp 737ndash751 2014

[26] F Yu M H Jiang J Liang et al ldquoAn improved indoor local-ization of wifi based on support vector machinesrdquo InternationalJournal of Future Generation Communication and Networkingvol 7 pp 191ndash206 2014

[27] S Shekhar and H Xiong ldquoIndoor positioning systemrdquo inEncyclopedia of GIS p 566 Springer NewYork NY USA 2008

[28] H Liu H Darabi P Banerjee and J Liu ldquoSurvey of wirelessindoor positioning techniques and systemsrdquo IEEE Transactionson Systems Man and Cybernetics Part C Applications andReviews vol 37 no 6 pp 1067ndash1080 2007

[29] M Molina-Garcıa J Calle-Sanchez J I Alonso A Fernandez-Duran and F B Barba ldquoEnhanced in-building fingerprint posi-tioning using femtocell networksrdquo Bell Labs Technical Journalvol 18 no 2 pp 195ndash211 2013

[30] A Kushki K N Plataniotis and A N VenetsanopoulosldquoKernel-based positioning in wireless local area networksrdquoIEEE Transactions on Mobile Computing vol 6 no 6 pp 689ndash705 2007

[31] T King T Haenselmann andW Effelsberg ldquoDeployment cal-ibration and measurement factors for position errors in 80211-based indoor positioning systemsrdquo in Location-and Context-Awareness pp 17ndash34 Springer 2007

[32] X Chai and Q Yang ldquoReducing the calibration effort forprobabilistic indoor location estimationrdquo IEEE Transactions onMobile Computing vol 6 no 6 pp 649ndash662 2007

[33] C Steiner and A Wittneben ldquoEfficient training phase forultrawideband-based location fingerprinting systemsrdquo IEEETransactions on Signal Processing vol 59 no 12 pp 6021ndash60322011

12 International Journal of Distributed Sensor Networks

[34] Z Yang C Wu and Y Liu ldquoLocating in fingerprint spacewireless indoor localization with little human interventionrdquoin Proceedings of the 18th Annual International Conference onMobile Computing and Networking pp 269ndash280 August 2012

[35] Y Chon N D Lane F Li H Cha and F Zhao ldquoAutomaticallycharacterizing places with opportunistic crowdsensing usingsmartphonesrdquo in Proceedings of the 14th International Confer-ence on Ubiquitous Computing (UbiComp rsquo12) pp 481ndash490Pittsburgh Pa USA September 2012

[36] P Torteeka and X Chundi ldquoIndoor positioning based on Wi-Fi fingerprint technique using fuzzy K-nearest neighborrdquo inProceedings of the 11th International Bhurban Conference onApplied Sciences and Technology (IBCAST rsquo14) pp 461ndash465Islamabad Pakistan January 2014

[37] B Shin J H Lee T Lee and H S Kim ldquoEnhanced weightedK-nearest neighbor algorithm for indoor Wi-Fi positioningsystemsrdquo in Proceedings of the 8th International Conference onComputing Technology and Information Management (ICCMrsquo12) pp 574ndash577 April 2012

[38] D Madigan E Einahrawy R P Martin W-H Ju P Krishnanand A Krishnakumar ldquoBayesian indoor positioning systemsrdquoin Proceedings of the 24th Annual Joint Conference of the IEEEComputer and Communications Societies (INFOCOM rsquo05) vol2 pp 1217ndash1227 IEEE 2005

[39] M Zhou Y Xu and L Tang ldquoMultilayer ANN indoor locationsystem with area division in WLAN environmentrdquo Journal ofSystems Engineering and Electronics vol 21 no 5 pp 914ndash9262010

[40] Z-L Wu C-H Li J K-Y Ng and K R P H Leung ldquoLocationestimation via support vector regressionrdquo IEEE Transactions onMobile Computing vol 6 no 3 pp 311ndash321 2007

[41] F Yu M H Jiang J Liang et al ldquoAn indoor localization ofwifi based on support vector machinesrdquo in Advanced MaterialsResearch vol 926 pp 2438ndash2441 Trans Tech Publications 2014

[42] O Baala Y Zheng and A Caminada ldquoThe impact of APplacement in WLAN-based indoor positioning systemrdquo inProceedings of the 8th International Conference on Networks(ICN rsquo09) pp 12ndash17 Gosier France March 2009

[43] A Farshad J Li M K Marina and F J Garcia ldquoA microscopiclook at wifi fingerprinting for indoor mobile phone localizationin diverse environmentsrdquo in Proceedings of the InternationalConference on Indoor Positioning and Indoor Navigation (IPINrsquo13) pp 1ndash10 IEEE 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

6 International Journal of Distributed Sensor Networks

According to precise localization algorithm we selectthree optimal nearest neighbor points 119899119886 119899119887 and 119899119888 as local-ization the ultimate localization estimation is represented as(119909 119910)

119909 =1

3(119909119899119886 + 119909119899119887 + 119909119899119888)

119910 =1

3(119910119899119886 + 119910119899119887 + 119910119899119888)

(3)

5 Accurate Localization Based on 5 GWiFi Signal

The indoor radio environment is quite complex Becausethe 24G is no permit limitation band a wide variety ofequipment use the band at the same time It is unavoidableto be interfered by the same frequency equipment that hasbeen introduced in Section 1 The interference signal willgenerate signal fluctuation when we detect the WiFi signalfrom surrounding access points

The entry level speed of 5GWiFi is 433Mbps which is atleast three times compared to that of the 24G WiFi and thehigh performance of 5G WiFi can reach more than 1GbpsThe high transmission rate can satisfy usersrsquo daily surfingneeds and provide stable and high quality signal as well

In this section we explore the impact of frequencyband (24G and 5G) to evaluate the localization accuracyWhile 24G signal is the only band originally used for WiFiincreasingly 5G signal is also used despite of its poorerpropagation characteristics resulting from higher frequencyoperation As the 5G frequency band is less crowded thereis far more spectrum available in 5G band From a WiFifingerprint localization system perspective in a typical envi-ronment today with APs using both 24G and 5G bands ameasurement RSS collected during either the offline trainingphase or the online localization phase will likely include amixof 24G and 5G APs

Figure 3 shows that the signal strength of 5G is strongerthan that of 24G Generally stronger signal is more stableand stable signal can guarantee the high localization accuracy

Besides this from Figures 4 and 5 we can get that the5G signal is more stable than that of 24G in the sameenvironment From 30-st RP to 35-st RP the locus of 30-st RPsim35-st RP in Figures 4 and 5 indicates the 5G signalhas poorer propagation characteristics resulting from higherfrequency operation

Today more and more APs have double frequency bandand the 5G signal seems to be muchmore suitable for indoorlocalization than 24G signal Due to a lack of cochannelinterference it can be feasible to use more stable RSS for thepurpose of accurate localization So the 5G signal is selectedas localization estimation in our localization system

6 Experiments and Evaluations

61 Experiments Setup In this section we present the imple-mentation and experimental evaluations of the proposed sys-temThefingerprint database is collected by the smartphones

its network card can detect both 24G and 5G signals Theworkspace is equipped with WiFi environment by five TP-LINK WDR6300 routers that can emit 24G and 5G WiFisignal The total area is 36 times 258 meters consisting ofhallways and some classroomsWe collect 40 reference pointsevenly from the hallways region and obtain WiFi fingerprintdatabase using smartphone with the application software thatis our developed mobile software application to collect RSSand build the fingerprint map automaticallyWhen collectingthe fingerprint map the smartphones are kept at the sameheight of approximately an adultrsquos breast and sometimesrotated horizontally at the same position to face differentdirections In particular for the RPs data 20 samples at eachRP with a rate of 1 samplesecond are collected by a userwalking through the hallway area

611 Performance Metric Besides that according to [9 43]Manhattan distance performs slightly better than Euclideandistance and our workspace is a regular rectangle we usethe Manhattan distance as the standard of error analysisTheManhattan distance is an expression of geometric metricspace It is defined as the sum of the absolute differences ofvalues in a real-time measurement RSS from fingerprint asindicated by the following equation

ManDist (120574(119900)119896119904 120574(119900)

119894119895 ) =

119873

sum

119895=1

10038161003816100381610038161003816120574(119900)

119896119904minus 120574(119900)

119894119895

10038161003816100381610038161003816 (4)

where ManDist(sdotsdot) is the Manhattan distance function 120574(119900)119896119904

is the real-time measurement RSS 120574(119900)119894119895

is one of fingerprintdatabases

612 Algorithms Compared We run the following algo-rithms for comparison

(i) 119896-nearest neighbor (KNN) this is the most popularused algorithm due to its excellent tradeoff betweenaccuracy and computation complexity It obtains the 119896nearest neighbors in the online localization phase insignal space among the known fingerprint maps ldquo119896rdquois a parameter adapted to each localization system toobtain better performance

(ii) Weighted 119896-nearest neighbor (WKNN) the proce-dure is similar to the 119896-nearest neighbor The onlydifference is that the average of the coordinates is aweighted average

(iii) Fuzzy logic it is used to select which points are themost important to calculate the final coordinates ofthe current position and to assess their correspondingweight in the average As for the other algorithmsthe first step after acquiring the current value of thereceived signal strength is to determine the distancein the signal domain between the current position andall the points that make part of the fingerprint mapThe next step is to transform these distance valuesinto grades of membership that is the fuzzificationis made

International Journal of Distributed Sensor Networks 7

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP1 (24G)RSS from AP1 (5G)

(a) RSS from AP1

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50minus40

Reference points

RSS

RSS from AP2 (24G)RSS from AP2 (5G)

(b) RSS from AP2

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP3 (24G)RSS from AP3 (5G)

(c) RSS from AP3

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference pointsRS

S

RSS from AP4 (24G)RSS from AP4 (5G)

(d) RSS from AP4

0 5 10 15 20 25 30 35 40minus100minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP5 (24G)RSS from AP5 (5G)

(e) RSS from AP5

Figure 3 Received signal strength from different APs

(iv) Bayesian histogrammethod the probabilisticmethodis more complex is and based on the Bayes ruleIt is classified as the kernel method and histogrammethod generally In the kernel method a proba-bility mass is assigned to a kernel around the dataobserved the probability is then computed usinga kernel function The histogram method (used inour experiments) uses bins or value categorization tocover all measurement range according to these binswe can then calculate the probability (existence of anAP in a certain position) thus each AP will appearwith different probability and we can estimate thelocation according to the probabilities

62 The Performance of Computation Effort In the coarselocalization phase the approximate localization region isinferred by the detected APs According to the detected APs

remaining fingerprint database cannot include the detectedAPs so we do not need to match the remaining fingerprintdatabase which can reduce the computation effort heavily inthe precise localization phase And the larger the fingerprintdatabase is the more efficient it is From Figures 6 and 7 wecan find that not all the APs can be detected in each point

Supposing that every unknown point can detect 5 APsand there are 40 RPs as the number of RPs increases thealgorithm shows greater efficiency And the computationeffort is reduced dramatically while the fingerprint mapincreases as is shown in Figure 8

63 The Performance of Localization Accuracy FromSection 4 the 5G signal is more stable than 24G if bothsignals can be received by our mobile devices the 5GHzsignal should have priority to be used for localizationestimation To fair evaluate the performance of our proposed

8 International Journal of Distributed Sensor Networks

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40

minus20RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40

minus20RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP5

RP

RSS

Figure 4 Signal fluctuation at all the RPs for 24GWiFi signal

algorithm as is described in detail in Section 3 we comparethe proposed algorithm with KNN WKNN fuzzy logicand histogram algorithm and the localization error of CDF(cumulative distribution function) is showed in Figures 9and 10 It demonstrates that the cluster KNN algorithmobtains the superior localization accuracy in both 24G and5GWiFi signals

From Figure 11 regardless of 24G and 5G signal theaverage error of cluster KNN is the smallest of all algorithmsThe 24G average errors of KNN WKNN fuzzy logichistogram and cluster KNN algorithm are 45700 4384046213 45483 and 14700 respectivelyThe 5G average errorsof the above algorithms are 27475 32122 25450 37895 and11500 respectively (In this study in order to simplify theexpression of error the distance between two neighbor RPsis defined as one unit error and all the error data omit unit)

64 The Performance of Localization Stabilization In addi-tion we also analyse the variance to evaluate the localization

stabilization Besides localization accuracy the localizationstabilization is also important for localization system Inour experiment the 24G variances of KNN WKNN fuzzylogic histogram and cluster KNN algorithm are 116129115713 116833 76819 and 18560 respectively The 5Gvariances of the above algorithms are 93477 94714 9310275370 and 11500 respectively as is showed in Figure 12The smaller the variance is the more stable the localizationpresents It indicates that not only localization accuracy butalso localization stabilization is improved with cluster KNNalgorithm (In this study in order to simplify the expression ofvariance the distance between two neighbor RPs is defined asone unit error and all the variance data omit unit)

7 Conclusion

This paper focuses on improving the localization accuracystabilization and reducing the computation effort by the pro-posed localization system that consists of coarse and precise

International Journal of Distributed Sensor Networks 9

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70RSS from AP5

RP

RSS

Figure 5 Signal fluctuation at all the RPs for 5GWiFi signal

1 5 10 15 20 25 30 35 40Reference points

AP1 (24G)AP2 (24G)AP3 (24G)

AP4 (24G)AP5 (24G)

Figure 6 Detected APs from all RPs (24G)

localization In the coarse localization we use the detectedAPs to infer the coarse localization region It can eliminate the

1 5 10 15 20 25 30 35 40Reference points

AP1 (5G)AP2 (5G)AP3 (5G)

AP4 (5G)AP5 (5G)

Figure 7 Detected APs from all RPs (5G)

impossible fingerprint database to reduce the computationeffort Besides the fingerprint map is built by smartphone

10 International Journal of Distributed Sensor Networks

0 50 100 150 200 250 300 350 400 450 5000

02040608

1

The number of RPs

Effici

ency

Figure 8 Matching efficiency with the increased RPs

0 2 4 6 8 10 12 140010203040506070809

1

Error distance

CDF

KNN (24G)WKNN (24G)Fuzzy logic (24G)

Histogram (24G)Cluster KNN (24G)

Figure 9Distribution of the localization error for 24GWiFi signal

0 2 4 6 8 10 12 14 16 18 200

010203040506070809

1

Error distance

CDF

KNN (5G)WKNN (5G)Fuzzy logic (5G)

Histogram (5G)Cluster KNN (5G)

Figure 10 Distribution of the localization error for 5GWiFi signal

with our developed software application automatically Itimproved efficiency of system operation enormously In theprecise localization we propose two approaches to improvethe localization accuracy and stabilization By analysing thechange rule of WiFi signal about plusmn5 dBm of fluctuation isin the 24G and 5G signal so the real-time measurementRSS is not so accurate as localization estimation The real-time measurement RSS is firstly expanded by plusmn5 dBm andthen used for thematching with the fingerprint map In orderto select the optimal nearest neighbor points we use therelationship of RPs to eliminate the discrete nearest neigh-bor points Experimental results indicate that the proposed

005

115

225

335

445

5

Different algorithms

Aver

age e

rror

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 11 Localization mean error for 24G and 5GWiFi signal

0

2

4

6

8

10

12

Different algorithms

Varia

nce

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 12 Variance of localization error for 24G and 5G WiFisignal

algorithm is the most accurate and stable of all comparativealgorithms In addition our experiment also demonstratesthat the 5GWiFi signal is more stable for indoor localization

In the future we intend to perform this system to applyit in larger indoor construction as well as to investigatingthe performance of the proposed algorithm in more complexenvironment and the multilayered buildings

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This work was supported by the National Natural ScienceFoundation of China under Grant Project nos 61232004 and61103085

International Journal of Distributed Sensor Networks 11

References

[1] B Hofmann-Wellenhof H Lichtenegger and J Collins ldquoGlobalpositioning system theory and practicerdquo in Global PositioningSystem Theory and Practice B Hofmann-Wellenhof H Licht-enegger and J Collins Eds vol 1 p 387 Springer ViennaAustria 1993

[2] N Bulusu J Heidemann and D Estrin ldquoGPS-less low-costoutdoor localization for very small devicesrdquo IEEE PersonalCommunications vol 7 no 5 pp 28ndash34 2000

[3] C Lee Y Chang G Park et al ldquoIndoor positioning systembased on incident angles of infrared emittersrdquo in Proceedings ofthe 30thAnnual Conference of IEEE Industrial Electronics Society(IECON rsquo04) vol 3 pp 2218ndash2222 Busan Republic of KoreaNovember 2004

[4] A Shahi A Aryan J S West C T Haas and R C G HaasldquoDeterioration of UWB positioning during constructionrdquoAutomation in Construction vol 24 pp 72ndash80 2012

[5] V Filonenko C Cullen and J Carswell ldquoInvestigating ultra-sonic positioning on mobile phonesrdquo in Proceedings of theInternational Conference on Indoor Positioning and IndoorNavigation (IPIN rsquo10) pp 1ndash8 IEEE September 2010

[6] S Li Y Lou and B Liu ldquoBluetooth aided mobile phone local-ization a nonlinear neural circuit approachrdquoACMTransactionson Embedded Computing Systems vol 13 no 4 p 78 2014

[7] N Li and B Becerik-Gerber ldquoPerformance-based evaluationof RFID-based indoor location sensing solutions for the builtenvironmentrdquo Advanced Engineering Informatics vol 25 no 3pp 535ndash546 2011

[8] L Luoh ldquoZigBee-based intelligent indoor positioning systemsoft computingrdquo Soft Computing vol 18 no 3 pp 443ndash4562014

[9] Y Chen D Lymberopoulos J Liu and B Priyantha ldquoIndoorlocalization using FM signalsrdquo IEEE Transactions on MobileComputing vol 12 no 8 pp 1502ndash1517 2013

[10] V Moghtadaiee and A G Dempster ldquoIndoor location finger-printing using fm radio signalsrdquo IEEE Transactions on Broad-casting vol 60 no 2 pp 336ndash346 2014

[11] J Chung M Donahoe C Schmandt I-J Kim P Razavai andM Wiseman ldquoIndoor location sensing using geo-magnetismrdquoin Proceedings of the 9th International Conference on MobileSystems Applications and Services (MobiSys rsquo11) pp 141ndash154July 2011

[12] I Bisio M Cerruti F Lavagetto et al ldquoA trainingless WiFifingerprint positioning approach over mobile devicesrdquo IEEEAntennas and Wireless Propagation Letters vol 13 pp 832ndash8352014

[13] N Alsindi Z Chaloupka N AlKhanbashi and J Aweya ldquoAnempirical evaluation of a probabilistic rf signature for wlanlocation fingerprintingrdquo IEEETransactions onWireless Commu-nications vol 13 no 6 pp 3257ndash3268 2014

[14] CWu Z Yang and Y Liu ldquoSmartphones based crowdsourcingfor indoor localizationrdquo 2014

[15] ADanalet B Farooq andM Bierlaire ldquoABayesian approach todetect pedestrian destination-sequences fromWiFi signaturesrdquoTransportation Research Part C Emerging Technologies vol 44pp 146ndash170 2014

[16] R S Campos L Lovisolo and M L R De Campos ldquoWi-Fimulti-floor indoor positioning considering architectural aspectsand controlled computational complexityrdquo Expert Systems withApplications vol 41 no 14 pp 6211ndash6223 2014

[17] M Heidari and K Pahlavan ldquoA new statistical model for thebehavior of ranging errors in TOA-based indoor localiza-tionrdquo in Proceedings of the IEEE Wireless Communications andNetworking Conference (WCNC rsquo07) pp 2564ndash2569 IEEEKowloon Hong Kong March 2007

[18] P-H Tseng K-T Feng Y-C Lin and C-L Chen ldquoWirelesslocation tracking algorithms for environments with insufficientsignal sourcesrdquo IEEE Transactions on Mobile Computing vol 8no 12 pp 1676ndash1689 2009

[19] M Bocquet C Loyez andA Benlarbi-Delaı ldquoUsing enhanced-TDOA measurement for indoor positioningrdquo IEEE Microwaveand Wireless Components Letters vol 15 no 10 pp 612ndash6142005

[20] C-H Lim B P Ng and D Da ldquoRobust methods for AOA geo-location in a real-time indoorWiFi systemrdquo Journal of LocationBased Services vol 2 no 2 pp 112ndash121 2008

[21] K Wu J Xiao Y Yi D Chen X Luo and L M Ni ldquoCSI-basedindoor localizationrdquo IEEE Transactions on Parallel and Dis-tributed Systems vol 24 no 7 pp 1300ndash1309 2013

[22] Z Yang Z Zhou and Y Liu ldquoFrom RSSI to CSI indoorlocalization via channel responserdquo ACM Computing Surveysvol 46 no 2 article 25 2013

[23] A Jaffe and M Wax ldquoSingle-site localization via maximumdiscrimination multipath fingerprintingrdquo IEEE Transactions onSignal Processing vol 62 no 7 pp 1718ndash1728 2014

[24] C Feng W S A Au S Valaee and Z Tan ldquoReceived-signal-strength-based indoor positioning using compressive sensingrdquoIEEETransactions onMobile Computing vol 11 no 12 pp 1983ndash1993 2012

[25] M Ficco C Esposito and A Napolitano ldquoCalibrating indoorpositioning systemswith low effortsrdquo IEEE Transactions onMobile Computing vol 13 no 4 pp 737ndash751 2014

[26] F Yu M H Jiang J Liang et al ldquoAn improved indoor local-ization of wifi based on support vector machinesrdquo InternationalJournal of Future Generation Communication and Networkingvol 7 pp 191ndash206 2014

[27] S Shekhar and H Xiong ldquoIndoor positioning systemrdquo inEncyclopedia of GIS p 566 Springer NewYork NY USA 2008

[28] H Liu H Darabi P Banerjee and J Liu ldquoSurvey of wirelessindoor positioning techniques and systemsrdquo IEEE Transactionson Systems Man and Cybernetics Part C Applications andReviews vol 37 no 6 pp 1067ndash1080 2007

[29] M Molina-Garcıa J Calle-Sanchez J I Alonso A Fernandez-Duran and F B Barba ldquoEnhanced in-building fingerprint posi-tioning using femtocell networksrdquo Bell Labs Technical Journalvol 18 no 2 pp 195ndash211 2013

[30] A Kushki K N Plataniotis and A N VenetsanopoulosldquoKernel-based positioning in wireless local area networksrdquoIEEE Transactions on Mobile Computing vol 6 no 6 pp 689ndash705 2007

[31] T King T Haenselmann andW Effelsberg ldquoDeployment cal-ibration and measurement factors for position errors in 80211-based indoor positioning systemsrdquo in Location-and Context-Awareness pp 17ndash34 Springer 2007

[32] X Chai and Q Yang ldquoReducing the calibration effort forprobabilistic indoor location estimationrdquo IEEE Transactions onMobile Computing vol 6 no 6 pp 649ndash662 2007

[33] C Steiner and A Wittneben ldquoEfficient training phase forultrawideband-based location fingerprinting systemsrdquo IEEETransactions on Signal Processing vol 59 no 12 pp 6021ndash60322011

12 International Journal of Distributed Sensor Networks

[34] Z Yang C Wu and Y Liu ldquoLocating in fingerprint spacewireless indoor localization with little human interventionrdquoin Proceedings of the 18th Annual International Conference onMobile Computing and Networking pp 269ndash280 August 2012

[35] Y Chon N D Lane F Li H Cha and F Zhao ldquoAutomaticallycharacterizing places with opportunistic crowdsensing usingsmartphonesrdquo in Proceedings of the 14th International Confer-ence on Ubiquitous Computing (UbiComp rsquo12) pp 481ndash490Pittsburgh Pa USA September 2012

[36] P Torteeka and X Chundi ldquoIndoor positioning based on Wi-Fi fingerprint technique using fuzzy K-nearest neighborrdquo inProceedings of the 11th International Bhurban Conference onApplied Sciences and Technology (IBCAST rsquo14) pp 461ndash465Islamabad Pakistan January 2014

[37] B Shin J H Lee T Lee and H S Kim ldquoEnhanced weightedK-nearest neighbor algorithm for indoor Wi-Fi positioningsystemsrdquo in Proceedings of the 8th International Conference onComputing Technology and Information Management (ICCMrsquo12) pp 574ndash577 April 2012

[38] D Madigan E Einahrawy R P Martin W-H Ju P Krishnanand A Krishnakumar ldquoBayesian indoor positioning systemsrdquoin Proceedings of the 24th Annual Joint Conference of the IEEEComputer and Communications Societies (INFOCOM rsquo05) vol2 pp 1217ndash1227 IEEE 2005

[39] M Zhou Y Xu and L Tang ldquoMultilayer ANN indoor locationsystem with area division in WLAN environmentrdquo Journal ofSystems Engineering and Electronics vol 21 no 5 pp 914ndash9262010

[40] Z-L Wu C-H Li J K-Y Ng and K R P H Leung ldquoLocationestimation via support vector regressionrdquo IEEE Transactions onMobile Computing vol 6 no 3 pp 311ndash321 2007

[41] F Yu M H Jiang J Liang et al ldquoAn indoor localization ofwifi based on support vector machinesrdquo in Advanced MaterialsResearch vol 926 pp 2438ndash2441 Trans Tech Publications 2014

[42] O Baala Y Zheng and A Caminada ldquoThe impact of APplacement in WLAN-based indoor positioning systemrdquo inProceedings of the 8th International Conference on Networks(ICN rsquo09) pp 12ndash17 Gosier France March 2009

[43] A Farshad J Li M K Marina and F J Garcia ldquoA microscopiclook at wifi fingerprinting for indoor mobile phone localizationin diverse environmentsrdquo in Proceedings of the InternationalConference on Indoor Positioning and Indoor Navigation (IPINrsquo13) pp 1ndash10 IEEE 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

International Journal of Distributed Sensor Networks 7

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP1 (24G)RSS from AP1 (5G)

(a) RSS from AP1

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50minus40

Reference points

RSS

RSS from AP2 (24G)RSS from AP2 (5G)

(b) RSS from AP2

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP3 (24G)RSS from AP3 (5G)

(c) RSS from AP3

0 5 10 15 20 25 30 35 40minus100

minus90minus80minus70minus60minus50

Reference pointsRS

S

RSS from AP4 (24G)RSS from AP4 (5G)

(d) RSS from AP4

0 5 10 15 20 25 30 35 40minus100minus90minus80minus70minus60minus50

Reference points

RSS

RSS from AP5 (24G)RSS from AP5 (5G)

(e) RSS from AP5

Figure 3 Received signal strength from different APs

(iv) Bayesian histogrammethod the probabilisticmethodis more complex is and based on the Bayes ruleIt is classified as the kernel method and histogrammethod generally In the kernel method a proba-bility mass is assigned to a kernel around the dataobserved the probability is then computed usinga kernel function The histogram method (used inour experiments) uses bins or value categorization tocover all measurement range according to these binswe can then calculate the probability (existence of anAP in a certain position) thus each AP will appearwith different probability and we can estimate thelocation according to the probabilities

62 The Performance of Computation Effort In the coarselocalization phase the approximate localization region isinferred by the detected APs According to the detected APs

remaining fingerprint database cannot include the detectedAPs so we do not need to match the remaining fingerprintdatabase which can reduce the computation effort heavily inthe precise localization phase And the larger the fingerprintdatabase is the more efficient it is From Figures 6 and 7 wecan find that not all the APs can be detected in each point

Supposing that every unknown point can detect 5 APsand there are 40 RPs as the number of RPs increases thealgorithm shows greater efficiency And the computationeffort is reduced dramatically while the fingerprint mapincreases as is shown in Figure 8

63 The Performance of Localization Accuracy FromSection 4 the 5G signal is more stable than 24G if bothsignals can be received by our mobile devices the 5GHzsignal should have priority to be used for localizationestimation To fair evaluate the performance of our proposed

8 International Journal of Distributed Sensor Networks

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40

minus20RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40

minus20RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP5

RP

RSS

Figure 4 Signal fluctuation at all the RPs for 24GWiFi signal

algorithm as is described in detail in Section 3 we comparethe proposed algorithm with KNN WKNN fuzzy logicand histogram algorithm and the localization error of CDF(cumulative distribution function) is showed in Figures 9and 10 It demonstrates that the cluster KNN algorithmobtains the superior localization accuracy in both 24G and5GWiFi signals

From Figure 11 regardless of 24G and 5G signal theaverage error of cluster KNN is the smallest of all algorithmsThe 24G average errors of KNN WKNN fuzzy logichistogram and cluster KNN algorithm are 45700 4384046213 45483 and 14700 respectivelyThe 5G average errorsof the above algorithms are 27475 32122 25450 37895 and11500 respectively (In this study in order to simplify theexpression of error the distance between two neighbor RPsis defined as one unit error and all the error data omit unit)

64 The Performance of Localization Stabilization In addi-tion we also analyse the variance to evaluate the localization

stabilization Besides localization accuracy the localizationstabilization is also important for localization system Inour experiment the 24G variances of KNN WKNN fuzzylogic histogram and cluster KNN algorithm are 116129115713 116833 76819 and 18560 respectively The 5Gvariances of the above algorithms are 93477 94714 9310275370 and 11500 respectively as is showed in Figure 12The smaller the variance is the more stable the localizationpresents It indicates that not only localization accuracy butalso localization stabilization is improved with cluster KNNalgorithm (In this study in order to simplify the expression ofvariance the distance between two neighbor RPs is defined asone unit error and all the variance data omit unit)

7 Conclusion

This paper focuses on improving the localization accuracystabilization and reducing the computation effort by the pro-posed localization system that consists of coarse and precise

International Journal of Distributed Sensor Networks 9

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70RSS from AP5

RP

RSS

Figure 5 Signal fluctuation at all the RPs for 5GWiFi signal

1 5 10 15 20 25 30 35 40Reference points

AP1 (24G)AP2 (24G)AP3 (24G)

AP4 (24G)AP5 (24G)

Figure 6 Detected APs from all RPs (24G)

localization In the coarse localization we use the detectedAPs to infer the coarse localization region It can eliminate the

1 5 10 15 20 25 30 35 40Reference points

AP1 (5G)AP2 (5G)AP3 (5G)

AP4 (5G)AP5 (5G)

Figure 7 Detected APs from all RPs (5G)

impossible fingerprint database to reduce the computationeffort Besides the fingerprint map is built by smartphone

10 International Journal of Distributed Sensor Networks

0 50 100 150 200 250 300 350 400 450 5000

02040608

1

The number of RPs

Effici

ency

Figure 8 Matching efficiency with the increased RPs

0 2 4 6 8 10 12 140010203040506070809

1

Error distance

CDF

KNN (24G)WKNN (24G)Fuzzy logic (24G)

Histogram (24G)Cluster KNN (24G)

Figure 9Distribution of the localization error for 24GWiFi signal

0 2 4 6 8 10 12 14 16 18 200

010203040506070809

1

Error distance

CDF

KNN (5G)WKNN (5G)Fuzzy logic (5G)

Histogram (5G)Cluster KNN (5G)

Figure 10 Distribution of the localization error for 5GWiFi signal

with our developed software application automatically Itimproved efficiency of system operation enormously In theprecise localization we propose two approaches to improvethe localization accuracy and stabilization By analysing thechange rule of WiFi signal about plusmn5 dBm of fluctuation isin the 24G and 5G signal so the real-time measurementRSS is not so accurate as localization estimation The real-time measurement RSS is firstly expanded by plusmn5 dBm andthen used for thematching with the fingerprint map In orderto select the optimal nearest neighbor points we use therelationship of RPs to eliminate the discrete nearest neigh-bor points Experimental results indicate that the proposed

005

115

225

335

445

5

Different algorithms

Aver

age e

rror

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 11 Localization mean error for 24G and 5GWiFi signal

0

2

4

6

8

10

12

Different algorithms

Varia

nce

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 12 Variance of localization error for 24G and 5G WiFisignal

algorithm is the most accurate and stable of all comparativealgorithms In addition our experiment also demonstratesthat the 5GWiFi signal is more stable for indoor localization

In the future we intend to perform this system to applyit in larger indoor construction as well as to investigatingthe performance of the proposed algorithm in more complexenvironment and the multilayered buildings

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This work was supported by the National Natural ScienceFoundation of China under Grant Project nos 61232004 and61103085

International Journal of Distributed Sensor Networks 11

References

[1] B Hofmann-Wellenhof H Lichtenegger and J Collins ldquoGlobalpositioning system theory and practicerdquo in Global PositioningSystem Theory and Practice B Hofmann-Wellenhof H Licht-enegger and J Collins Eds vol 1 p 387 Springer ViennaAustria 1993

[2] N Bulusu J Heidemann and D Estrin ldquoGPS-less low-costoutdoor localization for very small devicesrdquo IEEE PersonalCommunications vol 7 no 5 pp 28ndash34 2000

[3] C Lee Y Chang G Park et al ldquoIndoor positioning systembased on incident angles of infrared emittersrdquo in Proceedings ofthe 30thAnnual Conference of IEEE Industrial Electronics Society(IECON rsquo04) vol 3 pp 2218ndash2222 Busan Republic of KoreaNovember 2004

[4] A Shahi A Aryan J S West C T Haas and R C G HaasldquoDeterioration of UWB positioning during constructionrdquoAutomation in Construction vol 24 pp 72ndash80 2012

[5] V Filonenko C Cullen and J Carswell ldquoInvestigating ultra-sonic positioning on mobile phonesrdquo in Proceedings of theInternational Conference on Indoor Positioning and IndoorNavigation (IPIN rsquo10) pp 1ndash8 IEEE September 2010

[6] S Li Y Lou and B Liu ldquoBluetooth aided mobile phone local-ization a nonlinear neural circuit approachrdquoACMTransactionson Embedded Computing Systems vol 13 no 4 p 78 2014

[7] N Li and B Becerik-Gerber ldquoPerformance-based evaluationof RFID-based indoor location sensing solutions for the builtenvironmentrdquo Advanced Engineering Informatics vol 25 no 3pp 535ndash546 2011

[8] L Luoh ldquoZigBee-based intelligent indoor positioning systemsoft computingrdquo Soft Computing vol 18 no 3 pp 443ndash4562014

[9] Y Chen D Lymberopoulos J Liu and B Priyantha ldquoIndoorlocalization using FM signalsrdquo IEEE Transactions on MobileComputing vol 12 no 8 pp 1502ndash1517 2013

[10] V Moghtadaiee and A G Dempster ldquoIndoor location finger-printing using fm radio signalsrdquo IEEE Transactions on Broad-casting vol 60 no 2 pp 336ndash346 2014

[11] J Chung M Donahoe C Schmandt I-J Kim P Razavai andM Wiseman ldquoIndoor location sensing using geo-magnetismrdquoin Proceedings of the 9th International Conference on MobileSystems Applications and Services (MobiSys rsquo11) pp 141ndash154July 2011

[12] I Bisio M Cerruti F Lavagetto et al ldquoA trainingless WiFifingerprint positioning approach over mobile devicesrdquo IEEEAntennas and Wireless Propagation Letters vol 13 pp 832ndash8352014

[13] N Alsindi Z Chaloupka N AlKhanbashi and J Aweya ldquoAnempirical evaluation of a probabilistic rf signature for wlanlocation fingerprintingrdquo IEEETransactions onWireless Commu-nications vol 13 no 6 pp 3257ndash3268 2014

[14] CWu Z Yang and Y Liu ldquoSmartphones based crowdsourcingfor indoor localizationrdquo 2014

[15] ADanalet B Farooq andM Bierlaire ldquoABayesian approach todetect pedestrian destination-sequences fromWiFi signaturesrdquoTransportation Research Part C Emerging Technologies vol 44pp 146ndash170 2014

[16] R S Campos L Lovisolo and M L R De Campos ldquoWi-Fimulti-floor indoor positioning considering architectural aspectsand controlled computational complexityrdquo Expert Systems withApplications vol 41 no 14 pp 6211ndash6223 2014

[17] M Heidari and K Pahlavan ldquoA new statistical model for thebehavior of ranging errors in TOA-based indoor localiza-tionrdquo in Proceedings of the IEEE Wireless Communications andNetworking Conference (WCNC rsquo07) pp 2564ndash2569 IEEEKowloon Hong Kong March 2007

[18] P-H Tseng K-T Feng Y-C Lin and C-L Chen ldquoWirelesslocation tracking algorithms for environments with insufficientsignal sourcesrdquo IEEE Transactions on Mobile Computing vol 8no 12 pp 1676ndash1689 2009

[19] M Bocquet C Loyez andA Benlarbi-Delaı ldquoUsing enhanced-TDOA measurement for indoor positioningrdquo IEEE Microwaveand Wireless Components Letters vol 15 no 10 pp 612ndash6142005

[20] C-H Lim B P Ng and D Da ldquoRobust methods for AOA geo-location in a real-time indoorWiFi systemrdquo Journal of LocationBased Services vol 2 no 2 pp 112ndash121 2008

[21] K Wu J Xiao Y Yi D Chen X Luo and L M Ni ldquoCSI-basedindoor localizationrdquo IEEE Transactions on Parallel and Dis-tributed Systems vol 24 no 7 pp 1300ndash1309 2013

[22] Z Yang Z Zhou and Y Liu ldquoFrom RSSI to CSI indoorlocalization via channel responserdquo ACM Computing Surveysvol 46 no 2 article 25 2013

[23] A Jaffe and M Wax ldquoSingle-site localization via maximumdiscrimination multipath fingerprintingrdquo IEEE Transactions onSignal Processing vol 62 no 7 pp 1718ndash1728 2014

[24] C Feng W S A Au S Valaee and Z Tan ldquoReceived-signal-strength-based indoor positioning using compressive sensingrdquoIEEETransactions onMobile Computing vol 11 no 12 pp 1983ndash1993 2012

[25] M Ficco C Esposito and A Napolitano ldquoCalibrating indoorpositioning systemswith low effortsrdquo IEEE Transactions onMobile Computing vol 13 no 4 pp 737ndash751 2014

[26] F Yu M H Jiang J Liang et al ldquoAn improved indoor local-ization of wifi based on support vector machinesrdquo InternationalJournal of Future Generation Communication and Networkingvol 7 pp 191ndash206 2014

[27] S Shekhar and H Xiong ldquoIndoor positioning systemrdquo inEncyclopedia of GIS p 566 Springer NewYork NY USA 2008

[28] H Liu H Darabi P Banerjee and J Liu ldquoSurvey of wirelessindoor positioning techniques and systemsrdquo IEEE Transactionson Systems Man and Cybernetics Part C Applications andReviews vol 37 no 6 pp 1067ndash1080 2007

[29] M Molina-Garcıa J Calle-Sanchez J I Alonso A Fernandez-Duran and F B Barba ldquoEnhanced in-building fingerprint posi-tioning using femtocell networksrdquo Bell Labs Technical Journalvol 18 no 2 pp 195ndash211 2013

[30] A Kushki K N Plataniotis and A N VenetsanopoulosldquoKernel-based positioning in wireless local area networksrdquoIEEE Transactions on Mobile Computing vol 6 no 6 pp 689ndash705 2007

[31] T King T Haenselmann andW Effelsberg ldquoDeployment cal-ibration and measurement factors for position errors in 80211-based indoor positioning systemsrdquo in Location-and Context-Awareness pp 17ndash34 Springer 2007

[32] X Chai and Q Yang ldquoReducing the calibration effort forprobabilistic indoor location estimationrdquo IEEE Transactions onMobile Computing vol 6 no 6 pp 649ndash662 2007

[33] C Steiner and A Wittneben ldquoEfficient training phase forultrawideband-based location fingerprinting systemsrdquo IEEETransactions on Signal Processing vol 59 no 12 pp 6021ndash60322011

12 International Journal of Distributed Sensor Networks

[34] Z Yang C Wu and Y Liu ldquoLocating in fingerprint spacewireless indoor localization with little human interventionrdquoin Proceedings of the 18th Annual International Conference onMobile Computing and Networking pp 269ndash280 August 2012

[35] Y Chon N D Lane F Li H Cha and F Zhao ldquoAutomaticallycharacterizing places with opportunistic crowdsensing usingsmartphonesrdquo in Proceedings of the 14th International Confer-ence on Ubiquitous Computing (UbiComp rsquo12) pp 481ndash490Pittsburgh Pa USA September 2012

[36] P Torteeka and X Chundi ldquoIndoor positioning based on Wi-Fi fingerprint technique using fuzzy K-nearest neighborrdquo inProceedings of the 11th International Bhurban Conference onApplied Sciences and Technology (IBCAST rsquo14) pp 461ndash465Islamabad Pakistan January 2014

[37] B Shin J H Lee T Lee and H S Kim ldquoEnhanced weightedK-nearest neighbor algorithm for indoor Wi-Fi positioningsystemsrdquo in Proceedings of the 8th International Conference onComputing Technology and Information Management (ICCMrsquo12) pp 574ndash577 April 2012

[38] D Madigan E Einahrawy R P Martin W-H Ju P Krishnanand A Krishnakumar ldquoBayesian indoor positioning systemsrdquoin Proceedings of the 24th Annual Joint Conference of the IEEEComputer and Communications Societies (INFOCOM rsquo05) vol2 pp 1217ndash1227 IEEE 2005

[39] M Zhou Y Xu and L Tang ldquoMultilayer ANN indoor locationsystem with area division in WLAN environmentrdquo Journal ofSystems Engineering and Electronics vol 21 no 5 pp 914ndash9262010

[40] Z-L Wu C-H Li J K-Y Ng and K R P H Leung ldquoLocationestimation via support vector regressionrdquo IEEE Transactions onMobile Computing vol 6 no 3 pp 311ndash321 2007

[41] F Yu M H Jiang J Liang et al ldquoAn indoor localization ofwifi based on support vector machinesrdquo in Advanced MaterialsResearch vol 926 pp 2438ndash2441 Trans Tech Publications 2014

[42] O Baala Y Zheng and A Caminada ldquoThe impact of APplacement in WLAN-based indoor positioning systemrdquo inProceedings of the 8th International Conference on Networks(ICN rsquo09) pp 12ndash17 Gosier France March 2009

[43] A Farshad J Li M K Marina and F J Garcia ldquoA microscopiclook at wifi fingerprinting for indoor mobile phone localizationin diverse environmentsrdquo in Proceedings of the InternationalConference on Indoor Positioning and Indoor Navigation (IPINrsquo13) pp 1ndash10 IEEE 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

8 International Journal of Distributed Sensor Networks

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40

minus20RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40

minus20RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP5

RP

RSS

Figure 4 Signal fluctuation at all the RPs for 24GWiFi signal

algorithm as is described in detail in Section 3 we comparethe proposed algorithm with KNN WKNN fuzzy logicand histogram algorithm and the localization error of CDF(cumulative distribution function) is showed in Figures 9and 10 It demonstrates that the cluster KNN algorithmobtains the superior localization accuracy in both 24G and5GWiFi signals

From Figure 11 regardless of 24G and 5G signal theaverage error of cluster KNN is the smallest of all algorithmsThe 24G average errors of KNN WKNN fuzzy logichistogram and cluster KNN algorithm are 45700 4384046213 45483 and 14700 respectivelyThe 5G average errorsof the above algorithms are 27475 32122 25450 37895 and11500 respectively (In this study in order to simplify theexpression of error the distance between two neighbor RPsis defined as one unit error and all the error data omit unit)

64 The Performance of Localization Stabilization In addi-tion we also analyse the variance to evaluate the localization

stabilization Besides localization accuracy the localizationstabilization is also important for localization system Inour experiment the 24G variances of KNN WKNN fuzzylogic histogram and cluster KNN algorithm are 116129115713 116833 76819 and 18560 respectively The 5Gvariances of the above algorithms are 93477 94714 9310275370 and 11500 respectively as is showed in Figure 12The smaller the variance is the more stable the localizationpresents It indicates that not only localization accuracy butalso localization stabilization is improved with cluster KNNalgorithm (In this study in order to simplify the expression ofvariance the distance between two neighbor RPs is defined asone unit error and all the variance data omit unit)

7 Conclusion

This paper focuses on improving the localization accuracystabilization and reducing the computation effort by the pro-posed localization system that consists of coarse and precise

International Journal of Distributed Sensor Networks 9

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70RSS from AP5

RP

RSS

Figure 5 Signal fluctuation at all the RPs for 5GWiFi signal

1 5 10 15 20 25 30 35 40Reference points

AP1 (24G)AP2 (24G)AP3 (24G)

AP4 (24G)AP5 (24G)

Figure 6 Detected APs from all RPs (24G)

localization In the coarse localization we use the detectedAPs to infer the coarse localization region It can eliminate the

1 5 10 15 20 25 30 35 40Reference points

AP1 (5G)AP2 (5G)AP3 (5G)

AP4 (5G)AP5 (5G)

Figure 7 Detected APs from all RPs (5G)

impossible fingerprint database to reduce the computationeffort Besides the fingerprint map is built by smartphone

10 International Journal of Distributed Sensor Networks

0 50 100 150 200 250 300 350 400 450 5000

02040608

1

The number of RPs

Effici

ency

Figure 8 Matching efficiency with the increased RPs

0 2 4 6 8 10 12 140010203040506070809

1

Error distance

CDF

KNN (24G)WKNN (24G)Fuzzy logic (24G)

Histogram (24G)Cluster KNN (24G)

Figure 9Distribution of the localization error for 24GWiFi signal

0 2 4 6 8 10 12 14 16 18 200

010203040506070809

1

Error distance

CDF

KNN (5G)WKNN (5G)Fuzzy logic (5G)

Histogram (5G)Cluster KNN (5G)

Figure 10 Distribution of the localization error for 5GWiFi signal

with our developed software application automatically Itimproved efficiency of system operation enormously In theprecise localization we propose two approaches to improvethe localization accuracy and stabilization By analysing thechange rule of WiFi signal about plusmn5 dBm of fluctuation isin the 24G and 5G signal so the real-time measurementRSS is not so accurate as localization estimation The real-time measurement RSS is firstly expanded by plusmn5 dBm andthen used for thematching with the fingerprint map In orderto select the optimal nearest neighbor points we use therelationship of RPs to eliminate the discrete nearest neigh-bor points Experimental results indicate that the proposed

005

115

225

335

445

5

Different algorithms

Aver

age e

rror

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 11 Localization mean error for 24G and 5GWiFi signal

0

2

4

6

8

10

12

Different algorithms

Varia

nce

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 12 Variance of localization error for 24G and 5G WiFisignal

algorithm is the most accurate and stable of all comparativealgorithms In addition our experiment also demonstratesthat the 5GWiFi signal is more stable for indoor localization

In the future we intend to perform this system to applyit in larger indoor construction as well as to investigatingthe performance of the proposed algorithm in more complexenvironment and the multilayered buildings

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This work was supported by the National Natural ScienceFoundation of China under Grant Project nos 61232004 and61103085

International Journal of Distributed Sensor Networks 11

References

[1] B Hofmann-Wellenhof H Lichtenegger and J Collins ldquoGlobalpositioning system theory and practicerdquo in Global PositioningSystem Theory and Practice B Hofmann-Wellenhof H Licht-enegger and J Collins Eds vol 1 p 387 Springer ViennaAustria 1993

[2] N Bulusu J Heidemann and D Estrin ldquoGPS-less low-costoutdoor localization for very small devicesrdquo IEEE PersonalCommunications vol 7 no 5 pp 28ndash34 2000

[3] C Lee Y Chang G Park et al ldquoIndoor positioning systembased on incident angles of infrared emittersrdquo in Proceedings ofthe 30thAnnual Conference of IEEE Industrial Electronics Society(IECON rsquo04) vol 3 pp 2218ndash2222 Busan Republic of KoreaNovember 2004

[4] A Shahi A Aryan J S West C T Haas and R C G HaasldquoDeterioration of UWB positioning during constructionrdquoAutomation in Construction vol 24 pp 72ndash80 2012

[5] V Filonenko C Cullen and J Carswell ldquoInvestigating ultra-sonic positioning on mobile phonesrdquo in Proceedings of theInternational Conference on Indoor Positioning and IndoorNavigation (IPIN rsquo10) pp 1ndash8 IEEE September 2010

[6] S Li Y Lou and B Liu ldquoBluetooth aided mobile phone local-ization a nonlinear neural circuit approachrdquoACMTransactionson Embedded Computing Systems vol 13 no 4 p 78 2014

[7] N Li and B Becerik-Gerber ldquoPerformance-based evaluationof RFID-based indoor location sensing solutions for the builtenvironmentrdquo Advanced Engineering Informatics vol 25 no 3pp 535ndash546 2011

[8] L Luoh ldquoZigBee-based intelligent indoor positioning systemsoft computingrdquo Soft Computing vol 18 no 3 pp 443ndash4562014

[9] Y Chen D Lymberopoulos J Liu and B Priyantha ldquoIndoorlocalization using FM signalsrdquo IEEE Transactions on MobileComputing vol 12 no 8 pp 1502ndash1517 2013

[10] V Moghtadaiee and A G Dempster ldquoIndoor location finger-printing using fm radio signalsrdquo IEEE Transactions on Broad-casting vol 60 no 2 pp 336ndash346 2014

[11] J Chung M Donahoe C Schmandt I-J Kim P Razavai andM Wiseman ldquoIndoor location sensing using geo-magnetismrdquoin Proceedings of the 9th International Conference on MobileSystems Applications and Services (MobiSys rsquo11) pp 141ndash154July 2011

[12] I Bisio M Cerruti F Lavagetto et al ldquoA trainingless WiFifingerprint positioning approach over mobile devicesrdquo IEEEAntennas and Wireless Propagation Letters vol 13 pp 832ndash8352014

[13] N Alsindi Z Chaloupka N AlKhanbashi and J Aweya ldquoAnempirical evaluation of a probabilistic rf signature for wlanlocation fingerprintingrdquo IEEETransactions onWireless Commu-nications vol 13 no 6 pp 3257ndash3268 2014

[14] CWu Z Yang and Y Liu ldquoSmartphones based crowdsourcingfor indoor localizationrdquo 2014

[15] ADanalet B Farooq andM Bierlaire ldquoABayesian approach todetect pedestrian destination-sequences fromWiFi signaturesrdquoTransportation Research Part C Emerging Technologies vol 44pp 146ndash170 2014

[16] R S Campos L Lovisolo and M L R De Campos ldquoWi-Fimulti-floor indoor positioning considering architectural aspectsand controlled computational complexityrdquo Expert Systems withApplications vol 41 no 14 pp 6211ndash6223 2014

[17] M Heidari and K Pahlavan ldquoA new statistical model for thebehavior of ranging errors in TOA-based indoor localiza-tionrdquo in Proceedings of the IEEE Wireless Communications andNetworking Conference (WCNC rsquo07) pp 2564ndash2569 IEEEKowloon Hong Kong March 2007

[18] P-H Tseng K-T Feng Y-C Lin and C-L Chen ldquoWirelesslocation tracking algorithms for environments with insufficientsignal sourcesrdquo IEEE Transactions on Mobile Computing vol 8no 12 pp 1676ndash1689 2009

[19] M Bocquet C Loyez andA Benlarbi-Delaı ldquoUsing enhanced-TDOA measurement for indoor positioningrdquo IEEE Microwaveand Wireless Components Letters vol 15 no 10 pp 612ndash6142005

[20] C-H Lim B P Ng and D Da ldquoRobust methods for AOA geo-location in a real-time indoorWiFi systemrdquo Journal of LocationBased Services vol 2 no 2 pp 112ndash121 2008

[21] K Wu J Xiao Y Yi D Chen X Luo and L M Ni ldquoCSI-basedindoor localizationrdquo IEEE Transactions on Parallel and Dis-tributed Systems vol 24 no 7 pp 1300ndash1309 2013

[22] Z Yang Z Zhou and Y Liu ldquoFrom RSSI to CSI indoorlocalization via channel responserdquo ACM Computing Surveysvol 46 no 2 article 25 2013

[23] A Jaffe and M Wax ldquoSingle-site localization via maximumdiscrimination multipath fingerprintingrdquo IEEE Transactions onSignal Processing vol 62 no 7 pp 1718ndash1728 2014

[24] C Feng W S A Au S Valaee and Z Tan ldquoReceived-signal-strength-based indoor positioning using compressive sensingrdquoIEEETransactions onMobile Computing vol 11 no 12 pp 1983ndash1993 2012

[25] M Ficco C Esposito and A Napolitano ldquoCalibrating indoorpositioning systemswith low effortsrdquo IEEE Transactions onMobile Computing vol 13 no 4 pp 737ndash751 2014

[26] F Yu M H Jiang J Liang et al ldquoAn improved indoor local-ization of wifi based on support vector machinesrdquo InternationalJournal of Future Generation Communication and Networkingvol 7 pp 191ndash206 2014

[27] S Shekhar and H Xiong ldquoIndoor positioning systemrdquo inEncyclopedia of GIS p 566 Springer NewYork NY USA 2008

[28] H Liu H Darabi P Banerjee and J Liu ldquoSurvey of wirelessindoor positioning techniques and systemsrdquo IEEE Transactionson Systems Man and Cybernetics Part C Applications andReviews vol 37 no 6 pp 1067ndash1080 2007

[29] M Molina-Garcıa J Calle-Sanchez J I Alonso A Fernandez-Duran and F B Barba ldquoEnhanced in-building fingerprint posi-tioning using femtocell networksrdquo Bell Labs Technical Journalvol 18 no 2 pp 195ndash211 2013

[30] A Kushki K N Plataniotis and A N VenetsanopoulosldquoKernel-based positioning in wireless local area networksrdquoIEEE Transactions on Mobile Computing vol 6 no 6 pp 689ndash705 2007

[31] T King T Haenselmann andW Effelsberg ldquoDeployment cal-ibration and measurement factors for position errors in 80211-based indoor positioning systemsrdquo in Location-and Context-Awareness pp 17ndash34 Springer 2007

[32] X Chai and Q Yang ldquoReducing the calibration effort forprobabilistic indoor location estimationrdquo IEEE Transactions onMobile Computing vol 6 no 6 pp 649ndash662 2007

[33] C Steiner and A Wittneben ldquoEfficient training phase forultrawideband-based location fingerprinting systemsrdquo IEEETransactions on Signal Processing vol 59 no 12 pp 6021ndash60322011

12 International Journal of Distributed Sensor Networks

[34] Z Yang C Wu and Y Liu ldquoLocating in fingerprint spacewireless indoor localization with little human interventionrdquoin Proceedings of the 18th Annual International Conference onMobile Computing and Networking pp 269ndash280 August 2012

[35] Y Chon N D Lane F Li H Cha and F Zhao ldquoAutomaticallycharacterizing places with opportunistic crowdsensing usingsmartphonesrdquo in Proceedings of the 14th International Confer-ence on Ubiquitous Computing (UbiComp rsquo12) pp 481ndash490Pittsburgh Pa USA September 2012

[36] P Torteeka and X Chundi ldquoIndoor positioning based on Wi-Fi fingerprint technique using fuzzy K-nearest neighborrdquo inProceedings of the 11th International Bhurban Conference onApplied Sciences and Technology (IBCAST rsquo14) pp 461ndash465Islamabad Pakistan January 2014

[37] B Shin J H Lee T Lee and H S Kim ldquoEnhanced weightedK-nearest neighbor algorithm for indoor Wi-Fi positioningsystemsrdquo in Proceedings of the 8th International Conference onComputing Technology and Information Management (ICCMrsquo12) pp 574ndash577 April 2012

[38] D Madigan E Einahrawy R P Martin W-H Ju P Krishnanand A Krishnakumar ldquoBayesian indoor positioning systemsrdquoin Proceedings of the 24th Annual Joint Conference of the IEEEComputer and Communications Societies (INFOCOM rsquo05) vol2 pp 1217ndash1227 IEEE 2005

[39] M Zhou Y Xu and L Tang ldquoMultilayer ANN indoor locationsystem with area division in WLAN environmentrdquo Journal ofSystems Engineering and Electronics vol 21 no 5 pp 914ndash9262010

[40] Z-L Wu C-H Li J K-Y Ng and K R P H Leung ldquoLocationestimation via support vector regressionrdquo IEEE Transactions onMobile Computing vol 6 no 3 pp 311ndash321 2007

[41] F Yu M H Jiang J Liang et al ldquoAn indoor localization ofwifi based on support vector machinesrdquo in Advanced MaterialsResearch vol 926 pp 2438ndash2441 Trans Tech Publications 2014

[42] O Baala Y Zheng and A Caminada ldquoThe impact of APplacement in WLAN-based indoor positioning systemrdquo inProceedings of the 8th International Conference on Networks(ICN rsquo09) pp 12ndash17 Gosier France March 2009

[43] A Farshad J Li M K Marina and F J Garcia ldquoA microscopiclook at wifi fingerprinting for indoor mobile phone localizationin diverse environmentsrdquo in Proceedings of the InternationalConference on Indoor Positioning and Indoor Navigation (IPINrsquo13) pp 1ndash10 IEEE 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

International Journal of Distributed Sensor Networks 9

5 10 15 20 25 30 35minus100

minus90

minus80

minus70

minus60RSS from AP1

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP2

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP3

RP

RSS

5 10 15 20 25 30 35minus100

minus80

minus60

minus40RSS from AP4

RP

RSS

5 10 15 20 25 30 35minus100

minus90

minus80

minus70RSS from AP5

RP

RSS

Figure 5 Signal fluctuation at all the RPs for 5GWiFi signal

1 5 10 15 20 25 30 35 40Reference points

AP1 (24G)AP2 (24G)AP3 (24G)

AP4 (24G)AP5 (24G)

Figure 6 Detected APs from all RPs (24G)

localization In the coarse localization we use the detectedAPs to infer the coarse localization region It can eliminate the

1 5 10 15 20 25 30 35 40Reference points

AP1 (5G)AP2 (5G)AP3 (5G)

AP4 (5G)AP5 (5G)

Figure 7 Detected APs from all RPs (5G)

impossible fingerprint database to reduce the computationeffort Besides the fingerprint map is built by smartphone

10 International Journal of Distributed Sensor Networks

0 50 100 150 200 250 300 350 400 450 5000

02040608

1

The number of RPs

Effici

ency

Figure 8 Matching efficiency with the increased RPs

0 2 4 6 8 10 12 140010203040506070809

1

Error distance

CDF

KNN (24G)WKNN (24G)Fuzzy logic (24G)

Histogram (24G)Cluster KNN (24G)

Figure 9Distribution of the localization error for 24GWiFi signal

0 2 4 6 8 10 12 14 16 18 200

010203040506070809

1

Error distance

CDF

KNN (5G)WKNN (5G)Fuzzy logic (5G)

Histogram (5G)Cluster KNN (5G)

Figure 10 Distribution of the localization error for 5GWiFi signal

with our developed software application automatically Itimproved efficiency of system operation enormously In theprecise localization we propose two approaches to improvethe localization accuracy and stabilization By analysing thechange rule of WiFi signal about plusmn5 dBm of fluctuation isin the 24G and 5G signal so the real-time measurementRSS is not so accurate as localization estimation The real-time measurement RSS is firstly expanded by plusmn5 dBm andthen used for thematching with the fingerprint map In orderto select the optimal nearest neighbor points we use therelationship of RPs to eliminate the discrete nearest neigh-bor points Experimental results indicate that the proposed

005

115

225

335

445

5

Different algorithms

Aver

age e

rror

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 11 Localization mean error for 24G and 5GWiFi signal

0

2

4

6

8

10

12

Different algorithms

Varia

nce

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 12 Variance of localization error for 24G and 5G WiFisignal

algorithm is the most accurate and stable of all comparativealgorithms In addition our experiment also demonstratesthat the 5GWiFi signal is more stable for indoor localization

In the future we intend to perform this system to applyit in larger indoor construction as well as to investigatingthe performance of the proposed algorithm in more complexenvironment and the multilayered buildings

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This work was supported by the National Natural ScienceFoundation of China under Grant Project nos 61232004 and61103085

International Journal of Distributed Sensor Networks 11

References

[1] B Hofmann-Wellenhof H Lichtenegger and J Collins ldquoGlobalpositioning system theory and practicerdquo in Global PositioningSystem Theory and Practice B Hofmann-Wellenhof H Licht-enegger and J Collins Eds vol 1 p 387 Springer ViennaAustria 1993

[2] N Bulusu J Heidemann and D Estrin ldquoGPS-less low-costoutdoor localization for very small devicesrdquo IEEE PersonalCommunications vol 7 no 5 pp 28ndash34 2000

[3] C Lee Y Chang G Park et al ldquoIndoor positioning systembased on incident angles of infrared emittersrdquo in Proceedings ofthe 30thAnnual Conference of IEEE Industrial Electronics Society(IECON rsquo04) vol 3 pp 2218ndash2222 Busan Republic of KoreaNovember 2004

[4] A Shahi A Aryan J S West C T Haas and R C G HaasldquoDeterioration of UWB positioning during constructionrdquoAutomation in Construction vol 24 pp 72ndash80 2012

[5] V Filonenko C Cullen and J Carswell ldquoInvestigating ultra-sonic positioning on mobile phonesrdquo in Proceedings of theInternational Conference on Indoor Positioning and IndoorNavigation (IPIN rsquo10) pp 1ndash8 IEEE September 2010

[6] S Li Y Lou and B Liu ldquoBluetooth aided mobile phone local-ization a nonlinear neural circuit approachrdquoACMTransactionson Embedded Computing Systems vol 13 no 4 p 78 2014

[7] N Li and B Becerik-Gerber ldquoPerformance-based evaluationof RFID-based indoor location sensing solutions for the builtenvironmentrdquo Advanced Engineering Informatics vol 25 no 3pp 535ndash546 2011

[8] L Luoh ldquoZigBee-based intelligent indoor positioning systemsoft computingrdquo Soft Computing vol 18 no 3 pp 443ndash4562014

[9] Y Chen D Lymberopoulos J Liu and B Priyantha ldquoIndoorlocalization using FM signalsrdquo IEEE Transactions on MobileComputing vol 12 no 8 pp 1502ndash1517 2013

[10] V Moghtadaiee and A G Dempster ldquoIndoor location finger-printing using fm radio signalsrdquo IEEE Transactions on Broad-casting vol 60 no 2 pp 336ndash346 2014

[11] J Chung M Donahoe C Schmandt I-J Kim P Razavai andM Wiseman ldquoIndoor location sensing using geo-magnetismrdquoin Proceedings of the 9th International Conference on MobileSystems Applications and Services (MobiSys rsquo11) pp 141ndash154July 2011

[12] I Bisio M Cerruti F Lavagetto et al ldquoA trainingless WiFifingerprint positioning approach over mobile devicesrdquo IEEEAntennas and Wireless Propagation Letters vol 13 pp 832ndash8352014

[13] N Alsindi Z Chaloupka N AlKhanbashi and J Aweya ldquoAnempirical evaluation of a probabilistic rf signature for wlanlocation fingerprintingrdquo IEEETransactions onWireless Commu-nications vol 13 no 6 pp 3257ndash3268 2014

[14] CWu Z Yang and Y Liu ldquoSmartphones based crowdsourcingfor indoor localizationrdquo 2014

[15] ADanalet B Farooq andM Bierlaire ldquoABayesian approach todetect pedestrian destination-sequences fromWiFi signaturesrdquoTransportation Research Part C Emerging Technologies vol 44pp 146ndash170 2014

[16] R S Campos L Lovisolo and M L R De Campos ldquoWi-Fimulti-floor indoor positioning considering architectural aspectsand controlled computational complexityrdquo Expert Systems withApplications vol 41 no 14 pp 6211ndash6223 2014

[17] M Heidari and K Pahlavan ldquoA new statistical model for thebehavior of ranging errors in TOA-based indoor localiza-tionrdquo in Proceedings of the IEEE Wireless Communications andNetworking Conference (WCNC rsquo07) pp 2564ndash2569 IEEEKowloon Hong Kong March 2007

[18] P-H Tseng K-T Feng Y-C Lin and C-L Chen ldquoWirelesslocation tracking algorithms for environments with insufficientsignal sourcesrdquo IEEE Transactions on Mobile Computing vol 8no 12 pp 1676ndash1689 2009

[19] M Bocquet C Loyez andA Benlarbi-Delaı ldquoUsing enhanced-TDOA measurement for indoor positioningrdquo IEEE Microwaveand Wireless Components Letters vol 15 no 10 pp 612ndash6142005

[20] C-H Lim B P Ng and D Da ldquoRobust methods for AOA geo-location in a real-time indoorWiFi systemrdquo Journal of LocationBased Services vol 2 no 2 pp 112ndash121 2008

[21] K Wu J Xiao Y Yi D Chen X Luo and L M Ni ldquoCSI-basedindoor localizationrdquo IEEE Transactions on Parallel and Dis-tributed Systems vol 24 no 7 pp 1300ndash1309 2013

[22] Z Yang Z Zhou and Y Liu ldquoFrom RSSI to CSI indoorlocalization via channel responserdquo ACM Computing Surveysvol 46 no 2 article 25 2013

[23] A Jaffe and M Wax ldquoSingle-site localization via maximumdiscrimination multipath fingerprintingrdquo IEEE Transactions onSignal Processing vol 62 no 7 pp 1718ndash1728 2014

[24] C Feng W S A Au S Valaee and Z Tan ldquoReceived-signal-strength-based indoor positioning using compressive sensingrdquoIEEETransactions onMobile Computing vol 11 no 12 pp 1983ndash1993 2012

[25] M Ficco C Esposito and A Napolitano ldquoCalibrating indoorpositioning systemswith low effortsrdquo IEEE Transactions onMobile Computing vol 13 no 4 pp 737ndash751 2014

[26] F Yu M H Jiang J Liang et al ldquoAn improved indoor local-ization of wifi based on support vector machinesrdquo InternationalJournal of Future Generation Communication and Networkingvol 7 pp 191ndash206 2014

[27] S Shekhar and H Xiong ldquoIndoor positioning systemrdquo inEncyclopedia of GIS p 566 Springer NewYork NY USA 2008

[28] H Liu H Darabi P Banerjee and J Liu ldquoSurvey of wirelessindoor positioning techniques and systemsrdquo IEEE Transactionson Systems Man and Cybernetics Part C Applications andReviews vol 37 no 6 pp 1067ndash1080 2007

[29] M Molina-Garcıa J Calle-Sanchez J I Alonso A Fernandez-Duran and F B Barba ldquoEnhanced in-building fingerprint posi-tioning using femtocell networksrdquo Bell Labs Technical Journalvol 18 no 2 pp 195ndash211 2013

[30] A Kushki K N Plataniotis and A N VenetsanopoulosldquoKernel-based positioning in wireless local area networksrdquoIEEE Transactions on Mobile Computing vol 6 no 6 pp 689ndash705 2007

[31] T King T Haenselmann andW Effelsberg ldquoDeployment cal-ibration and measurement factors for position errors in 80211-based indoor positioning systemsrdquo in Location-and Context-Awareness pp 17ndash34 Springer 2007

[32] X Chai and Q Yang ldquoReducing the calibration effort forprobabilistic indoor location estimationrdquo IEEE Transactions onMobile Computing vol 6 no 6 pp 649ndash662 2007

[33] C Steiner and A Wittneben ldquoEfficient training phase forultrawideband-based location fingerprinting systemsrdquo IEEETransactions on Signal Processing vol 59 no 12 pp 6021ndash60322011

12 International Journal of Distributed Sensor Networks

[34] Z Yang C Wu and Y Liu ldquoLocating in fingerprint spacewireless indoor localization with little human interventionrdquoin Proceedings of the 18th Annual International Conference onMobile Computing and Networking pp 269ndash280 August 2012

[35] Y Chon N D Lane F Li H Cha and F Zhao ldquoAutomaticallycharacterizing places with opportunistic crowdsensing usingsmartphonesrdquo in Proceedings of the 14th International Confer-ence on Ubiquitous Computing (UbiComp rsquo12) pp 481ndash490Pittsburgh Pa USA September 2012

[36] P Torteeka and X Chundi ldquoIndoor positioning based on Wi-Fi fingerprint technique using fuzzy K-nearest neighborrdquo inProceedings of the 11th International Bhurban Conference onApplied Sciences and Technology (IBCAST rsquo14) pp 461ndash465Islamabad Pakistan January 2014

[37] B Shin J H Lee T Lee and H S Kim ldquoEnhanced weightedK-nearest neighbor algorithm for indoor Wi-Fi positioningsystemsrdquo in Proceedings of the 8th International Conference onComputing Technology and Information Management (ICCMrsquo12) pp 574ndash577 April 2012

[38] D Madigan E Einahrawy R P Martin W-H Ju P Krishnanand A Krishnakumar ldquoBayesian indoor positioning systemsrdquoin Proceedings of the 24th Annual Joint Conference of the IEEEComputer and Communications Societies (INFOCOM rsquo05) vol2 pp 1217ndash1227 IEEE 2005

[39] M Zhou Y Xu and L Tang ldquoMultilayer ANN indoor locationsystem with area division in WLAN environmentrdquo Journal ofSystems Engineering and Electronics vol 21 no 5 pp 914ndash9262010

[40] Z-L Wu C-H Li J K-Y Ng and K R P H Leung ldquoLocationestimation via support vector regressionrdquo IEEE Transactions onMobile Computing vol 6 no 3 pp 311ndash321 2007

[41] F Yu M H Jiang J Liang et al ldquoAn indoor localization ofwifi based on support vector machinesrdquo in Advanced MaterialsResearch vol 926 pp 2438ndash2441 Trans Tech Publications 2014

[42] O Baala Y Zheng and A Caminada ldquoThe impact of APplacement in WLAN-based indoor positioning systemrdquo inProceedings of the 8th International Conference on Networks(ICN rsquo09) pp 12ndash17 Gosier France March 2009

[43] A Farshad J Li M K Marina and F J Garcia ldquoA microscopiclook at wifi fingerprinting for indoor mobile phone localizationin diverse environmentsrdquo in Proceedings of the InternationalConference on Indoor Positioning and Indoor Navigation (IPINrsquo13) pp 1ndash10 IEEE 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

10 International Journal of Distributed Sensor Networks

0 50 100 150 200 250 300 350 400 450 5000

02040608

1

The number of RPs

Effici

ency

Figure 8 Matching efficiency with the increased RPs

0 2 4 6 8 10 12 140010203040506070809

1

Error distance

CDF

KNN (24G)WKNN (24G)Fuzzy logic (24G)

Histogram (24G)Cluster KNN (24G)

Figure 9Distribution of the localization error for 24GWiFi signal

0 2 4 6 8 10 12 14 16 18 200

010203040506070809

1

Error distance

CDF

KNN (5G)WKNN (5G)Fuzzy logic (5G)

Histogram (5G)Cluster KNN (5G)

Figure 10 Distribution of the localization error for 5GWiFi signal

with our developed software application automatically Itimproved efficiency of system operation enormously In theprecise localization we propose two approaches to improvethe localization accuracy and stabilization By analysing thechange rule of WiFi signal about plusmn5 dBm of fluctuation isin the 24G and 5G signal so the real-time measurementRSS is not so accurate as localization estimation The real-time measurement RSS is firstly expanded by plusmn5 dBm andthen used for thematching with the fingerprint map In orderto select the optimal nearest neighbor points we use therelationship of RPs to eliminate the discrete nearest neigh-bor points Experimental results indicate that the proposed

005

115

225

335

445

5

Different algorithms

Aver

age e

rror

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 11 Localization mean error for 24G and 5GWiFi signal

0

2

4

6

8

10

12

Different algorithms

Varia

nce

KNN (24G)WKNN (24G)Fuzzy logic (24G)Histogram (24G)Cluster KNN (24G)

KNN (5G)WKNN (5G)Fuzzy logic (5G)Histogram (5G)Cluster KNN (5G)

Figure 12 Variance of localization error for 24G and 5G WiFisignal

algorithm is the most accurate and stable of all comparativealgorithms In addition our experiment also demonstratesthat the 5GWiFi signal is more stable for indoor localization

In the future we intend to perform this system to applyit in larger indoor construction as well as to investigatingthe performance of the proposed algorithm in more complexenvironment and the multilayered buildings

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This work was supported by the National Natural ScienceFoundation of China under Grant Project nos 61232004 and61103085

International Journal of Distributed Sensor Networks 11

References

[1] B Hofmann-Wellenhof H Lichtenegger and J Collins ldquoGlobalpositioning system theory and practicerdquo in Global PositioningSystem Theory and Practice B Hofmann-Wellenhof H Licht-enegger and J Collins Eds vol 1 p 387 Springer ViennaAustria 1993

[2] N Bulusu J Heidemann and D Estrin ldquoGPS-less low-costoutdoor localization for very small devicesrdquo IEEE PersonalCommunications vol 7 no 5 pp 28ndash34 2000

[3] C Lee Y Chang G Park et al ldquoIndoor positioning systembased on incident angles of infrared emittersrdquo in Proceedings ofthe 30thAnnual Conference of IEEE Industrial Electronics Society(IECON rsquo04) vol 3 pp 2218ndash2222 Busan Republic of KoreaNovember 2004

[4] A Shahi A Aryan J S West C T Haas and R C G HaasldquoDeterioration of UWB positioning during constructionrdquoAutomation in Construction vol 24 pp 72ndash80 2012

[5] V Filonenko C Cullen and J Carswell ldquoInvestigating ultra-sonic positioning on mobile phonesrdquo in Proceedings of theInternational Conference on Indoor Positioning and IndoorNavigation (IPIN rsquo10) pp 1ndash8 IEEE September 2010

[6] S Li Y Lou and B Liu ldquoBluetooth aided mobile phone local-ization a nonlinear neural circuit approachrdquoACMTransactionson Embedded Computing Systems vol 13 no 4 p 78 2014

[7] N Li and B Becerik-Gerber ldquoPerformance-based evaluationof RFID-based indoor location sensing solutions for the builtenvironmentrdquo Advanced Engineering Informatics vol 25 no 3pp 535ndash546 2011

[8] L Luoh ldquoZigBee-based intelligent indoor positioning systemsoft computingrdquo Soft Computing vol 18 no 3 pp 443ndash4562014

[9] Y Chen D Lymberopoulos J Liu and B Priyantha ldquoIndoorlocalization using FM signalsrdquo IEEE Transactions on MobileComputing vol 12 no 8 pp 1502ndash1517 2013

[10] V Moghtadaiee and A G Dempster ldquoIndoor location finger-printing using fm radio signalsrdquo IEEE Transactions on Broad-casting vol 60 no 2 pp 336ndash346 2014

[11] J Chung M Donahoe C Schmandt I-J Kim P Razavai andM Wiseman ldquoIndoor location sensing using geo-magnetismrdquoin Proceedings of the 9th International Conference on MobileSystems Applications and Services (MobiSys rsquo11) pp 141ndash154July 2011

[12] I Bisio M Cerruti F Lavagetto et al ldquoA trainingless WiFifingerprint positioning approach over mobile devicesrdquo IEEEAntennas and Wireless Propagation Letters vol 13 pp 832ndash8352014

[13] N Alsindi Z Chaloupka N AlKhanbashi and J Aweya ldquoAnempirical evaluation of a probabilistic rf signature for wlanlocation fingerprintingrdquo IEEETransactions onWireless Commu-nications vol 13 no 6 pp 3257ndash3268 2014

[14] CWu Z Yang and Y Liu ldquoSmartphones based crowdsourcingfor indoor localizationrdquo 2014

[15] ADanalet B Farooq andM Bierlaire ldquoABayesian approach todetect pedestrian destination-sequences fromWiFi signaturesrdquoTransportation Research Part C Emerging Technologies vol 44pp 146ndash170 2014

[16] R S Campos L Lovisolo and M L R De Campos ldquoWi-Fimulti-floor indoor positioning considering architectural aspectsand controlled computational complexityrdquo Expert Systems withApplications vol 41 no 14 pp 6211ndash6223 2014

[17] M Heidari and K Pahlavan ldquoA new statistical model for thebehavior of ranging errors in TOA-based indoor localiza-tionrdquo in Proceedings of the IEEE Wireless Communications andNetworking Conference (WCNC rsquo07) pp 2564ndash2569 IEEEKowloon Hong Kong March 2007

[18] P-H Tseng K-T Feng Y-C Lin and C-L Chen ldquoWirelesslocation tracking algorithms for environments with insufficientsignal sourcesrdquo IEEE Transactions on Mobile Computing vol 8no 12 pp 1676ndash1689 2009

[19] M Bocquet C Loyez andA Benlarbi-Delaı ldquoUsing enhanced-TDOA measurement for indoor positioningrdquo IEEE Microwaveand Wireless Components Letters vol 15 no 10 pp 612ndash6142005

[20] C-H Lim B P Ng and D Da ldquoRobust methods for AOA geo-location in a real-time indoorWiFi systemrdquo Journal of LocationBased Services vol 2 no 2 pp 112ndash121 2008

[21] K Wu J Xiao Y Yi D Chen X Luo and L M Ni ldquoCSI-basedindoor localizationrdquo IEEE Transactions on Parallel and Dis-tributed Systems vol 24 no 7 pp 1300ndash1309 2013

[22] Z Yang Z Zhou and Y Liu ldquoFrom RSSI to CSI indoorlocalization via channel responserdquo ACM Computing Surveysvol 46 no 2 article 25 2013

[23] A Jaffe and M Wax ldquoSingle-site localization via maximumdiscrimination multipath fingerprintingrdquo IEEE Transactions onSignal Processing vol 62 no 7 pp 1718ndash1728 2014

[24] C Feng W S A Au S Valaee and Z Tan ldquoReceived-signal-strength-based indoor positioning using compressive sensingrdquoIEEETransactions onMobile Computing vol 11 no 12 pp 1983ndash1993 2012

[25] M Ficco C Esposito and A Napolitano ldquoCalibrating indoorpositioning systemswith low effortsrdquo IEEE Transactions onMobile Computing vol 13 no 4 pp 737ndash751 2014

[26] F Yu M H Jiang J Liang et al ldquoAn improved indoor local-ization of wifi based on support vector machinesrdquo InternationalJournal of Future Generation Communication and Networkingvol 7 pp 191ndash206 2014

[27] S Shekhar and H Xiong ldquoIndoor positioning systemrdquo inEncyclopedia of GIS p 566 Springer NewYork NY USA 2008

[28] H Liu H Darabi P Banerjee and J Liu ldquoSurvey of wirelessindoor positioning techniques and systemsrdquo IEEE Transactionson Systems Man and Cybernetics Part C Applications andReviews vol 37 no 6 pp 1067ndash1080 2007

[29] M Molina-Garcıa J Calle-Sanchez J I Alonso A Fernandez-Duran and F B Barba ldquoEnhanced in-building fingerprint posi-tioning using femtocell networksrdquo Bell Labs Technical Journalvol 18 no 2 pp 195ndash211 2013

[30] A Kushki K N Plataniotis and A N VenetsanopoulosldquoKernel-based positioning in wireless local area networksrdquoIEEE Transactions on Mobile Computing vol 6 no 6 pp 689ndash705 2007

[31] T King T Haenselmann andW Effelsberg ldquoDeployment cal-ibration and measurement factors for position errors in 80211-based indoor positioning systemsrdquo in Location-and Context-Awareness pp 17ndash34 Springer 2007

[32] X Chai and Q Yang ldquoReducing the calibration effort forprobabilistic indoor location estimationrdquo IEEE Transactions onMobile Computing vol 6 no 6 pp 649ndash662 2007

[33] C Steiner and A Wittneben ldquoEfficient training phase forultrawideband-based location fingerprinting systemsrdquo IEEETransactions on Signal Processing vol 59 no 12 pp 6021ndash60322011

12 International Journal of Distributed Sensor Networks

[34] Z Yang C Wu and Y Liu ldquoLocating in fingerprint spacewireless indoor localization with little human interventionrdquoin Proceedings of the 18th Annual International Conference onMobile Computing and Networking pp 269ndash280 August 2012

[35] Y Chon N D Lane F Li H Cha and F Zhao ldquoAutomaticallycharacterizing places with opportunistic crowdsensing usingsmartphonesrdquo in Proceedings of the 14th International Confer-ence on Ubiquitous Computing (UbiComp rsquo12) pp 481ndash490Pittsburgh Pa USA September 2012

[36] P Torteeka and X Chundi ldquoIndoor positioning based on Wi-Fi fingerprint technique using fuzzy K-nearest neighborrdquo inProceedings of the 11th International Bhurban Conference onApplied Sciences and Technology (IBCAST rsquo14) pp 461ndash465Islamabad Pakistan January 2014

[37] B Shin J H Lee T Lee and H S Kim ldquoEnhanced weightedK-nearest neighbor algorithm for indoor Wi-Fi positioningsystemsrdquo in Proceedings of the 8th International Conference onComputing Technology and Information Management (ICCMrsquo12) pp 574ndash577 April 2012

[38] D Madigan E Einahrawy R P Martin W-H Ju P Krishnanand A Krishnakumar ldquoBayesian indoor positioning systemsrdquoin Proceedings of the 24th Annual Joint Conference of the IEEEComputer and Communications Societies (INFOCOM rsquo05) vol2 pp 1217ndash1227 IEEE 2005

[39] M Zhou Y Xu and L Tang ldquoMultilayer ANN indoor locationsystem with area division in WLAN environmentrdquo Journal ofSystems Engineering and Electronics vol 21 no 5 pp 914ndash9262010

[40] Z-L Wu C-H Li J K-Y Ng and K R P H Leung ldquoLocationestimation via support vector regressionrdquo IEEE Transactions onMobile Computing vol 6 no 3 pp 311ndash321 2007

[41] F Yu M H Jiang J Liang et al ldquoAn indoor localization ofwifi based on support vector machinesrdquo in Advanced MaterialsResearch vol 926 pp 2438ndash2441 Trans Tech Publications 2014

[42] O Baala Y Zheng and A Caminada ldquoThe impact of APplacement in WLAN-based indoor positioning systemrdquo inProceedings of the 8th International Conference on Networks(ICN rsquo09) pp 12ndash17 Gosier France March 2009

[43] A Farshad J Li M K Marina and F J Garcia ldquoA microscopiclook at wifi fingerprinting for indoor mobile phone localizationin diverse environmentsrdquo in Proceedings of the InternationalConference on Indoor Positioning and Indoor Navigation (IPINrsquo13) pp 1ndash10 IEEE 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

International Journal of Distributed Sensor Networks 11

References

[1] B Hofmann-Wellenhof H Lichtenegger and J Collins ldquoGlobalpositioning system theory and practicerdquo in Global PositioningSystem Theory and Practice B Hofmann-Wellenhof H Licht-enegger and J Collins Eds vol 1 p 387 Springer ViennaAustria 1993

[2] N Bulusu J Heidemann and D Estrin ldquoGPS-less low-costoutdoor localization for very small devicesrdquo IEEE PersonalCommunications vol 7 no 5 pp 28ndash34 2000

[3] C Lee Y Chang G Park et al ldquoIndoor positioning systembased on incident angles of infrared emittersrdquo in Proceedings ofthe 30thAnnual Conference of IEEE Industrial Electronics Society(IECON rsquo04) vol 3 pp 2218ndash2222 Busan Republic of KoreaNovember 2004

[4] A Shahi A Aryan J S West C T Haas and R C G HaasldquoDeterioration of UWB positioning during constructionrdquoAutomation in Construction vol 24 pp 72ndash80 2012

[5] V Filonenko C Cullen and J Carswell ldquoInvestigating ultra-sonic positioning on mobile phonesrdquo in Proceedings of theInternational Conference on Indoor Positioning and IndoorNavigation (IPIN rsquo10) pp 1ndash8 IEEE September 2010

[6] S Li Y Lou and B Liu ldquoBluetooth aided mobile phone local-ization a nonlinear neural circuit approachrdquoACMTransactionson Embedded Computing Systems vol 13 no 4 p 78 2014

[7] N Li and B Becerik-Gerber ldquoPerformance-based evaluationof RFID-based indoor location sensing solutions for the builtenvironmentrdquo Advanced Engineering Informatics vol 25 no 3pp 535ndash546 2011

[8] L Luoh ldquoZigBee-based intelligent indoor positioning systemsoft computingrdquo Soft Computing vol 18 no 3 pp 443ndash4562014

[9] Y Chen D Lymberopoulos J Liu and B Priyantha ldquoIndoorlocalization using FM signalsrdquo IEEE Transactions on MobileComputing vol 12 no 8 pp 1502ndash1517 2013

[10] V Moghtadaiee and A G Dempster ldquoIndoor location finger-printing using fm radio signalsrdquo IEEE Transactions on Broad-casting vol 60 no 2 pp 336ndash346 2014

[11] J Chung M Donahoe C Schmandt I-J Kim P Razavai andM Wiseman ldquoIndoor location sensing using geo-magnetismrdquoin Proceedings of the 9th International Conference on MobileSystems Applications and Services (MobiSys rsquo11) pp 141ndash154July 2011

[12] I Bisio M Cerruti F Lavagetto et al ldquoA trainingless WiFifingerprint positioning approach over mobile devicesrdquo IEEEAntennas and Wireless Propagation Letters vol 13 pp 832ndash8352014

[13] N Alsindi Z Chaloupka N AlKhanbashi and J Aweya ldquoAnempirical evaluation of a probabilistic rf signature for wlanlocation fingerprintingrdquo IEEETransactions onWireless Commu-nications vol 13 no 6 pp 3257ndash3268 2014

[14] CWu Z Yang and Y Liu ldquoSmartphones based crowdsourcingfor indoor localizationrdquo 2014

[15] ADanalet B Farooq andM Bierlaire ldquoABayesian approach todetect pedestrian destination-sequences fromWiFi signaturesrdquoTransportation Research Part C Emerging Technologies vol 44pp 146ndash170 2014

[16] R S Campos L Lovisolo and M L R De Campos ldquoWi-Fimulti-floor indoor positioning considering architectural aspectsand controlled computational complexityrdquo Expert Systems withApplications vol 41 no 14 pp 6211ndash6223 2014

[17] M Heidari and K Pahlavan ldquoA new statistical model for thebehavior of ranging errors in TOA-based indoor localiza-tionrdquo in Proceedings of the IEEE Wireless Communications andNetworking Conference (WCNC rsquo07) pp 2564ndash2569 IEEEKowloon Hong Kong March 2007

[18] P-H Tseng K-T Feng Y-C Lin and C-L Chen ldquoWirelesslocation tracking algorithms for environments with insufficientsignal sourcesrdquo IEEE Transactions on Mobile Computing vol 8no 12 pp 1676ndash1689 2009

[19] M Bocquet C Loyez andA Benlarbi-Delaı ldquoUsing enhanced-TDOA measurement for indoor positioningrdquo IEEE Microwaveand Wireless Components Letters vol 15 no 10 pp 612ndash6142005

[20] C-H Lim B P Ng and D Da ldquoRobust methods for AOA geo-location in a real-time indoorWiFi systemrdquo Journal of LocationBased Services vol 2 no 2 pp 112ndash121 2008

[21] K Wu J Xiao Y Yi D Chen X Luo and L M Ni ldquoCSI-basedindoor localizationrdquo IEEE Transactions on Parallel and Dis-tributed Systems vol 24 no 7 pp 1300ndash1309 2013

[22] Z Yang Z Zhou and Y Liu ldquoFrom RSSI to CSI indoorlocalization via channel responserdquo ACM Computing Surveysvol 46 no 2 article 25 2013

[23] A Jaffe and M Wax ldquoSingle-site localization via maximumdiscrimination multipath fingerprintingrdquo IEEE Transactions onSignal Processing vol 62 no 7 pp 1718ndash1728 2014

[24] C Feng W S A Au S Valaee and Z Tan ldquoReceived-signal-strength-based indoor positioning using compressive sensingrdquoIEEETransactions onMobile Computing vol 11 no 12 pp 1983ndash1993 2012

[25] M Ficco C Esposito and A Napolitano ldquoCalibrating indoorpositioning systemswith low effortsrdquo IEEE Transactions onMobile Computing vol 13 no 4 pp 737ndash751 2014

[26] F Yu M H Jiang J Liang et al ldquoAn improved indoor local-ization of wifi based on support vector machinesrdquo InternationalJournal of Future Generation Communication and Networkingvol 7 pp 191ndash206 2014

[27] S Shekhar and H Xiong ldquoIndoor positioning systemrdquo inEncyclopedia of GIS p 566 Springer NewYork NY USA 2008

[28] H Liu H Darabi P Banerjee and J Liu ldquoSurvey of wirelessindoor positioning techniques and systemsrdquo IEEE Transactionson Systems Man and Cybernetics Part C Applications andReviews vol 37 no 6 pp 1067ndash1080 2007

[29] M Molina-Garcıa J Calle-Sanchez J I Alonso A Fernandez-Duran and F B Barba ldquoEnhanced in-building fingerprint posi-tioning using femtocell networksrdquo Bell Labs Technical Journalvol 18 no 2 pp 195ndash211 2013

[30] A Kushki K N Plataniotis and A N VenetsanopoulosldquoKernel-based positioning in wireless local area networksrdquoIEEE Transactions on Mobile Computing vol 6 no 6 pp 689ndash705 2007

[31] T King T Haenselmann andW Effelsberg ldquoDeployment cal-ibration and measurement factors for position errors in 80211-based indoor positioning systemsrdquo in Location-and Context-Awareness pp 17ndash34 Springer 2007

[32] X Chai and Q Yang ldquoReducing the calibration effort forprobabilistic indoor location estimationrdquo IEEE Transactions onMobile Computing vol 6 no 6 pp 649ndash662 2007

[33] C Steiner and A Wittneben ldquoEfficient training phase forultrawideband-based location fingerprinting systemsrdquo IEEETransactions on Signal Processing vol 59 no 12 pp 6021ndash60322011

12 International Journal of Distributed Sensor Networks

[34] Z Yang C Wu and Y Liu ldquoLocating in fingerprint spacewireless indoor localization with little human interventionrdquoin Proceedings of the 18th Annual International Conference onMobile Computing and Networking pp 269ndash280 August 2012

[35] Y Chon N D Lane F Li H Cha and F Zhao ldquoAutomaticallycharacterizing places with opportunistic crowdsensing usingsmartphonesrdquo in Proceedings of the 14th International Confer-ence on Ubiquitous Computing (UbiComp rsquo12) pp 481ndash490Pittsburgh Pa USA September 2012

[36] P Torteeka and X Chundi ldquoIndoor positioning based on Wi-Fi fingerprint technique using fuzzy K-nearest neighborrdquo inProceedings of the 11th International Bhurban Conference onApplied Sciences and Technology (IBCAST rsquo14) pp 461ndash465Islamabad Pakistan January 2014

[37] B Shin J H Lee T Lee and H S Kim ldquoEnhanced weightedK-nearest neighbor algorithm for indoor Wi-Fi positioningsystemsrdquo in Proceedings of the 8th International Conference onComputing Technology and Information Management (ICCMrsquo12) pp 574ndash577 April 2012

[38] D Madigan E Einahrawy R P Martin W-H Ju P Krishnanand A Krishnakumar ldquoBayesian indoor positioning systemsrdquoin Proceedings of the 24th Annual Joint Conference of the IEEEComputer and Communications Societies (INFOCOM rsquo05) vol2 pp 1217ndash1227 IEEE 2005

[39] M Zhou Y Xu and L Tang ldquoMultilayer ANN indoor locationsystem with area division in WLAN environmentrdquo Journal ofSystems Engineering and Electronics vol 21 no 5 pp 914ndash9262010

[40] Z-L Wu C-H Li J K-Y Ng and K R P H Leung ldquoLocationestimation via support vector regressionrdquo IEEE Transactions onMobile Computing vol 6 no 3 pp 311ndash321 2007

[41] F Yu M H Jiang J Liang et al ldquoAn indoor localization ofwifi based on support vector machinesrdquo in Advanced MaterialsResearch vol 926 pp 2438ndash2441 Trans Tech Publications 2014

[42] O Baala Y Zheng and A Caminada ldquoThe impact of APplacement in WLAN-based indoor positioning systemrdquo inProceedings of the 8th International Conference on Networks(ICN rsquo09) pp 12ndash17 Gosier France March 2009

[43] A Farshad J Li M K Marina and F J Garcia ldquoA microscopiclook at wifi fingerprinting for indoor mobile phone localizationin diverse environmentsrdquo in Proceedings of the InternationalConference on Indoor Positioning and Indoor Navigation (IPINrsquo13) pp 1ndash10 IEEE 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

12 International Journal of Distributed Sensor Networks

[34] Z Yang C Wu and Y Liu ldquoLocating in fingerprint spacewireless indoor localization with little human interventionrdquoin Proceedings of the 18th Annual International Conference onMobile Computing and Networking pp 269ndash280 August 2012

[35] Y Chon N D Lane F Li H Cha and F Zhao ldquoAutomaticallycharacterizing places with opportunistic crowdsensing usingsmartphonesrdquo in Proceedings of the 14th International Confer-ence on Ubiquitous Computing (UbiComp rsquo12) pp 481ndash490Pittsburgh Pa USA September 2012

[36] P Torteeka and X Chundi ldquoIndoor positioning based on Wi-Fi fingerprint technique using fuzzy K-nearest neighborrdquo inProceedings of the 11th International Bhurban Conference onApplied Sciences and Technology (IBCAST rsquo14) pp 461ndash465Islamabad Pakistan January 2014

[37] B Shin J H Lee T Lee and H S Kim ldquoEnhanced weightedK-nearest neighbor algorithm for indoor Wi-Fi positioningsystemsrdquo in Proceedings of the 8th International Conference onComputing Technology and Information Management (ICCMrsquo12) pp 574ndash577 April 2012

[38] D Madigan E Einahrawy R P Martin W-H Ju P Krishnanand A Krishnakumar ldquoBayesian indoor positioning systemsrdquoin Proceedings of the 24th Annual Joint Conference of the IEEEComputer and Communications Societies (INFOCOM rsquo05) vol2 pp 1217ndash1227 IEEE 2005

[39] M Zhou Y Xu and L Tang ldquoMultilayer ANN indoor locationsystem with area division in WLAN environmentrdquo Journal ofSystems Engineering and Electronics vol 21 no 5 pp 914ndash9262010

[40] Z-L Wu C-H Li J K-Y Ng and K R P H Leung ldquoLocationestimation via support vector regressionrdquo IEEE Transactions onMobile Computing vol 6 no 3 pp 311ndash321 2007

[41] F Yu M H Jiang J Liang et al ldquoAn indoor localization ofwifi based on support vector machinesrdquo in Advanced MaterialsResearch vol 926 pp 2438ndash2441 Trans Tech Publications 2014

[42] O Baala Y Zheng and A Caminada ldquoThe impact of APplacement in WLAN-based indoor positioning systemrdquo inProceedings of the 8th International Conference on Networks(ICN rsquo09) pp 12ndash17 Gosier France March 2009

[43] A Farshad J Li M K Marina and F J Garcia ldquoA microscopiclook at wifi fingerprinting for indoor mobile phone localizationin diverse environmentsrdquo in Proceedings of the InternationalConference on Indoor Positioning and Indoor Navigation (IPINrsquo13) pp 1ndash10 IEEE 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of