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Gradient-based Fingerprinting for IndoorLocalization and Tracking

Yuanchao Shu, Member, IEEE, Yinghua Huang, Jiaqi Zhang, Philippe Coue, Peng Cheng, Member, IEEE,Jiming Chen, Senior Member, IEEE, and Kang G. Shin, Life Fellow, IEEE

Abstract—Of the different branches of indoor localizationresearch, WiFi fingerprinting has drawn significant attentionover the past decade. These localization systems function bycomparing WiFi Received Signal Strength Indicator (RSSI) anda pre-established location-specific fingerprint map. However, dueto the time-variant wireless signal strength, the RSSI fingerprintmap needs to be calibrated periodically, incurring high laborand time costs. In addition, biased RSSI measurements acrossdevices along with transmission power control techniques of WiFirouters further undermine the fidelity of existing fingerprint-based localization systems. To remedy these problems, we proposeGIFT which leverages a more stable RSSI gradient. GIFT firstbuilds a gradient-based fingerprint map (Gmap) by comparingabsolute RSSI values at nearby positions, and then runs anonline extended particle filter to localize the user/device. Byincorporating Gmap, GIFT is more adaptive to the time-variantRSSI in indoor environments, thus effectively reducing theoverhead of fingerprint map calibration. We implemented GIFTon Android smartphones and tablets, and conducted extensiveexperiments in a five-story campus building. GIFT is shown toachieve an 80 percentile accuracy of 5.6m with dynamic WiFisignals.

Index Terms—Indoor localization, fingerprinting, WiFi

I. INTRODUCTION

Emergence of location-based services and applications hasled to a growing demand of anywhere localization [2], [18],[29], [35], [42]. Although space-based satellite navigationsystems such as GPS offer high localization accuracy outdoor,the poor connectivity between satellites and end-devicesmakes them unavailable indoor, triggering research on indoorlocalization.

Numerous approaches in indoor localization have beenproposed using Inertial Measurement Unit (IMU) [21], ul-trasonic [6], [18], radio-frequency ID tags [29], [44], [45],FM [47], magnetic field [7], [20], WiFi [27] and etc. Among

Manuscript received January 10, 2015. Revised April 20, 2015. Acceptedfor publication June 25, 2015

Copyright (c) 2015 IEEE. Personal use of this material is permitted.However, permission to use this material for any other purposes must beobtained from the IEEE by sending a request to pubs-permissions@ieee.org.

Yuanchao Shu is with Microsoft Research Asia. (Email:yushu@microsoft.com)

Yinghua Huang is with Huawei Technologies Co. Ltd.(Email:huangyinghua@huawei.com).

Jiaqi Zhang, Peng Cheng and Jiming Chen are with the State Key Lab. ofIndustrial Control Technology, Zhejiang University, Hangzhou, China. (Email:jiaqizhang@zju.edu.cn, {pcheng, jmchen}@iipc.zju.edu.cn).

Philippe Coue is a data intelligence consultant working in Paris, France.(Email: king.pcoue@gmail.com)

Kang G. Shin is with the Department of Electrical Engineering andComputer Science, University of Michigan – Ann Arbor, USA. (Email:kgshin@umich.edu).

different branches of techniques, fingerprinting approachesbased on WiFi Received Signal Strength Indicator (RSSI)have attracted the most attention over the last decade. Insuch systems, an RSSI signature database (a.k.a. fingerprintmap) needs to be constructed first in the training phase. Userlocation is then obtained according to the results of comparingRSSI observations and the position-specific fingerprint map.WiFi fingerprinting systems are known to be accurate andfree of additional infrastructures and specific hardware [47].However, they suffer from several shortcomings, of which theWiFi signal variation is a major hurdle.

WiFi signal strength is subjected to indoor environmentalchanges. In addition to these probabilistic temporal variations,recent technologies implemented in off-the-shelf Access Points(APs) also cause difficulties in large-scale deployments ofexisting RSSI-based indoor localization systems. For example,in the AP-220 series APs, Aruba Networks implementsAdaptive Radio Management (ARM) which could monitorthe ambient radio frequency (RF) environment and auto-matically adjust transmit power to optimize the overallnetwork performance [15]. Similar technologies could alsobe found in the equipments of other networking vendorssuch as Netgear [17] and Cisco [16]. Device diversity [11],[26] is another issue in WiFi-based fingerprinting systemswhere signal strength variations are observed among differentdevices. Due to unpredictable environmental changes, andlimited a priori knowledge on both the sensing device and thepower adjustment strategy, the RSSI fingerprint map needs tobe periodically calibrated, incurring high costs on both laborand time.

Recent technological advances improved the efficiency ofWiFi fingerprint map construction [27], [30], [39], [46]. Forexample, crowdsourcing is used to reduce the overhead ofsite profiling by distributing the workload among multiplemobile users [27], [46]. Floor maps and indoor landmarkshave also been exploited for fast and unsupervised site pro-filing [30], [39]. Although these efforts improved fingerprintmap construction, they still cannot deal with uneven fingerprintdensity and device heterogeneity, and therefore cannot adaptto the variations in AP transmission power, let alone reducingthe frequency of the map construction to eliminate time-consuming and labor-intensive database maintenance.

To deal with these problems, we propose a GradIent Fin-gerprinTing (GIFT) indoor localization and tracking system.The basic rationale behind GIFT comes from the observationthat differential RSSI between nearby positions is more stablethan the absolute RSSI values, and more importantly, is

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independent of the AP’s transmission power and the sensingdevice. For example, in an ideal RF environment, as the useris walking towards an AP, he will obtain increasing RSSImeasurements with high probability, regardless of the AP’sabsolute transmission power and model of the sensing device.

Unlike several existing calibration approaches, no assump-tions on the RSSI variation model caused by environmentalchanges and device diversity (e.g., linear relationship [13],[19]) are made in this paper. Instead, we extract the binaryRSSI gradients from the RSSI fingerprint map, and establishthe gradient fingerprint database, Gmap, leveraging statisticalhypothesis testing methods. This way, GIFT is able todeal with both the time-varying effects and diverse RSSImeasurements from heterogeneous devices, thus dramaticallyreducing the overhead of periodical maintenance of thefingerprint map. Besides Gmap, we design an extendedparticle filter-based GIFT engine which simultaneously detectsuser movements and tracks the user’s position.

The contributions of this paper are summarized as follows.• Based on the observation of the WiFi signal strength

fluctuation, we proposed a novel indoor localization andtracking system using the RSSI gradient, i.e., GIFT.Unlike existing work which focuses on the design ofeither efficient fingerprint map building or calibrationacross heterogeneous clients, GIFT relies on simplerbinary RSSI gradient values.

• We develop a novel method to construct the binarygradient fingerprint map on top of the WiFi RSSIfingerprint map using significance testing. It is back-ward compatible with existing, advanced fingerprint mapconstruction approaches which could further reduce thetraining overhead.

• We design an extended particle filter-based GIFT enginewith a motion detection module. It automatically locatesand tracks the user and is adaptive to different mobilitypatterns.

• We have implemented GIFT, as well as two legacyWiFi fingerprinting methods on smartphones and tablets,and conducted extensive experiments in a five-storycampus building. The experimental results demonstratehigh localization accuracy and robustness of GIFT incomplex indoor WiFi environments.

The rest of the paper is organized as follows. Section IIsummarizes the related work firstly and Section III motivatesGIFT with preliminary observations on WiFi RSSI in indoorenvironments. Section IV presents an overview of GIFTdesign. Sections V and VI detail the gradient-based mapconstruction and the GIFT engine, respectively. Section VIIpresents the experimental evaluation of our proposed approach.Section VIII concludes the paper.

II. RELATED WORK

Indoor localization has been extensively studied over thepast decade. Here we only focus on the systems related toGIFT.

WiFi-based fingerprinting has been discussed heavily in thisarea [1], [3], [36], [40], [48]. As a pioneering fingerprint-based indoor localization system, RADAR [1] first overhears

and records broadcast packets from WiFi APs to build alocation-specific fingerprint map. Localization can be achievedby performing lookups within a pre-established database.On the basis of basic fingerprinting, Horus [48] adopts aprobability-based inference model, where the RSSI from anAP is modeled into a random variable in both time andspatial domains. Fingerprinting leveraging FM [3], [47] signalsand magnetic field [7], [20] are also well studied. Despitethe low cost on infrastructures, fingerprint-based approachessuffer from periodical onerous fingerprint map calibrationcaused by environmental changes. Although crowdsourcingtechniques [27], [30], [46] could be used to reduce the cost,they do not take frequent and automatic configuration changesof WLAN APs into consideration. In GIFT, by replacing theabsolute value-based fingerprint map with a gradient-basedmap, we minimize the overhead on repetitive site profilingwithout performance degradation.

Another issue of fingerprinting is the time-varying signalstrength and biased observations reported by heterogeneousdevices [32]. The authors of [11] proposed an automaticradio map generation mechanism for device-free localization.It requires full knowledge of the AP (e.g., the position, thetransmission power and the antenna radiation pattern) and thesurrounding environment (e.g., RF propagation properties ofcommon building materials) to handle transmission reflectionsand diffractions. In addition, the overhead of constructingthe measured radio map is prohibitive when taking thehuman body effect into account in RF prorogation modeling.In [13], Haeberlen et al. suggest environmental changes couldbe compensated by a simple linear transformation betweenRSSI values from different devices. However, Park et al.[26] claim that a linear transformation is insufficient forcross-device localization. Instead, they propose to use kernelestimation with wide kernel widths at the cost of single devicelocalization performance degradation. In [19], fingerprintsare recorded as more stable signal strength ratios betweenpairs of base stations and Dong et al. [10] proposed touse the difference between signal strengths across accesspoints as a localization feature. However, as each AP adjustsits transmission power independently, the signal strengthratio (or the difference) remains unstable and unpredictable.WiGEM [12] is a learning-based approach using the GaussianMixture Model (GMM) and Expectation Maximization (EM)to estimate parameters of the WiFi propagation model onsniffers (or APs doubling as sniffers). It is robust to the devicediversity and power level variability but requires knowledge ofAP locations. Despite all of these techniques, GIFT leveragesbinary spatial RSSI gradients without any assumptions onsignal prorogation models or signal strength transformations(e.g., linear) among devices, and is also complementary tothe model-based fingerprint map construction approaches (e.g.,WiGEM) for reducing the bootstrap effort.

Range-based approaches localize the user/device based ondistance estimation, which can be achieved by measuring thereceived signal strength [5], [43], chirp-spread-spectrum [41],curve fitting [37] or Time-of-Arrival (ToA) [4], [24]. Signalpropagation models are widely used in RSSI-based rangingapproaches [5], [9], [23]. However, varied and complex

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interior structures in different indoor environments preventthe usage of a universal radio attenuation model thereforemake such kind of systems less practical. In ToA-basedranging, time delays in signal propagation is leveraged toestimate distances between wireless nodes. With transmittersat known locations, difference in time of arrival of multiple RFsignals are also used by TPS [4]. Although these techniquesoffer high localization accuracy, they require complicatedprocessing in either software or hardware to ensure tighttime synchronization and precise ranging, limiting theirapplicability.

Dead Reckoning (DR) is also a well-studied localizationtopic for its infrastructure independency. These systemsachieve positioning through data fusion from multiple inertialsensors, e.g., accelerometer, gyroscope and magnetometer.However, the unstable human locomotion during walking andaccumulated errors in inertial sensing make DR hard to playwell solely. Anomalies of indoor geomagnetic field, causedby local construction materials, also give rise to noisy direc-tion sensing [7]. Complementary approaches that incorporatelandmarks [39], map-constraints [27] and advanced humanlocomotion detection components [8], [21], [33], [42] wereproposed recently to deal with problems in legacy DR systems.GIFT borrows these ideas and improves localization accuracysignificantly.

III. MEASUREMENTS AND OBSERVATIONS

WiFi is the de facto standard for wireless Internet connectiv-ity, with its signals ubiquitously exist in indoor environments.Due to wireless propagation model and the unique interiorconstruction, indoor WiFi signals have been found to exhibitcertain characteristics, e.g., location-specific RSSI. Such char-acteristics have been leveraged to build fingerprint maps tolocalize user/device [1], [27], [48]. Before proposing our maindesign, we first present some measurement and observationresults on indoor WiFi signals.

A. Temporal Variation of WiFi Signals

Observations on the temporal variation of received signalstrength were reported in WiFi’s early days. With fixedtransmit power, broadcasting signals from a WiFi routerfluctuates in a time-variant indoor environment, e.g., thepresence and absence of humans, the fluctuating indoortemperature and humidity. In most localization literature whichleverages the RSSI fingerprint map, these factors are treated asrandom variables and could be eliminated through modelingor basic averaging.

Recently, with the advent of the high-density WiFi deploy-ment and extensive research on wireless communication, newtechniques are adopted in WiFi routers to meet the need ofpervasive and high-performance wireless access. For examplein the new 802.11ac standard, beamforming is proposed tofortify RF connections of the AP/client link, and could alsobe used to optimize the RF spectrum and the overall networkperformance. One typical optimization method used in off-the-shelf routers is to dynamically adjust the transmit power [15],[16] based on real-time wireless LAN conditions. Unlike the

random impacts mentioned above, wireless signal strengthvariations caused by such modifications cannot be modeledeasily due to the rapid and unpredictable changes of trafficload, the number of communication links and communicationinterference, etc.

0 1 2 3 4 5 6

−65

−60

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−50

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−40

Time (hour)

RS

SI (

dBm

)

Position APosition B

(a) Absolute RSSI Value

3 5 7 9 11 13 15 170

0.1

0.2

0.3

0.4

RSSI (dBm)

Pro

babi

lity

(b) RSSI Gap

Fig. 1. Temporal Variation of WiFi signals

We conducted experiments to verify the impact of thechange of transmit power. Two smartphones 4m apart contin-uously scan and record WiFi RSSI for hours in an office room.There are no object movements around during the experiment.RSSI observations from the same AP are recorded and plottedin Figure 1(a), which shows the fluctuations of RSSI (withmaximum change of 6 dBm) caused by adaptive transmitpower adjustment. However, we find the RSSI gap betweentwo smartphones remains positive and relatively stable. InFigure 1(b), 81% RSSI differences between observations fromtwo devices are in the range between 9 dBm and 11 dBm.

B. Spatial-temporal Variation of WiFi Signals

We further examine the spatial-temporal variation of WiFisignals. In this experiment, one user carries a smartphone andwalks along a pre-defined path in an office environment atdifferent times of the day. The smartphone keeps recordingthe WiFi RSSI during user walking. We plot the results toexamine the difference among various traces.

0 30 60 90 120 150−90

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(a) Absolute RSSI Value

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Val

ue

Trace 1

0 30 60 90 120 150−1

0

1

Time (s)

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ue

Trace 2

(b) Gradient-based Map

Fig. 2. Local disturbance of WiFi signals

Figure 2(a) presents a high-dimensional perspective on thespatial-temporal variation of WiFi signals. A clear shift existsbetween two RSSI curves in Figure 2(a). This is because theAP slightly increases its transmit power to cover a larger areain the morning when few connections have been established(Trace 1). In fact, the gap in between two curves reflects thetemporal variation of RSSI at a fixed location (as presented inSection III-A).

Although absolute RSSI values vary in two traces, twotrends are similar. In Figure 2(b), we use a sliding window

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to capture the trend of absolute RSSI values. Specifically, ifthe difference between RSSI values at two ends of the windowis larger than a threshold δ (say 6 dBm), we generate an outputof 1 (or −1 when the difference is smaller than −δ). This way,the RSSI increase and decrease are represented by 1 and −1respectively in Figure 2(b), and a 0 is generated when thereis no obvious change. In Figure 2(b), we find outputs of twotraces are similar and more stable.

C. Human Body Effect and Device Diversity

−15 −10 −5 0 5 10 150

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(a) Human body effect

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Samsung Galaxy NexusSamsung GT−N8000Samsung SM−T310Samsung I9100

(b) Device diversity

Fig. 3. Human body effect and device diversity

Variations of RSSI are observed when the user is takingmeasurements while changing his/her orientation owing to thehuman body effect. In Figure 3(a), we measure the signalstrength towards four directions (e.g., East, West, North andSouth) at a fixed location, and compute the RSSI deviations(i.e., the difference between the observed value and the mean).We repeat the experiment at 20 randomly selected locationsin an office building. From Figure 3(a) we find a symmetricaldistribution of deviations centered at zero and in 79% of cases,RSSI deviations are less than 5 dBm though it could be as largeas 11. In GIFT, we use Gaussian estimation to model thissmall-scale variation and perform gradient map constructionthrough a statistical significance testing.

Fingerprint-based indoor localization systems also sufferfrom signal variation due to device heterogeneity. This isclearly demonstrated in Figure 3(b), which shows the RSSIof one AP along exactly the same path using different smart-phones. Compared with existing techniques for compensatingfor differences in RSSI values, GIFT leverages stable RSSIgradients without any signal strength transformations (e.g.,linear) among devices.

As a brief summary, the time-varying signal strength makesit rather challenging to explore the WiFi signal directly,e.g., using absolute value-based fingerprinting techniques. Thehuman body effect and device diversities further exaggerate theissue. Nevertheless, we find the spatial gradient signal strengthhas favorable properties to serve as a localization modality.

IV. GIFT OVERVIEW

In this section, we present an overview of GIFT. GIFTconsists of two main modules: the map construction and theGIFT engine. Figure 4 shows the architecture of GIFT.

First, a gradient fingerprint map (Gmap) is constructed.Different from existing absolute value-based fingerprint map,Gmap contains WiFi gradients of the indoor environment

Location-based service and applications

Extended particle filter

Gmap construction

Fingerprint map construction

Step detection

Motion detection

Pattern

recognition

We

igh

t u

pd

ate

Acc.

Gyro.

GIFT engine

WiFi

Map construction

Pa

rtic

le m

ov

em

en

t

Imp

ort

an

ce

sa

mp

ling

Fig. 4. GIFT architecture

and can be stored either on the cloud or pre-installed onthe clients (e.g., smartphones) before running GIFT. Gmap isderived from a traditional fingerprint map through significancetesting. This way, GIFT is compatible with various fingerprintmap building approaches including site-survey [1], [48],crowdsourcing [27] and other advanced calibration methodswith little human intervention [5], [46]. Gmap is used as inputreferences by the GIFT engine which further compares usermeasurements and the pre-established WiFi gradient samples.

The GIFT engine continuously estimates locations of theuser through a motion detection module and an extended parti-cle filter (EPF). The motion detection module recognizes usersteps and learns the stride length based on the observationsfrom the accelerometer and the gyroscope on smartphones.In addition, the accelerometer and the gyroscope are used tocorrect the noisy compass readings in indoor environment, thusproviding reliable walking directions. Motion detection resultsare then forwarded to the EPF along with the pre-establishedGmap. The location of the user, which is represented by a setof particles, is finally estimated and updated. We will detaileach of the techniques in subsequent sections, followed bylarge-scale system evaluation.

V. GRADIENT-BASED MAP CONSTRUCTION

In this section, we present the calibration procedure of GIFTthat includes a novel Gmap construction method.

A. Fingerprint collection

We generate Gmap from an absolute value-based fingerprintmap during the training phase of localization. Absolute value-based fingerprint map is universally used in indoor localizationsystems [1], [20], [27], [46], [48] with site survey [1], [48]being the main technique in fingerprint map construction.In spite of its reliability from standstill data collection, sitesurveying is strangled by the high cost on manpower and time.Efficient fingerprint map construction methods were proposedin literature during the past few years. For example in [46],human motions are exploited to connect radio fingerprints.GIFT builds a Gmap by adding an additional layer above the

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absolute value-based fingerprint map therefore is compatiblewith legacy fingerprint map construction approaches. Inaddition, due to the stability of the Gmap, cost of theperiodical fingerprint map calibration can be significantlyreduced. Without loss of generality, we adopt the basic sitesurveying to illustrate the construction of Gmap.

Denoting the two-dimensional indoor space by G, we firstdiscretize the area of interest, G, into grids and create pathswhich are composed of a series of consecutive grids basedon the floor plan (the bottom layer in Figure 5). For ease ofpresentation, we consider four basic walking directions (e.g.,up, down, left, right) with square grids. For a complex floorplan, hexagonal/octagonal grids can be leveraged to adaptiveto non-vertical paths. In order to minimize the small-scalevariations of RSSI caused by slightly movement of the user(order of wavelength, i.e., 12.5cm and 6cm in the 2.4GHzand 5GHz WiFi frequencies, respectively), we set the gridlength to 2m, in accordance with the general performanceof fingerprinting-based localization methods. Since the widthsof most aisles in typical indoor environments are less than3m, one grid suffices. For rooms and wider hallways, multipleparallel and crossing paths are considered.

During site surveying, the surveyor scans the ambient WiFisignal ng times at grid g ∈ G on a path and collects thesignal strength from kg access points. The correspondingmeasurement matrix Fg ∈ Rkg×ng is then used to build theabsolute value-based fingerprint map F (the middle layer inFigure 5).

Map discretization and a path

RSSI heat map on a path

Gmap

[2,3] [0,3] [0,3]

[3,2]

[0,1]

Fig. 5. Illustration of Gmap construction (with one AP)

B. t-test and Gmap construction

The basic idea of generating Gmap is comparison of RSSIvalues at neighboring locations and replacing the originalabsolute RSSI values with the corresponding gradients. Todetermine the difference between fingerprint values at differentlocations, we borrow the idea from the significance testing instatistics and adopt t-test.

A t-test is a statistical hypothesis test which determineswhether the null hypothesis (no difference between samplevalues) is to be rejected or accepted. Compared to otherstatistical hypothesis tests like z-test, t-test is more adaptableand is preferred for limited sample size with unknown standarddeviation. Since the RSSI measurements from a specific AP

follow a Gaussian distribution (as shown in Section III-C andreported in literature [26]), and ng is not large enough inthe training phase, t-test is a good means to compare twosets of fingerprint values so as to generate the gradient-basedfingerprint map.

Let F jg and F j

g′ denote two sets of RSSI samples from AP j

in the absolute value-based fingerprint map. Specifically, F jg =

{sg1, . . . , sgng} and F j

g′ = {sg′

1 , . . . , sg′

ng′} are signal strength

vectors collected at adjacent grids g and g′, respectively. Themean of F j

g and F jg′ are denoted by µ1 = F j

g and µ2 = F jg′ ,

respectively, and we test the following two-sided hypothesis

H0 : µ1 = µ2

H1 : µ1 = µ2.(1)

The t statistic can be calculated as

tj =µ1 − µ2√

Sµ1(Fjg , ng) + Sµ2(F

jg′ , ng′)

, (2)

where Sµ1(Fjg , ng) = S2

F jg/ng and Sµ2(F

jg′ , ng′) = S2

F j

g′/ng′ .

S2F j

g= 1

ng−1

∑(F j

g − µ1)2 and S2

F j

g′= 1

ng′−1

∑(F j

g′ − µ2)2

are the unbiased estimators of the variance of the two sets ofRSSI samples.

To test the significance, we set the significance level α = 0.1and the distribution of the test statistic can be approximated asan ordinary t distribution with the Degrees-of-Freedom (DoFs)

d.f. =(Sµ1(F

jg , ng) + Sµ2(F

jg′ , ng′))2

(Sµ1 (Fjg ,ng))2

ng−1 +(Sµ2 (F

j

g′ ,ng′ ))2

ng′−1

. (3)

Therefore, we can compute the cumulative distribution fort distribution with d.f. DoFs at the values in tj . A cumulativedensity which is greater than 1 − α indicates that the nullhypotheses H0 can be rejected with confidence (i.e., µ1 = µ2),and accepted (i.e., µ1 = µ2) otherwise. Note that although thecalculated cumulative density varies with different α, subtlevariations on the test results are observed when 0.05 ≤ α ≤0.15. In GIFT, these variations can be efficiently alleviatedthrough the extended particle filter due to its probabilisticnature.

On the basis of significance test, we compute the binarygradient of AP j between location g and g′ as

bj(g, g′) =

1, H0 is accepted,2, H0 is rejected and µ1 ≥ µ2,0, H0 is rejected and µ1 < µ2,

(4)

bj(g, g′) in Equation 4 characterizes the RSSI changesbetween neighboring grids and it is easy to prove b(g, g′) +b(g′, g) = 2 (e.g., if b(g, g′) = 0, then b(g′, g) = 2).The gradient-based fingerprint value Gmapg at position gcan be represented as a kg × 2 matrix which stores thebinary gradient values bj(g, g′) of kg different APs. Sinceb(g, g′) + b(g′, g) = 2, we only consider grid g′ which islocated either right above or to the left of grid g and includedthe corresponding two gradient values in Gmapg (i.e., the twocolumns) to reduce the size of the map.

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Finally, Gmap is obtained as a union of Gmapg as

Gmap =∪g

Gmapg, g ∈ G. (5)

A Gmap captures gradient values of RSSI from one specificAP is shown in Figure 5. Particularly, if the grid right above oron top of g is not on walking paths, we set the correspondinggradient value to 3.

VI. GIFT ENGINE

The GIFT engine consists of a motion detection module andan extended particle filter.

A. Motion detection

This module recognizes user mobility pattern and steps us-ing sensor data from the IMU, i.e., accelerometers, gyroscopesand compass. In GIFT, we assume the user is holding thesmartphone steady in his hand when requesting his location,which is a likely indoor localization scenario.

User mobility pattern recognition, serving as an input ofthe motion model in the extended particle filter, detectswhether the user is standing still, walking on level surfaces orwalking up/down stairs. For motion detection, we first divideaccelerometer data into several segments, then extract featuresfrom three-axes accelerometer readings contained withineach segment and adopt a decision tree-based classificationalgorithm. Specifically, the algorithm firstly decides whetherthe user is moving. If so, it then determines if the user iswalking on level surfaces or walking up/down stairs. Afterthat, different parameters (e.g., thresholds) are used to helpextract steps.

The duration of each segment is set to 3s, enough to capturemultiple steps involved in activities. Features we use includethe average acceleration, standard deviation of acceleration andthe average absolute difference between each reading and theaverage value. An example of accelerometer’s Z-axis readingsis shown in Figure 6(a) where we can easily differentiate theoutput of walking on level surfaces from walking up stairs.Specifically, the standard deviation and the average absolutedifference of walking up stairs is almost 3x higher than theoutput during level walking (3.39, 3.06 vs. 1.21, 0.96).

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(a) Level walking vs. walking up stairs

Up_max

Down_max

False peak

valley

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Down_minFalse valley

peak

(b) Peak detection

Fig. 6. Accelerometer readings during walking

If the user is standing still, GIFT holds the currentlocation estimation, otherwise it will be continuously runningthe step detection algorithm based on the accelerometerreadings. Previously-developed algorithms can reliably detect

user steps [27], [28]. GIFT borrows these algorithms with afew minor adjustments. First, in the step detection algorithm, alow pass FIR digital filter is used to remove the high frequencynoise and spikes in raw acceleration readings. Since the stepfrequency in indoor environments mostly ranges from 1Hz(slow walk) to 3Hz (walking briskly or running) [25], we setthe cut-off frequency of the filter to 3Hz. After pre-processing,a peak detection algorithm is applied on the filtered signal toidentify steps.

Four thresholds are used in the algorithm to detect the peakand valley. Upmin and Downmin are used to remove theinterference of small peaks and valleys caused by a slightsways of the phone. Similarly, Upmax and Downmax are usedto remove the interference of heavy bouncing of the phone.Here, we use the same value used in [34] to set the threshold.Once the duration of one candidate step is smaller than 0.3sor larger than 1s, we consider it as a false step. Figure 6(b)shows an example of filtered accelerometer readings withautomatically detected peaks. Putting all these componentstogether, our step detection algorithm achieves a 3% falsepositive rate and 2% false negative rate.

B. Extended particle filter

All of the motion detection results are combined into anextended particle filter (EPF) to estimate the user’s location.Particle filtering, also known as Monte Carlo methods, iscommonly used in tracking-based localization systems [14],[27], [31], [35]. In this paper, particles are uniformly driven bythe fused direction sensing results and weights of particles areupdated during the movement based on the comparison resultsbetween WiFi RSSI observations and Gmap. User locationis simultaneously estimated via a probability distribution ofparticles with different weights. In what follows, we describethe details of particle initialization, movement, weight updateand importance sampling.

1) Particle initialization and the motion model: In EPF,the state of a particle pi is a 4-tuple consisting of its currentlocation li, weight wi and two stride length coefficients ai andbi. At the very beginning of the localization process with noa prior knowledge of the user’s initial position, N particlesare generated and uniformly distributed on all paths with anequal weight wi =

1N , i ∈ [1, N ].

After initialization, all particles move once a step is detectedby the motion detection module. In each movement, EPFfirst estimates the stride length of the user and then updatesthe location of each particle toward the moving direction.Although stride length varies with users and is affectedby walking speed, a linear function of step frequency isadaptive to capture the variation [21]. In EPF, we define thestride length sli = aif + bi where f is the step frequencyobtained by the motion detection module and ai, bi are twocoefficients that needed to be estimated. Each particle i inEPF is associated with a fixed pair of a, b (i.e., ai, bi). Duringinitialization, values of ai and bi are set based on a genericstride length model where ai and bi are sampled from aGaussian distribution with means of 2 and 4 and variance of0.8 and 0.4, respectively. A non-zero variance is introduced

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here to capture the possible error of stride length estimation.Note that similar values of ai and bi can still be applied due tothe randomness of particle moving distance. Via importancesampling (discussed in Section VI-B2), EPF eliminates thoseparticles with improper stride length coefficients, and thusestimates location and stride length simultaneously as the usercontinues walking.

The moving direction of particles is primarily generatedbased on the absolute heading direction readings reportedby the smartphone. It is often a fusion result from compassand gyroscope. In consideration of the magnetic interferers inindoor environments, we adopt the Iterative Magnetic Trian-gulation method [28] (assuming a single ambient interferer)to further correct the raw direction sensing results1.

2) Weight update and importance sampling: As mentionedabove, particles i, i ∈ [1, N ] move for sli toward the user’smoving direction once a step is detected. At the same time,GIFT periodically scans ambient WiFi signals with a fixedfrequency fs. A sequence diagram of EPF is shown inFigure 7.

t

Outside EPF

Inside EPF

WiFi scan

User step

Particle movement

Comparison and importance sampling

Fig. 7. Illustration of particle movement and comparison

Two series of actions can be found in Figure 7: user stepsand WiFi scans. Unlike WiFi scanning, the frequency of stepschanges with time. Similarly, in EPF, particle movement istriggered by steps while the gradient calculation, weight updateand importance sampling are conducted on a per-scan basis.2

Gradient calculation: The EPF first performs RSSI com-parison between two consecutive WiFi scans, based on whichweights of particles are updated. Let Rg and Rg′ denoteRSSI samples of two consecutive scans, then RSSI difference(dissimilarity) djgg′ between two scan results is computed asdjgg′ = ∥Rj

g − Rjg′∥1 where ∥ · ∥1 is the Euclidean distance

between two RSSI values and j is the AP included in both Rg

and Rg′ . The EPF then computes bj0, the binary gradient of APj between Rg and Rg′ based on the calculated dissimilaritydjgg′ . Specifically, if djgg′ is greater than a pre-defined thresholdδ, then the binary gradient bj0 = 2, if dj ≤ −δ, bj0 = 0,else bj0 = 1. δ is determined based on fingerprint samplescollected at a given location. In order to avoid the small-scale RSSI variations, we set δ to 6 in GIFT based onthe observation of Figure 3(a). Similar solutions have beenadopted elsewhere [39], [46] as well.

Weight update and importance sampling: After obtainingthe binary gradient values of all APs after each scan,

1The key intuition of IMT is to decompose the measured magnetic vectorinto the earth’s magnetic vector (G) and the interference vector (I), and adjustthe direction of G until all the I vectors intersect at the same point

2Particularly, if the user is standing still, EPF holds without weight updateand importance sampling.

EPF updates weights of particles based on their similaritiesbetween calculated gradient values and the pre-installedGmap. Specifically, for particle pi located at position li, EPFfirst extracts Gmapli and compares it with the gradient valueb0. Note that particles may be located at non-adjacent gridsduring two consecutive WiFi scans, and hence we replaceGmapli with the average value Gmapg across the entiretraveling grids g of particles between two WiFi scans. Then,Euclidean distance is calculated between Gmapli and b0, and

the weight of particle is set to wi = e−(∑

j ∥Gmapjli

−bj0∥1)2

2σ2

where j refers to the shared AP between Gmapli and b0,and σ is a parameter that reflects the overall disturbanceintensity of WiFi gradient. In addition, when the particlesare moving towards the walls according to the directionsensing result, their weights will be significantly reduced (butnot eliminated). Finally, weight-based importance samplingof particles is conducted. Since the distribution of particlesreflects the likelihood of the real location of the user, theestimated location is set to the weighted averaged point ofall particles’ positions using their own weights.

VII. EVALUATION

We have implemented GIFT on Android platforms andevaluated it in a five-story office building with a testingarea of about 8000m2. Detailed evaluation settings andimplementation are described in Section VII-A. Section VII-Bpresents experimental results of GIFT in environments withboth relatively stable and dynamic WiFi signals. Localizationperformance of GIFT on multiple devices is presented inSection VII-C.

A. Implementation

• Settings: We implemented GIFT on the Android platformincluding a tablet and several smartphones. A screenshotof the GIFT application is shown in Figure 8. Userscan adjust the zoom level to change the size of theindoor floor plan in the middle of the screen. GIFT offersseveral modes: the map construction mode to guide theuser to collect values on each path on the map and thelocalization mode to test the system accuracy.

• Map construction mode: Before running GIFT, we firstbuilt the RSSI fingerprint map F as well as Gmap.Specifically, once the construction mode is chosen, pathsare shown on the screen and the user can choose eachpath by clicking on it. During user walking, GIFTautomatically collects RSSI data on each path. It recordstimestamps at both the beginning and end of a walk andmaps the fingerprints onto the floor plan through a simplelinear interpolation. Gmap is then generated followingthe procedures presented in Section V.

• Localization mode: In the localization mode, the mobileclient performs continuous IMU sampling at 50Hz, WiFiscan at 1Hz, and updates the estimated location of theuser through the online EPF. In our experiment, we setthe number of particles to 2000. In order to obtain groundtruth positions of the user, we set landmarks every other

8

Fig. 8. A screenshot of GIFT application

meter along the path, and users tap on the screen to recordtimestamps when they pass by each landmark.

• Micro-benchmark: We use two classical fingerprint-based localization approaches, Radar [1] and Horus [48],and a particle filtering-based tracking method [31] asbenchmarks in experiments. Radar [1] is an earlyfingerprinting system which uses a deterministic RSSIfingerprint for each location, whereas Horus [48] adopts aprobabilistic RSSI map and a maximum likelihood basedapproach. Although new fingerprinting techniques havebeen proposed in recent years, they either focus on thepre-localization training process [46] or achieve similar(or slightly worse [5]) positioning accuracy to these twolegacy methods. To further evaluate the performanceof using gradient map, we also implemented a particlefiltering-based tracking method [31], which takes thefingerprint map of absolute value of RSSI and floorplan as inputs. Step detection algorithm in GIFT is alsoleveraged for particle movement and weight updating.We collected more than 300 walking traces and ignorelocalization results in the first 3 seconds of each trace dueto the time particles spend to converge. Location requestswere randomly made in Radar and Horus during eachwalking trace.

B. Performance in Static and Dynamic Environments

We first conducted experiments in a static environment withcustomized deployment of early WiFi APs with fixed transmitpower.

Cumulative distribution function (CDF) of localization errorof all traces is shown in Figure 10(a). We can see thatGIFT, Horus and WiFi RSSI-based particle filtering methodachieve comparable performance in terms of localizationerror, outperforming the localization accuracy of Radar. Forexample, the 80 percentile accuracy of GIFT, WiFi particlefiltering, Horus and Radar are 3.6m, 2.9m, 3.3m and 5.4m,respectively.

To further examine the accuracy and robustness of the fouralgorithms, we tested them in a dynamic environment withpre-deployed, ARM-enabled WiFi APs. To verify the RSSIvariations in the dynamic environment, we collected RSSI ofmultiple APs at a fixed location for 4 hours, and aligned allmeasurements by removing the mean RSSI value of each APduring the monitoring period. The CDF of RSSI variations ofall APs is shown in Figure 9 in which an RSSI difference

−10 −5 0 50

0.2

0.4

0.6

0.8

1

RSSI Variation (dBm)

CD

F

Fig. 9. RSSI variation in a dynamic environment

of as high as 9dB can be observed. In this experiment, webuilt Gmap in the morning and ran localization systems inthe afternoon after one week.

0 4 8 12 16 200

0.2

0.4

0.6

0.8

1

Error (m)C

DF

GIFTHorusRadarWiFi, with PF

(a) Static WiFi environment

0 4 8 12 16 200

0.2

0.4

0.6

0.8

1

Error (m)

CD

F

GIFTHorusRadarWiFi, with PF

(b) Dynamic WiFi environment

Fig. 10. Localization performance

Compared to Figure 10(a), we find localization performanceof all four systems degrades in Figure 10(b). This is owing tothe fact of fluctuant transmit power of WiFi APs. However,GIFT is more reliable in the dynamic environment due to thegradient-based fingerprinting. For example, in Figure 10(b),the 80 percentile accuracy of Horus is 6.58m, which hasa 99.4% performance degradation over that in the staticenvironment. However for GIFT, the performance degradationof the 80 percentile accuracy is only 55.6% (5.6m vs. 3.6m).

C. Robustness among Different Devices and Users

To examine GIFT’s robustness, we conducted experimentsusing heterogeneous devices and tested GIFT among multipleusers with different heights that may have various stridelengths and walking speeds. All experiments were conductedin the static environment. The CDF of the localization errorfor four devices and three users are plotted in Figures 11(a)and 11(b), respectively. From both figures we can see thatthe CDF curves are very close, and they are consistent withthe experimental results in Section VII-B. This demonstratesthe robustness and practicality of GIFT. On the contrary,fingerprint-based localization approaches (e.g., Radar andHorus) yield a much worse accuracy while localizing a newmobile device that has never been used to generate the model.We refer [5], [22], [38] for detailed evaluation results.

We also found several practical issues and limitationsof GIFT during evaluation. For example, as GIFT usescompute-intensive particle filter as the localization engine,the energy consumption of the system is relatively high. Inour experiments, the runtime current of Samsung GT-N8000reaches as high as 840 mA when the particle filter is running.

9

0 5 10 15 20 250

0.2

0.4

0.6

0.8

1

Error(m)

CD

F

Samsung GT−N8000Samsung I9100Samsung SM−T310Samsung Galaxy Nexus

(a) Different devices

0 5 10 15 20 250

0.2

0.4

0.6

0.8

1

Error(m)

CD

F

User #1: H1.85mUser #2: H1.77mUser #3: H1.72m

(b) Different users

Fig. 11. Results from different devices and users

Therefore, it is worth investigating how to reduce the energyconsumption while enjoying the benefits of using gradientmaps.

VIII. CONCLUSION

In this paper, we propose GIFT, an indoor localizationsystem leveraging gradient-based fingerprint map. We firstpresent key observations of indoor WiFi signals and thenpresent the design of GIFT based on the robust gradient-basedRSSI map. Gradient-based map is a sub-layer compatible withvarious wireless networks. Compared to legacy fingerprintingmethods, GIFT is more adaptive to the time-variant indoorwireless signals and device heterogeneity, and therefore couldeffectively reduce the cost of maintenance of the fingerprintmap. We implemented GIFT on off-the-shelf devices andextensively evaluated its performance in indoor environmentswith dynamic WiFi signals. Our experimental results confirmthe high accuracy and robustness of GIFT.

ACKNOWLEDGEMENT

The work was partially supported by 973 Program un-der grant 2015CB352503, NSFC under grant U1401253,61473251, 61429301, and National Program for SpecialSupport of Top Notch Young Professionals. The correspondingauthor of this paper is Jiming Chen.

REFERENCES

[1] P. Bahl and V. N. Padmanabhan. RADAR: An In-Building RF-BasedUser Location and Tracking System. In IEEE INFOCOM, 2000.

[2] J. Chen, J. Li, and T. Lai. Trapping Mobile Targets in Wireless SensorNetworks: An Energy-Efficient Perspective. IEEE Transactions onVehicular Technology, 62(7):3287–3300, September 2013.

[3] Y. Chen, D. Lymberopoulos, J. Liu, and B. Priyantha. FM-based indoorlocalization. In ACM MobiSys, 2012.

[4] X. Cheng, A. Thaeler, G. Xue, and D. Chen. TPS: a time-basedpositioning scheme for outdoor wireless sensor networks. In IEEEINFOCOM, 2004.

[5] K. Chintalapudi, A. P. Iyer, and V. N. Padmanabhan. Indoor Localizationwithout the Pain. In ACM MobiCom, 2010.

[6] K. H. Choi, W.-S. Ra, S.-Y. Park, and J. B. Park. Robust Least SquaresApproach to Passive Target Localization Using Ultrasonic ReceiverArray. IEEE Transactions on Industrial Electronics, 61(4):1993–2002,April 2014.

[7] J. Chung, M. Donahoe, C. Schmandt, I.-J. Kim, P. Razavai, andM. Wiseman. Indoor Location Sensing Using Geo-magnetism. In ACMMobiSys, 2011.

[8] I. Constandache, R. R. Choudhury, and I. Rhee. Towards Mobile PhoneLocalization without War-Driving. In IEEE INFOCOM, 2010.

[9] I. Constandache, S. Gaonkar, M. Sayler, R. Choudhury, and L. Cox.EnLoc: Energy-Efficient Localization for Mobile Phones. In IEEEINFOCOM, 2009.

[10] F. Dong, Y. Chen, J. Liu, Q. Ning, and S. Piao. A Calibration-free Localization Solution for Handling Signal Strength Variance. InInternational Conference on Mobile Entity Localization and Tracking inGPS-less Environments, 2009.

[11] A. Eleryan, M. Elsabagh, and M. Youssef. Synthetic Generation ofRadio Maps for Device-Free Passive Localization. In IEEE Globecom,2011.

[12] A. Goswami, L. E. Ortiz, and S. R. Das. WiGEM : A Learning-BasedApproach for Indoor Localization. In ACM CoNEXT, 2011.

[13] A. Haeberlen, E. Flannery, A. Ladd, A. Rudys, D. Wallach, andL. Kavraki. Practical Robust Localization over Large-scale 802.11Wireless Networks. In ACM MobiCom, 2004.

[14] B. F. D. Hahnel and D. Fox. Gaussian Processes for SignalStrength-Based Location Estimation. In Robotics: Science and SystemsConference, 2006.

[15] A. N. Inc. Aruba 220 Series Access Points. http://www.arubanetworks.com/products/access-points/aruba-220-series-access-points.

[16] C. S. Inc. Cisco Aironet 1240AG Series Access Points. http://www.cisco.com/c/en/us/products/wireless/aironet-700w-series/index.html.

[17] N. Inc. Adaptive Radio Management. http://kb.netgear.com/ci/fattach/get/16/1236074685/redirect/1/filename/AdaptiveRadioManagement.pdf.

[18] S. J. Kim and B. K. Kim. Dynamic Ultrasonic Hybrid LocalizationSystem for Indoor Mobile Robots. IEEE Transactions on IndustrialElectronics, 60(10):4562–4573, Oct 2013.

[19] M. B. Kjæ rgaard. Indoor Location Fingerprinting with HeterogeneousClients. Pervasive and Mobile Computing, 7(1):31–43, Feb. 2011.

[20] B. Li, T. Gallagher, A. G. Dempster, and C. Rizos. How Feasible is theUse of Magnetic Field Alone for Indoor Positioning? In IPIN, 2012.

[21] F. Li, C. Zhao, G. Ding, J. Gong, C. Liu, and F. Zhao. A Reliable andAccurate Indoor Localization Method Using Phone Inertial Sensors. InACM UbiComp, 2012.

[22] L. Li, G. Shen, C. Zhao, T. Moscibroda, J. Lin, and F. Zhao. Expe-riencing and handling the diversity in data density and environmentallocality in an indoor positioning service. In ACM MobiCom, 2014.

[23] K. Lin, A. Kansal, D. Lymberopoulos, and F. Zhao. Energy-accuracyTrade-off for Continuous Mobile Device Location. In ACM MobiSys,2010.

[24] K. Liu, X. Liu, and X. Li. Guoguo: Enabling Fine-grained IndoorLocalization via Smartphone. In ACM MobiSys, 2013.

[25] A. Pachi and T. Ji. Frequency and Velocity of People Walking. StructuralEngineer, 83(3), 2005.

[26] J.-g. Park, D. Curtis, S. Teller, and J. Ledlie. Implications of DeviceDiversity for Organic Localization. In IEEE INFOCOM, 2011.

[27] A. Rai, K. K. Chintalapudi, V. N. Padmanabhan, and R. Sen. Zee:Zero-effort Crowdsourcing for Indoor Localization. In ACM MobiCom,2012.

[28] N. Roy, H. Wang, and R. Roy Choudhury. I Am a Smartphone and ICan Tell My User’s Walking Direction. In ACM Mobisys, 2014.

[29] S. Saad and Z. Nakad. A Standalone RFID Indoor Positioning SystemUsing Passive Tags. IEEE Transactions on Industrial Electronics,58(5):1961–1970, May 2011.

[30] G. Shen, Z. Chen, P. Zhang, T. Moscibroda, and Y. Zhang. Walkie-Markie: Indoor Pathway Mapping Made Easy. In USENIX NSDI, 2013.

[31] Y. Shu, C. Bo, G. Shen, C. Zhao, L. Li, and F. Zhao. Magicol:Indoor Localization Using Pervasive Magnetic Field and OpportunisticWiFi Sensing. IEEE Journal on Selected Areas in Communications,33(7):1443–1457, July 2015.

[32] Y. Shu, P. Coue, Y. Huang, J. Zhang, P. Cheng, and J. Chen. Demo: G-Loc: Indoor Localization Leveraging Gradient-based Fingerprint Map.In IEEE INFOCOM, pages 129–130, April 2014.

[33] Y. Shu, K. G. Shin, T. He, and J. Chen. Last Mile Navigation UsingSmartphones. In ACM MobiCom, 2015.

[34] M. Susi, V. Renaudin, and G. Lachapelle. Motion Mode Recognitionand Step Detection Algorithms for Mobile Phone Users. Sensors,13(2):1539–1562, 2013.

[35] I. Vallivaara, J. Haverinen, A. Kemppainen, and J. Roning. MagneticField-based SLAM Method for Solving the Localization Problem inMobile Robot Floor-Cleaning Task. In ICAR, 2011.

[36] A. Varshavsky, E. de Lara, J. Hightower, A. LaMarca, and V. Otsason.GSM Indoor Localization. Perv. Mob. Comput., 3(6):698–720, 2007.

[37] B. Wang, S. Zhou, W. Liu, and Y. Mo. Indoor Localization Basedon Curve Fitting and Location Search Using Received Signal Strength.IEEE Transactions on Industrial Electronics, 62(1):572–582, Jan 2015.

[38] C.-H. Wang, T.-W. Kao, S.-H. Fang, Y. Tsao, L.-C. Kuo, K. Shih-Wei,and N.-C. Lin. Robust Wi-Fi Location Fingerprinting against DeviceDiversity Based on Spatial Mean Normalization. In APSIPA, 2013.

10

[39] H. Wang, S. Sen, A. Elgohary, M. Farid, M. Youssef, and R. R.Choudhury. No Need to War-Drive: Unsupervised Indoor Localization.In ACM MobiSys, 2012.

[40] J. Wang, Q. Gao, Y. Yu, P. Cheng, L. Wu, and H. Wang. Robust Device-Free Wireless Localization Based on Differential RSS Measurements.IEEE Transactions on Industrial Electronics, 60(12):5943–5952, Dec2013.

[41] J. Wang, Q. Gao, Y. Yu, H. Wang, and M. Jin. Toward Robust IndoorLocalization Based on Bayesian Filter Using Chirp-Spread-SpectrumRanging. IEEE Transactions on Industrial Electronics, 59(3):1622–1629, March 2012.

[42] O. Woodman and R. Harle. Pedestrian Localization for IndoorEnvironments. In ACM UbiComp, 2008.

[43] K. Wu, J. Xiao, Y. Yi, M. Gao, and L. M. Ni. FILA: Fine-grainedIndoor Localization. In IEEE INFOCOM, 2012.

[44] P. Yang and W. Wu. Efficient Particle Filter Localization Algorithm inDense Passive RFID Tag Environment. IEEE Transactions on IndustrialElectronics, 61(10):5641–5651, Oct 2014.

[45] P. Yang, W. Wu, M. Moniri, and C. Chibelushi. Efficient ObjectLocalization Using Sparsely Distributed Passive RFID Tags. IEEETransactions on Industrial Electronics, 60(12):5914–5924, Dec 2013.

[46] Z. Yang, C. Wu, and Y. Liu. Locating in Fingerprint Space: WirelessIndoor Localization with Little Human Intervention. In ACM MobiCom,2012.

[47] S. Yoon, K. Lee, and I. Rhee. FM-based Indoor Localization viaAutomatic Fingerprint DB Construction and Matching. In ACMMobiSys, 2013.

[48] M. Youssef and A. K. Agrawala. The Horus WLAN LocationDetermination System. In ACM MobiSys, 2005.

Yuanchao Shu (S’12-M’15) received the Ph.D.degree from Zhejiang University in 2015. He iscurrently an Associate Researcher at MicrosoftResearch Asia. From 2013 to 2015, he was a jointPh.D. student in Computer Science at Universityof Michigan, Ann Arbor. He is the author and co-author of over 10 papers in premier journals andconferences, including IEEE JSAC, IEEE TMC,IEEE TPDS, ACM MobiCom, IEEE INFOCOM,and is the recipient of the IBM PhD Fellowship andthe INFOCOM’14 Best Demo Award. His research

interests include cyber-physical systems, mobile computing, wireless sensornetwork and big data analysis. He is a member of ACM and IEEE.

Yinghua Huang received the B.E degree in automa-tion from Shanghai jiaotong university, ShanghaiChina in 2012 and the M.S.degree in controlscience and engineering from Zhejiang University,Hangzhou, China, in 2015. He is currently a workingstaff of Huawei. His research interests includemobile social networks, delay-tolerant networks andmobile computing.

Jiaqi Zhang received the bachelor degree fromZhejiang University in 2013. He is currently amaster student with the Department of ControlScience and Engineering, Zhejiang University. Hisresearch interests include mobile computing andsensor network.

Philippe Coue received the Master degree inTelecommunication from Insa Lyon engineeringschool in 2013. He is currently a data intelligenceconsultant working all over Europe (Paris, Budapestand Copenhagen) on global transformation of inter-national telecom operator information systems. Hewas a joint student in the Department of Control atZhejiang University in 2013. He is the recipient ofthe INFOCOM’14 Best Demo Award. His researchinterests include mobile computing, wireless sensornetwork and big data analysis.

Peng Cheng (M’10) received the B.E. degreein Automation, and the Ph.D. degree in ControlScience and Engineering in 2004 and 2009 re-spectively, both from Zhejiang University, China.Currently he is an associate professor with theDepartment of Control Science and Engineering,Zhejiang University. He serves as Associate Editorfor Wireless Networks, International Journal ofDistributed Sensor Networks. He is also the guesteditor for IEEE TCNS. He served as publicity Co-Chair for IEEE MASS 2013 and Local Arrangement

Chair for ACM MobiHoc 2015. His research interests include networkedsensing and control, cyber-physical systems, and robust control.

Jiming Chen (M’08-SM’11) received B.Sc degreeand Ph.D degree both in Control Science andEngineering from Zhejiang University in 2000 and2005, respectively. He was a visiting researcher atINRIA in 2006, National University of Singaporein 2007, and University of Waterloo from 2008to 2010. Currently, he is a full professor withDepartment of Control Science and Engineering,and vice director of the State Key laboratoryof Industrial Control Technology and Institute ofIndustrial Process Control at Zhejiang University,

China. He currently serves associate editors for several international Journalsincluding IEEE Transactions on Parallel and Distributed System, IEEENetwork, IEEE Transactions on Control of Network Systems, etc. He wasa guest editor of IEEE Transactions on Automatic Control, etc. His researchinterests include sensor networks, networked control.

Kang G. Shin (L’F’12) is the Kevin & NancyO’Connor Professor of Computer Science in theDepartment of Electrical Engineering and ComputerScience, The University of Michigan, Ann Arbor.His current research focuses on QoS-sensitive com-puting and networking as well as on embedded real-time and cyber-physical systems. He has supervisedthe completion of 75 PhDs, and authored/coauthoredmore than 830 technical articles, a textbook andmore than 30 patents or invention disclosures, andreceived numerous best paper awards, including the

Best Paper Awards from the 2011 ACM MobiCom, the 2011 IEEE ICAC, the2010 and 2000 USENIX ATC, as well as the 2003 IEEE CommunicationsSociety William R. Bennett Prize Paper Award and the 1987 OutstandingIEEE Transactions of Automatic Control Paper Award. He has also receivedseveral institutional awards, including the Research Excellence Award in 1989,Outstanding Achievement Award in 1999, Distinguished Faculty AchievementAward in 2001, and Stephen Attwood Award in 2004 from The University ofMichigan (the highest honor bestowed to Michigan Engineering faculty); aDistinguished Alumni Award of the College of Engineering, Seoul NationalUniversity in 2002; 2003 IEEE RTC Technical Achievement Award; and 2006Ho-Am Prize in Engineering (the highest honor bestowed to Korean-originengineers). He was a co-founder of a couple of startups and also licensedsome of his technologies to industry.