adaptive data dissemination schemes for location-aware mobile services

www.elsevier.com/locate/jss

The Journal of Systems and Software 79 (2006) 674–688

Adaptive data dissemination schemes for location-awaremobile services

KwangJin Park *, MoonBae Song, Chong-Sun Hwang

Department of Computer Science and Engineering, Korea University 5-1, Anam-dong, Seongbuk-Ku, Seoul 136-701, Republic of Korea

Received 9 January 2005; received in revised form 5 August 2005; accepted 7 August 2005Available online 15 September 2005

Abstract

Broadcasting is the natural method of propagating information in wireless links, which guarantees scalability in the case of bulkdata transfers. It is particularly attractive for resource limited mobile clients in asymmetric communications. To facilitate powersaving via wireless data broadcast, index information is typically broadcast along with the data. By first accessing the broadcastindex, the mobile client is able to predict the arrival time of the desired data. However, it suffers from the drawback that the clienthas to wait and tune for an index segment, in order to conserve battery power consumption. In location-aware mobile services(LAMSs), it is important to reduce the query response time, since a late query response may contain out-of-date information. Thispaper proposes a new broadcast-based spatial query processing method, called BBS designed to support NN query processing. Inthe BBS, broadcasted data objects are sorted sequentially based on their locations, and the server broadcasts the location dependentdata along with an index segment. In this method, since the data objects broadcasted by the server are sequentially ordered based ontheir location, it is not necessary for the client to wait for an index segment, if it has already identified the desired data items beforethe associated index segment has arrived. The performance of this scheme is investigated in relation to various environmental vari-ables, such as the distributions of the data objects, the average speed of the clients and the size of the service area.� 2005 Elsevier Inc. All rights reserved.

Keywords: Index; Wireless data broadcasting; Mobile computing

1. Introduction

In today�s increasingly mobile computing world, peo-ple wish to be able to access various kinds of services atany time and in any place. With the increasing popular-ity of portable wireless computers, mechanisms whichallow information to be efficiently transmitted to suchclients are of significant interest. For example, in geo-graphical information systems, the mobile clients couldask for geographical information, such as, ‘‘what arethe names and addresses of the markets near to my cur-rent location?’’. In this case, the server periodicallybroadcasts reports, which contain the data items des-

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* Corresponding author. Tel.: +82 29240547; fax: +82 29530771.E-mail address: [email protected] (K. Park).

tined for the clients. In this way, the user obtains the ad-dresses of the markets from the broadcast channel.Location Dependent Information Services (LDISs) arean important class of geographical information services.LDIS is the ability to find the geographical location ofthe mobile device and provide services based on thislocation information. In the days to come, the LDISswill be benefiting both the consumers and network oper-ators. While the consumers will have greater personalsafety, more personalized features and increased com-munication convenience, the network operators will ad-dress discrete market segments based on the differentservice portfolios. Fig. 1 shows three-services areas witha wireless location model. Suppose that the nearby res-taurants are A and B for service area 1, the nearby res-taurants are B and C for service area 2 and the nearby

mailto:[email protected]

Service Area 1 Service Area 2 Service Area 3

Restaurant A

Restaurant B

Restaurant C

Restaurant D

Fig. 1. Location dependent information services.

K. Park et al. / The Journal of Systems and Software 79 (2006) 674–688 675

restaurant is D for service area 3. That is, if the clientwants to find the nearby restaurant form service area1, the answer is A and B, the nearby restaurant form ser-vice area 2 is B and C and the nearby restaurant formservice area 3 is D.

Broadcasting is the natural method of propagatinginformation in wireless links, which guarantees scalabil-ity in the case of bulk data transfers. In broadcast envi-ronments, the server repeatedly sends information to theclients, without them having to send an explicit request(Acharya et al., 1995; Acharya and Franklin, 1995). Anynumber of clients can monitor the broadcast channeland retrieve the data as it arrives on the broadcast chan-nel. If the data is properly organized to cater to theneeds of the clients, such a scheme makes effective useof the low wireless bandwidth and is ideal for achievingmaximal scalability. Two key requirements for dataaccess in wireless environments are the conservation ofpower and the minimization of the client waiting time.In push-based systems, the mobile clients must wait untilthe server broadcasts the desired information. There-fore, the client waiting time is determined by the overalllength of the broadcast data (Xu and Athman, 2002).

One critical issue for mobile device is the consump-tion of battery power. With increasing emphasis andrapid development on energy conserving functionality,mobile device can switch between doze (power save)mode and active mode in order to conserve energy con-sumption. Air indexing is one of techniques used toaddress this issue, and which operates by interleavingindexing information among the broadcast data items.At the same time, the client device can reduce its batterypower consumption through the use of selective tuning(Imielinski et al., 1994, 1997). This method allowsmobile clients requesting data to tune into a continuousbroadcast channel only when spatial data of interest andrelevance is available on the channel, thus minimizingtheir power consumption.

(1,m) index is one of the famous air index technique.In this method, the index is broadcast m times during asingle broadcast cycle. The broadcast index is broad-casted every fraction ð1

mÞ of the broadcast cycle. In orderto reduce the tuning time, each index segment and eachdata contains a pointer pointing to the root of the nextindex. In case of (1,m) indexing, selective tuning is

accomplished by multiplexing an index with the dataitems in the broadcast. The clients are only required tooperate in active mode when probing for the addressof the index and downloading the required data items,while spending the waiting time in power save mode.Air indexing techniques can be evaluated in terms ofthe following factors:

• Access Latency: The average time elapsed from themoment a user issues a query to the client to themoment when the required data item is received bythe client.

• Tuning Time: The amount of time spent by a clientlistening to the channel.

The Access Latency consists of two separate compo-nents, namely:

• Probe Wait: The average duration for getting to thenext index segment. If we assume that the distancebetween two consecutive index segment is L, thenthe probe wait is L/2.

• Bcast Wait: The average duration from the momentthe index segment is encountered to the momentwhen the required data item is downloaded.

The Access Latency is the sum of the Probe Wait andBcast Wait, and these two factors work against eachother (Imielinski et al., 1994, 1997).

As stated above, power consumption can be reducedby interleaving auxiliary index with data. However, theproblem which we have to consider is the worst possiblelatency, because the clients have to wait until the begin-ning of the next broadcast cycle, even if the desired datais just in front of them. Let us consider two cases inwhich the server broadcasts 12 data items and the clientis desired to obtain the data item 9 and begins to tunethe broadcast channel at T1 as shown in Figs. 2 and 3,respectively. The first case, the server broadcasts dataitems with an index segment as shown in Fig. 3(a). Inthis case, the client has to wait until the next broadcastcycle, since the client has to wait for an index, even thedesired data item 9 is broadcasted in the current broad-cast cycle. As shown in this figure, in this case, Access

Latency = 19(Probe Wait 10 + Bcast Wait 9) withoutan index. The second case, the server broadcasts dataitems without an index segment as shown in Fig. 3(b).In this case, the data objects broadcasted by the serverare sequentially ordered based on their location andthus, it is not necessary for the client to wait for an indexsegment, if it has already identified the nearest object be-fore the associated index segment has arrived. As shownin this figure, in this case, Access Latency = 7(Probe

Wait 0 + Bcast Wait 7).In general, the fastest access time in a broadcast cycle

is obtained when there is no index, but this increases the

y

x

O3

O1

O2 O4

O5

O7

O9

O10

O11

O12

Query

O6

O8

Fig. 2. Example of data distribution.

676 K. Park et al. / The Journal of Systems and Software 79 (2006) 674–688

Tuning Time. On the other hand, increasing the numberof index segments in a single broadcast cycle reduces theTuning Time, but increases the Access Latency (Imielin-ski et al., 1994, 1997). Therefore, the number of indicesin the broadcast cycle has to be optimized by taking intoconsideration the trade off between the Tuning Time andthe Access Latency. Consequently, in order to achieveefficient indexing on air, it is necessary to simultaneouslyminimize both the Tuning Time and the Access Latency,

index 1 2 5 7 10 11 129 inde

1 3 6 7 9 11 1210 2

10

Index 12 34

5678910

11

12

Broadcast cycle

(a) Access sequence = {3, 4, 5, 6, 7, 8, 9, 10, 11, 12, inde

(a)

(b) Access sequence = {3, 4, 5, 6, 7, 8, 9} and total = 7

T1

T1

3 4 6 8

2 4 5 8 1

Fig. 3. Access Latency of (a)

by adjusting the number of indices in the broadcastcycle. There are several indexing techniques which havebeen developed, such as the distributed indexing ap-proach (Imielinski et al., 1997), the signature approach(Lee and Lee, 1996), and the hybrid approach (Huet al., 2001).

In this paper, we attempt to reduce both the Tuning

Time and Access Latency in the wireless environment.To accomplish this, firstly, we introduce the concept ofdata sorting for broadcasting. Then, we present a selec-tion algorithm designed to optimize either the Tuning

Time or the Access Latency. After pointing out the lim-itations of the existing indexing schemes, we present var-ious schemes that can overcome these problems. In thispaper, we assume a geometric location model, i.e., alocation is specified as a two-dimensional coordinateand the broadcasted data objects are static, such as res-taurants, hospitals, and hotels. The mobile clients canidentify their locations using systems such as the GlobalPositioning System (GPS). The remainder of the paper isorganized as follows: Section 2 provides backgroundinformation on the index model and cache maintenancescheme. Section 3 describes the proposed BBS schemeand prefetching method. A performance analysis andevaluation are presented in Section 4 and 5. Finally,Section 6 concludes this paper.

2. Background

With the advent of high speed wireless networks andportable devices, data requests based on the location of

2 4 5 7 9 10 11 12x 1

5 7 8 10 11 1243

10

12 34

56

7891011

12

Broadcast cycle

x, 1, 2, 3, 4, 5, 6, 7, 8, 9} and total = 20

(b)

3 6 8

6 9

index vs (b) no index.

previousbroadcast

nextbroadcast

direction of the broadcast

previousbroadcast

nextbroadcast

direction of the broadcast

data

data

index

(a)

(b)

Fig. 4. One-dimensional optimal methods: (a) optimal latency, (b) optimal tune.


mobile clients have increased in number. However, thereare several challenges to be met in the development ofLDISs, such as the constraints associated with the mo-bile environment and the difficulty of taking the user�smovement into account (Lee et al., 2002). Hence, vari-ous techniques have been proposed to overcome thesedifficulties.

2.1. Data on air

The broadcasting of spatial data together with an in-dex structure is an effective way of disseminating data ina wireless mobile environment (Xu et al., 2003, 2004;Chen et al., 2003). This method allows mobile clientsrequesting data to tune into a continuous broadcastchannel only when spatial data of interest and relevanceis available on the channel, thus minimizing their powerconsumption. The use of broadcasting methods thatproperly interleave index information and data on thebroadcast channel can significantly improve not onlythe energy efficiency, but also the Access Latency. LetData be the number of data objects and C be the down-load time for the required records. There are two param-eters that need to be optimized in the one-dimensionalspace of the Access Latency and the Tuning Time asshown in Fig. 4 (Imielinski et al., 1994, 1997):

• Optimal Latency: The best latency is obtained whenno index is broadcast along with the data. In thiscase, the size of the entire Bcast is minimized, andthe average latency time is Data

2þ C. However, in this

case, the worst value of the average Tuning Time isobtained, since it is equal to Data

2þ C, as shown in

Fig. 1(a).• Optimal Tune: This parameter allows the best Tuning

Time to be obtained, while simultaneously increasingthe Access Latency. The server broadcasts the indexat the beginning of each Bcast, as shown inFig. 1(b). In this case, the Tuning Time is 1 + k +

C, where k is the number of levels in the multileveledindex tree. In this case, the probe wait is equal toDataþindex

2and the Bcast wait is equal to Dataþindex

2þ C.

Since the Latency is the sum of the Probe Wait andthe Bcast Wait, the average latency time is equal toðDataþindex

2Þ � 2þ C ¼ Dataþ indexþ C.

2.2. Air index replication scheme

In this scheme (Chung and Kim, 2000), authors pres-ent three energy-efficient index replication approaches,FL, NL, and SL, that are based on three criteria: acces-sibility, energy efficiency and adjacency. In the forwardlink (FL) approach, authors use forward address. Theforward address means that it is the address in the nextbroadcast rather than that in the current broadcast. Thenephew link (NL) approach adopts nephews as linkpointers when replicating control index buckets. The ne-phew of a control index is the children of its next sibling.The sibling link (SL) approach uses the pointers to thesibling nodes instead of inaccessible ones. The proposedschemes reduce the waste of index space on wirelesschannel and gives Tuning Time reduction.

2.3. LAMSs schemes

In (Gedik et al., 2004), authors present algorithms toadapt and KNN search on R-tree family, instead ofintroducing another indexing structure. Furthermore,they investigate the use of histograms as a techniqueto improve tune-in time and memory requirement ofKNN search. In (Zheng et al., 2004), a linear indexstructure is proposed based on the Hilbert curve, inorder to enable the linear broadcasting of objects in amulti-dimensional space. They address the issues in-volved with organizing location dependent data andanswering spatial queries on air. However, the Hilbertcurve needs to allocate a sufficient number of bits to


represent the index values, in order to guarantee thateach of the points in the original space has a distinctvalue. If k is the number of bits used for a coordinatein the ith dimension of the targeted m-dimensional spaceand n is the number of bits assigned to represent thecoordinates, then a total of

Pmi¼1k bits need to be allo-

cated to represent the coordinates and the expected timefor the conversion is O(n2). Besides, with this scheme, inorder to identify the sequence of the broadcast dataitems, the clients have to wait until the index segmentis arrives, even if the desired data is just in front of them.In (Zheng et al., 2003), authors discuss the specific char-acteristics of broadcast environments and conclude thatexisting index structures are unsuitable for wirelessbroadcast environment, since the most of the previousstudies do not consider the time-series characteristicsof the air index. Authors address the issues involvedwith organizing location dependent data and answeringK-nearest neighbor queries on air. Then, a new indexstructure, called sorted list is proposed to enable a lineartransmission of location dependent data and processingof KNN queries. However, they only provide approxi-mate search techniques. In (Hambrusch et al., 2001),authors present techniques for scheduling a spatial indextree for broadcast in a single and double channel envi-ronment. The algorithms executed by the clients aimto minimize latency and tuning time.

In a mobile computing environment, caching data atthe client�s side is an important technique to improveperformance. However, frequently disconnection andmobility of the clients may cause cache inconsistencyproblems. In (Xu et al., 2003), they propose locationdependent cache invalidation schemes for mobile envi-ronments. In this scheme, they use bits to indicate thatthe data item in the specific area has been changed.For instance, if there are eight service areas and the val-ues of the bit vector are 00010011, it represents that thedata item is valid in 4th, 7th, and 8th only. Then, theyorganize each service area as a group in order to reducethe overhead for scope information. In (Zheng et al.,2002), they proposed a PE (Polygonal Endpoint) andAC (Approximate Circle) schemes. The PE scheme re-cords all the endpoints of the polygon representing thevalid scope, and the AC scheme uses an inscribed circlefrom the polygon to represent valid scope of data. Thismeans that a valid scope can be estimated by informa-tion of the center of the inscribed circle and the radiusvalue. Compare to the PE, the AC scheme reduces theamounts of information. However, the AC schememay lead lower cache hit ratio since the cache will incor-rectly treat valid data as invalid if the query location isoutside the inscribed circle but within the polygon.Hence, they introduce a new performance criterion,called CEB (Cache Efficiency Based scheme), for balanc-ing the overhead and the precision of a representationscheme.

3. Proposed algorithms

In this section, we describe two schemes for LDIS.We first introduce the broadcast-based LDIS scheme(BBS). In this scheme, the server broadcasts reportswhich contain the IDs of the data objects (e.g., buildingnames) and the values of the location coordinates. Thedata objects broadcast by the server are sorted basedon the locations of the data objects. Then, we presenta data prefetching scheme and OBC (Object BoundaryCircle), in order to reduce the client�s Tuning Time. Inthe previous work (Acharya et al., 1995; Acharya andFranklin, 1995), groups of pages, such as hot and coldgroups, with different broadcast frequencies are multi-plexed on the same channel based on their access fre-quencies. Then, data items that are selected as hotgroups are broadcasted more frequently than others.In this paper, we assign the different broadcast frequen-cies based on the distance between the data objects.There are two parameters in which to select the hot datagroup and as follows:

• min OBC: the data object which has the shortestdistance among the data objects is picked as hotdata group,

• max OBC: the data object which has the longestdistance among the data objects is picked as hotdata group.

3.1. Problem description

Indexing provides for selective tuning, but suffersfrom the drawback that the client has to wait and tunefor an index segment, in order to limit battery powerconsumption (Imielinski et al., 1997). Let�s considerthe two scenarios of the mobile client for LAMSs: con-serving power consumption and minimize the latency:

Case 1: Let us consider the case of a user who drives acar and wants to find the NN (nearest neighbor) with amobile unit such as a car navigation system, e.g. he orshe wants to find the nearest gas station to his or hercurrent location. Here, the client does not consider theproblem of the battery power consumption, since thevehicle can be used as a source of electric power. Thus,in this case, energy consumption is not an importantissue. Moreover, the user wants to obtain the fastestpossible answer, since a late query result may result inthe answer being incorrect.

Let the size of each data item be 1024 bytes and thebroadcast bandwidth of the server be 14.4 kbps. Then,it takes about 0.071 s for each data item to be received.Let us assume that the user drives his or her car at aspeed of 100 km/h. That is, the average speed is about22.7 m/s. Let the number of broadcast data items be1000. Then, the average time need to obtain the desired


data items along with the accompanying index is about70 s. Since the average Access Latency in the optimal la-tency case is Data + index + C, the average distancetraveled while receiving the desired data items is about1.5km. Fig. 5 illustrates this example. Let us supposethat the client issues a query at location A, such as ‘‘findthe nearest gas station to my current location’’, using hismobile device. Now, the client begins to tune the broad-cast channel and obtains the desired data item contain-ing gas station A after 70 s. However, during this time,the client has already moved to location B (1.5 km farway from location A) and, consequently, the answermay no longer be valid. In this case, the optimal latencyis required to reduce the latency.

Case 2: Let us consider the case of a user who walksalong the street and wants to find the NN with his or hermobile unit, such as a PDA, e.g. he or she want to findthe nearest restaurant to his or her current location.Here, the user�s PDA has low battery power and, thus,it is necessary to minimize the battery power consump-tion. In this case, the optimal tuning is required.

In this paper, we aim to provide research directionstowards minimizing both the Tuning Time and Access

Latency for the NN (Nearest Neighbor)-query process-ing [14] (Roussopoulos et al., 1995).

3.2. Data organizing

In the BBS method, the server periodically broadcaststhe IDs and coordinates of the data objects, along withan index segment, to the clients, and these broadcasteddata objects are sorted sequentially, according to thelocation of the data objects, before being sent to the cli-

Fig. 5. Example of w

ents. In the BBS method, since the data objects broad-casted by the server are sequentially ordered based ontheir location, it is not necessary for the client to waitfor the index segment, if the desired data object is ableto be identified before the associated index segmenthas arrived. In this method, the structure of the broad-cast affects the distribution of the data object. TheBBS provides the fastest access time, since the size ofthe entire broadcast cycle is minimized.

A simple sequential broadcast can be generated bylinearizing the two dimensional coordinates in two dif-ferent ways: i.e. horizontal broadcasting (HB) or verticalbroadcasting (VB). In HB, the server broadcasts theLDD (location dependent data) in horizontal order, thatis, from the leftmost coordinate to the rightmost coordi-nate. On the other hand, in VB, the server broadcaststhe LDD in vertical order, that is, from the bottomcoordinate to the top coordinate. In order to decidewhether to broadcast the data using HB or VB, theserver uses the following algorithm:

Notations

• leftmost_P: a point that is located at the leftmostextremity in the map (e.g., object �a� in Fig. 6),

• leftmost_P 0: the x-axis coordinate of a point that islocated at the leftmost extremity in the map withthe exception of leftmost_P, where the x-coordinatevalue of leftmost_P 0 P leftmost_P (e.g., object �b� inFig. 6),

• x-dist: distance between leftmost_P and leftmost_P 0

based on x-axis,

rong answer.

Sequence of broadcast (Horizontal Broadcast)=(a, b, c, d, e, f, g)

a

b

d

e

fg

c

y

x

trajactory of amoving object

q

dist((x-coordinate of Oi), (x-coordinate of q))

dist( Ocandi,q )

Fig. 6. An example of horizontal broadcast.


• y-dist: distance between leftmost_P and leftmost_P 0

based on y-axis,• x-dist_counter: initial value is 0,• y-dist_counter: initial value is 0,• NOC: number of compares (initial value is 0).

Algorithm 1. The server decision algorithm for VBor HB data broadcasting

Input: data objects� IDs and locations;Output: selection result for HB or VB;Procedure:

1: find leftmost_P

2: while (NOC < = Data) {3: do : {4: find leftmost_P 0 (if more than

two points have same x-axisvalue, select upper point first)

5: compare x-dist and y-dist

6: if x-dist > y-dist

7: then x-dist_counter++8: else y-dist_counter++9: leftmost_P = leftmost_P 0

10: NOC++11: }12: }13: if x-dist_counter > y-dist_counter

14: then select HB for the broadcastdata object // after the server countsthe whole data items, it decideswhether HB or VB

15: else select VB for the broadcast data object

The server decides the sequential order of the broad-cast data objects based on the above algorithm. If the

final value of the counter, x-dist, is bigger than that ofy-dist, that is, the data objects are horizontally distrib-uted, the server broadcasts the data objects in horizontalorder. On the other hand, if the value of y-dist is biggerthan that of x-dist, that is, the data objects are verticallydistributed, the server broadcasts the data objects in ver-tical order. In this paper, we assume that the serverbroadcasts the data objects using HB.

Notations

• S: server data set,• O: broadcast data object, where O 2 S,• Ocandi: candidate for the nearest data object,• Oc: current broadcast data object (initially Oc

regarded as NN), where Oc 2 S,• Op: previous broadcast data object, where Op 2 S,• q: a query point,• Ops: one of the data items broadcast before Oi in the

current broadcast cycle, where Ops 2 S,• Op: a data item broadcast just before Oc in the current

broadcast cycle, where Op 2 S,• Of: the client�s first tuned data item in the broadcast

channel,• Data_first: the server�s first broadcast data item in the

current broadcast cycle,• Flag A: if the dist (x-coordinate of Ops, x-coordinate

of q) is larger than dist (Oc, q), then set to 1 (initiallyset to 0). This flag guarantees that the client dose notmiss the NN in the current broadcast channel,

• Flag B: if the dist ((x-coordinate of Oc), (x-coordinateof q)) < dist(Ocandi, q), then set to 1 (initially set to 0)(see Lemma 1).

Lemma 1. While the data objects are sequentially broad-

casted in horizontal order, that is, from the leftmost

coordinate to the rightmost coordinate, if Oc = Oi, where

Algorithm 2. The client algorithm used to identifythe nearest object

Input: locations of the clients and the dataobjects;Output: NN;Procedure:

1: if (optimal tuning is required)2: then read the first broadcast data item

and go to doze mode until the indexsegment arrives

3: return NN using index segment4: else if (optimal latency is required) {5: do {6: for each data object O 2 S

7: if (Flag A = 0 and Flag B = 1)8: then stop tune and go to doze

until the next index segmentarrives

9: else if (Oc is the first broadcastdata object)

10: then Oc = Ocandi

11: else if dist(Oc, q) < dist(Op, q)12: then Oc = Ocandi

13: else Op = Ocandi

14: } while (getting to the index segment orFlag B = 1

15: Ocandi = NN16: return NN


Oi 2 S and dist((x-coordinate of Oi),(x-coordinate of

q)) > dist(Ocandi, q), then Oi and the rest of the broadcast

data objects are located outside of the NN range.

Proof. Given a query point �q�, if the Ocandi is an object �e�and Oc is an object �f�, as shown in Fig. 6. If dist((x-coor-dinate of the object �f�),(x-coordinate of �q�)) > dist(�e�,�q�),then the objects �f� and �g� are located outside of the NNrange and thus the client stop tuning the broadcast chan-nel and select the object �e� as the NN.

There are two cases in which the clients tune to thebroadcast channel.

Case 1: the client could not identify the NN in thecurrent broadcast cycle, since it was not able to deter-mine whether or not the desired data item was broad-casted before it tuned to the broadcast channel (seeFig. 7). In other words, the client wakes up during thebroadcast cycle and required data item may broadcastbefore it tunes the channel (see Lemma 2 and proof).

Lemma 2. While Flag A = 0 and Of 5 Data_first, the

client could not identify the NN in the current broadcast

cycle.

Proof. Let the client begin to tune at time Ti. At Ti,Oc = Oi and Ocandi is Oi (see Fig. 7. At Ti + 1, Oc = Oi + 1

and Ocandi is Oi, since dist(Oi, q) < dist(Oi+1, q). At Ti + 2,Oc = Oi+2 and Ocandi is Oi + 2, since dist(Oi + 2, q) < dis-t(Oi, q) and dist(Oi + 2, q) < dist(Oi + 1, q). At Ti + 3,Oc = Oi + 3 and Ocandi is Oi + 2, since dist(Oi + 2, q) < dist(Oi + 3, q). Then, the client stops tuning to the broadcastchannel, since dist(Oi + 2, q) < dist(x-coordinate of Oi + 3,x-coordinate of q) (see Lemma 1). However, the clientcould not guarantee Oi + 2 as the NN, since it missedthe Oi�n, such as Oi�1 data item, and one of them couldalso be Ocandi. Thus, if Flag A = 0 and Flag B = 1, thenthe client stops tuning to the broadcast channel andswitches to doze mode, until the next index segmentarrives.

Case 2: the client is able to identify the NN from thecurrent broadcast cycle, since it is sure that the desireddata item is going to appear in the current broadcastcycle (see Fig. 8).

Lemma 3. If Flag A is set to 1, then the client can

identify the NN from the current broadcast cycle.

Proof. Let Oc be data item �c� in Fig. 8. After the clientreceives data item �d�, Flag A is set to 1, which means thatdata item �c� and the data items before �c�, such as �a� and�b�, are not candidates for the NN, since the distancesfrom the x-coordinates of �a�, �b, and �c� to the x-coordi-nate of q are longer than dist(d, q). Consequently, we canconclude that the client does not miss the NN and canfind it from the current broadcast channel.

Definition 1. If Oc is Data_first, then the client canidentify the NN from the current broadcast cycle.

We assume that each client has a queue, in order tomaintain previously broadcasted data objects. The clientuses the following algorithm to identity the NN.

3.3. Prefetching scheme

The result of the NN query is changed if the clientmoves. Thus, the client has to tune its broadcast channelevery time it moves. Data prefetching has been proposedas a technique for hiding the Access Latency of data itemthat defeat caching strategies. In this section, we presenta prefetching method for use in LDIS. In this method,the client prefetches the data object for future use. Letwp be the size of prefetched data objects. The client ad-justs the size of wp according to the speed and size of thecache. Moreover, in order to adjust the value of k basedon the k-nearest objects, the proposed scheme simplyadjust the size of wp. Let client�s current location bepoint q and object�s location be point p. We denotethe Euclidian distance between the two points p and qby dist(p, q). In the map, we have distðp; qÞ :¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðpx � qxÞ

2 þ ðpy � qyÞ2

q.

Sequence of broadcast (Horizontal Broadcast): {a, b, c, d, e}

c

d e

y

x

q

dist((x-coordinate of Oc), (x-coordinate of q) )

dist(Ocandi , q)

b

a

The client starts to tune thebroadcast channel at T i

T i T i+1

Fig. 8. The client is able to identify the NN in the current broadcast cycle.

Sequence of broadcast (Horizontal Broadcast): {a, b, c, d, e, f, g, h}

c

d

f

y

x

qb

a

The client starts to tune thebroadcast channel at T i

T i+1T iT i+2

g

Oi

Oi+1

O i+2

O i+3

T i+3

h

O i+4O i-1

Fig. 7. The client could not identify the NN in the current broadcast cycle.


Let P :¼ {p�n . . .,p�2,p�1,p0,p1,p2 . . .,pn} be a set of n

distinct points that represent the data objects, and q rep-resents a query point.

Notations

• w, n P 0 and (w � n) P 0,• target = an object p0, where p0 5 pn and {p�n,p0,

pn} 2 P then dist(p0,q) 6 dist("p�n,q) or dist(p0,q) 6dist("pn,q),

• pmin = an object p�w, where dist(p�(w � n),q) 6dist(p�w,q) 6 dist(p�(w + n),q),

• pmax = an object pw, where dist(pw � n,q) 6 dist(pw,q) 6 dist(pw + n,q).

A query can be categorized as the nearest or the k-near-est based on the number of returned objects. We assumethat the each client has a queue, in order to maintain pre-viously broadcasted data objects. Let Sq be the set of dataobjects in the queue. The number of returned objects de-pends on the value of wp. If we regard the value of wp as n,the number of returned objects is 2n + 1. Hence, wp = setof 2n + 1 points. For instance, if the value of n is 0, thenumber of returned objects is 1 (nearest neighbor) or ifthe value of n is 3, the number of returned objects is 7(7-nearest neighbors). In order to adjust the value of k

of the k-nearest objects, the proposed scheme simply ad-justs the size of wp. The formal description of the algo-rithm used for prefetching at the client side is as follows:

Algorithm 3. Client algorithm for data prefetching

input: sorted broadcast data objects according tothe distance between the q and the data object;output: set of final k-NN;procedure:1: while (a client looking for the nearest object) {2: active mode (listen to the broadcast

channel)3: if (desired data comes from the server)

{// by using Algorithm 14: then current broadcast data object = p0

and prefetch a data object from pmin topmax}

5: else

6: wait until the desired data comesfrom the server

7:}8: doze mode


Lemma 4. Given a point �q�, wp contains k-NN query set,

if Sq is sorted according to the distance between the �q� and

pi, where pi 2 Sq.

Proof. Let Sq = {pi,pi + 1,pi + 2 . . .pi + n}. If Sq is sortedascending order according to the distance between the�q� and pi, then dist(pi,q) < dist(pi + 1,q) < dist(pi + 2,q) � � �< dist(pi + n,q). Therefore, wp contains k-NN query setfrom a query point �q�.

4. Performance analysis

In this section, we compare the performance of theproposed BBS scheme with that of the (1, m) indexscheme. First we compare the Access Latency betweenthe BBS and (1,m) index schemes. Then, we comparethe Tuning Time between these two schemes. We assumethat during each broadcast cycle, the server broadcaststhe same data items in the same order, and that thesedata items contain the IDs and coordinates of the dataitems and the index segments.

In order to make all of the data items self-identifying,each data item contains the following header informa-tion (Imielinski et al., 1994, 1997): bucket id: the offsetof the data item from the beginning of the bcast pointer:the offset to the beginning of the next bcast index poin-ter: the offset to the beginning of the next index segmentdata type: data item or index segment. Let C be the aver-age number of buckets containing records with the sameattribute value.

The following shows comparison of the Probe Wait

and the Bcast Wait between BBS and previous indexmethod (Imielinski et al., 1994, 1997). Let m denotesthe number of times broadcast indices:

Probe Wait: The average duration for getting to thenext index segment. If we assume that the distance be-tween two consecutive index segment is Data, then theprobe wait is Data/2. Then, as the number of m increase,Probe Wait time decreases. In the BBS method, since thedata objects broadcasted by the server are sequentiallyordered based on their location and thus, it is not neces-sary for the client to wait for an index segment. There-fore, Probe Wait of BBS = 0. Thus:

• Previous index method: 12� ðindexþ Data

m Þ,• BBS method: None.

Bcast Wait: The average duration from the momentthe index segment is encountered to the moment whenthe required data item is downloaded. As the sameway of the previous index method, the BBS method alsobroadcasts data objects, along with an index segment.Thus, BBS and previous method has the same Bcast

Wait time. Thus:

• Previous index method: 12� ððm � indexÞ þDataÞ þ C,

• BBS method: 12� ððm � indexÞ þDataÞ þ C.

Since the Access Latency is the sum of the Probe Wait

and the Bcast Wait, average Access Latency for:Previous index method is:

1

2� indexþData

m

� �þ 1

2� ððm � indexÞ þDataÞ þ C

¼ 1

2� ðmþ 1Þ � indexþ 1

mþ 1

� ��Data

� �þ C.

ð1Þ

BBS is:

1

2� ððm � indexÞ þDataÞ þ C. ð2Þ

The following shows the average Tuning Time of BBS.Let m denotes the number of times broadcast indices,k 0 denotes the number of levels in the index tree forBBS and P denotes the probability:

P (containing the desired data object among the in-dex) is 1

m, and then, P (obtaining the desired data object)is 1

2m.

Thus, average Tuning Time is P (obtaining data ob-ject without an index)* cost of reading data objects + P

(failure obtaining the desired data object after read theindex)* cost of obtain the desire data object after readthe index, and thus,

f ðmÞ ¼ 1

2mData

m� 1

2

� �

þ 2m� 1

2mData

m� 1

2þ k0 þ 1

� �

¼ Data� k0 � 1

2m�1 þ k0 þ 1: ð3Þ

Table 1Simulation parameters

Parameters Setting

Service area 1000 (km) * 1000 (km)% of service area 30–100No. of data objects 10–1000Size of data object 128–4096 bytesopt_latency Data/2opt_tune 1 + k + CNo. of indices 1–30Broadcast bandwidth 144 kbpsNo. of clients 0–500Minimum moving speed of the client 10Maximum moving speed of the client 90wp 0–5No. of broadcast period 100–1000Size of max_OBC Longer than 900 m


Now, we present a formula to compute the optimal m soas to minimize the latency and Tuning Time for the (1,m)indexing (Imielinski et al., 1997). For finding the mini-mal latency and Tuning Time, we differentiate the aboveformula with respect to m, equate it to zero and solve form; m* denotes the optimum m.

f ðmÞ ¼ Access Latency þ Tuning Time

¼ 1

2� index � mþData� k0 � 1

2m�1

þ 1

2�Dataþ k0 þ 2C

¼ f 0ðmÞ ¼ index�Data� k0 � 1

1m�2 ¼ 0;

m� ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiData� k0 � 1

Index

r: ð4Þ

5. Performance evaluation

In this section, we evaluate the performance with var-ious parameters settings, such as the client�s speed, thenumber of indices, and the distributions of the data ob-jects. Then, we evaluate the cache hit ratio, while vary-ing certain parameters, such as the size of wp and theservice coverage area. We assume that the broadcastdata objects are static, such as restaurants, hospitals,and hotels. We use a system model similar to that de-scribed in (Zheng et al., 2002; Barbara and Imielinski,1994). The whole geometric service area is divided intogroups of MSSs (Mobile Supporting Stations). In thispaper, two datasets are used in the experiments (seeFig. 9). The first data set D1 contains data objects ran-domly distributed in a square Euclidian space, while thesecond data set D2 contains the data objects of hospitalsin the Southern California area, and is extracted fromthe data set at (Spatial Datasets). Table 1 shows thenotations and default parameter settings used in thesimulation.

Fig. 9. Two scope distribution for performance evaluation.

5.1. Latency

5.1.1. Effect of the size of the service area

and the client’s speed

First, we study the effect of the size of the service areaaccording to the client�s speed. We vary the service cov-erage area from 5% to 100% of the whole geographicarea. The query arrival time decreases as the size ofthe service area decreases, since the size of the entirebroadcast is reduced. However, the query arrival timeis significantly increased when the client�s speed in-creases and the client goes outside of the service cover-age area, as shown in Fig. 10(a). In this case, theclient�s cached data items become invalid and the clienthas to tune to the broadcast channel again. Therefore,the best access latency can be obtain when the clientspeed is low and the size of the service area is small.On the other hand, if the size of the service area is smalland the client speed is high, it may cause worst possibleaccess latency.

Second, we study the effect of the client�s speed. Wevary the client�s speed from 5 to 50 in D1. When the cli-ent�s speed is the lowest, a broadcast size of 10% is thebest. However, as the client�s speed increases, its perfor-mance is degraded in comparison with that of the otherclients, whose speed exceeds the service coverage area, asshown in Fig. 10(b). This is the same reason as theFig. 10(a). That is, as the client speed increase, the resultof the obtained data items become invalid quickly andthe client has to tune the broadcast channel again, inorder to process the NN query.

5.1.2. Effect of the number of indices, size of data

and the number of data in D1

In this section, we study the Access Latency whilevarying three different parameters, viz. opt_latency,BBS and the (1, m) index in D1 (see Fig. 9). In this exper-iment, we assume that the clients are uniformly distrib-uted in the map. Fig. 11(a) shows the Access Latency as

100 90 80 70 60 50 40 30 20 10 5

low speed

0

10

20

30

40

50

60ac

cess

late

ncy

% of service area

low speedhigh speed

(a)

5 10 15 20 25 30 35 40 45 5010%

90%

05

101520253035404550

speed (km/h)

accesslatency

10%

30%

60%90%

(b)

Fig. 10. Effect of the size of the service area and the client�s speed.

0

5

10

15

20

25

30

35

2 8 10 12 14 16 18 20 22 24 26 28 30number of indices

acce

ss la

tenc

y

opt_latency

BBS

(1,m)

(a)

0

200

400

600

800

1000

1200

10 50 100 150 200 250 300 350 400 450 500

No. of clients

acce

ss la

tenc

y

opt_latency

BBS

(1,m)

(b)

010203040

5060708090

128 256 512 1024 2048 4096size of data(bytes)

acce

ss la

tenc

y

opt_latency

BBS

(1,m)

(c)

0

0.5

11.5

2

2.5

33.5

4

100 150 200 250 300 350 400 450 500

No. of data

acce

ss la

tenc

y

opt_latency

BBS

(1,m)

(d)

4 6

Fig. 11. Access Latency in D1.


the number of indices is increased from 2 to 30 in asingle broadcast cycle. As shown in the figure, BBSand opt_latency dose not affect of the number of indices,since it is not necessary for the client to wait for an indexsegment. Fig. 11(b) shows the result as the number ofclients increases. Fig. 11(c) and (d) shows the accesstimes as the size of the data items and the number ofdata items increase, respectively. As shown in the fig-ures, BBS outperforms the (1,m) method and the result-ing of latency is close to the optimum values, since theBBS method eliminates the Probe Wait time.

Finally, Fig. 12 shows a comparison of the Tuning

Time between the opt_tune and BBS schemes as thenumber of indices is increased. As shown in this figure,the BBS scheme significantly decreases the Tuning Time

as the number of indices increases. If the number of indi-

ces is equal to the number of data items, then the Tuning

Time of the opt_tune and BBS schemes are the same.However, the Access Latency is significantly increasedin the case of the opt_tune scheme, since the broadcastcycle is lengthened due to the additional indexinformation.

5.1.3. Effect of the number of indices, size of dataand the number of data in D2

In this section, we study the Access Latency whilevarying three different parameters, viz. opt_latency,BBS and the (1, m) index in D2 (see Fig. 9). Fig. 13(a)shows the Access Latency as the number of indices isincreased from 2 to 12 in a single broadcast cycle.Fig. 13(b) shows the corresponding result as the numberof clients is increased. Fig. 13(c) shows the access time as

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

2 6 10 12 14 16 18 20No. of indices

tuni

ng ti

me

opt_tune

BBS tuningtime

4 8

Fig. 12. Tuning Time.


the size of the data items is increased. As shown in thefigures, BBS outperforms the (1,m) method and theresulting of latency is close to the optimum values, sincethe BBS method eliminates the Probe Wait time in thesame way of D1.

5.2. Cache hit ratio

This section evaluates the cache hit ratio for variousparameters settings such as the size of the wp, the client�sspeed and the size of service area. First, we vary the cli-ent�s speed from 10 to 50 in D2. As shown in Fig. 14(a),

0

0.5

1

1.5

2

2.5

3

3.5

2 8 10 12

No. of indices

acce

ss la

tenc

y

opt_latency

BBS

(1,m)

(a) (b

0

20

40

60

80

100

128 256 512

size of d

acce

ss la

tenc

y

opt_latency

BBS

(1,m)

(c)

4

Fig. 13. Access La

the number of cache hits decreases as the client�s speed isincreased. In this case, uniform_100% outperforms theuniform_50%, since clients discard the cached data ob-ject if they move to the other service area. Second, wevary the client�s speed from 10 to 50 in D1. As shownin Fig. 14(b), the number of cache hits decreases as theclient�s speed is increased. Third, we vary the value ofwp from 1 to 5 in D1. As shown in Fig. 14(c), the numberof cache hits increases as the client�s speed is decreasedand the size of wp increases.

6. Conclusion

In this paper, we studied the novel broadcasting andprefetching schemes which can be used for k-NN queryprocessing. For the purpose of broadcasting in LAMSs,we present the BBS and sequential prefetching methods.In these methods, we do not modify the previous indexscheme per se. Rather, we simply sort the data objectsbased on their locations, and the server then broadcaststhem sequentially to the mobile clients. The BBS methodattempts to minimize the Access Latency for the client.Furthermore, the proposed sequential prefetching andcaching methods can also reduce the query responsetime and Tuning Time, respectively. With the proposedschemes, the client can perform k-NN query processing

0

200

400

600

800

1000

1200

1400

1600

10 50 100 150 200 250 300 350 400 450 500No. of clients

acce

ss la

tenc

y

opt_latency

BBS

(1,m)

)

1024 2048 4096

ata (bytes)

tency in D2.

0

5

10

15

20

25

30

35

40

10 15 20 25 30 35 40 45 50

speed (km/h)

No.

of c

ache

hits

100%ofservice_area

50%ofservice_area

(a)

0

5

1015

20

25

30

35

40

10 15 20 25 30 35 40 45 50

speed (km/h)

No.

of c

ache

hits

100% ofservice_area

50%of service

area

(b)

05

10

1520253035404550

10 20 30 40 50 60 70 80 90

speed (km/h)

No.

of c

ache

hits

w=1

w=2

w=3w=4

w=5

(c)

Fig. 14. Cache hits.


without having to tune into the index segment if it needsa fast response time. On the other hand, the client readsonly some portions of the index and recognizes theappropriate address of the target data. After obtainingthe address, the client can remain in doze mode untilthe target data are delivered. In this case, the clientcan minimize the energy consumption.

In this paper, we aim to provide research directionstowards minimizing both the Tuning Time and Access

Latency for the NN (Nearest Neighbor)-query process-ing. The proposed schemes were investigated in relationto various environmental variables, such as the distribu-tions of the data objects, the average speed of the clientand the number of indices in a single broadcast cycle.The experimental results show that the proposed BBSscheme significantly reduces the Access Latency, sincethe client does not always have to wait for the index seg-ment. In this paper, we did not consider the case of mov-ing data objects in LAMSs. Hence, we are planning toextend this study to the case of a moving object data-base. Finally, we are also planning to investigate theissue of cache replacement in a future study.

References

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Acharya, S., Franklin, M., 1995. Broadcast Disks: Data Managementfor asymmetric communication environments. In: Proceeding ofACM SIGMOD. pp. 199–210.

Barbara D., Imielinski, T., 1994. Sleepers and Workaholics: CashingStrategies in Mobile Environments. In: Proceeding of ACMSIGMOD. pp. 1–12.

Chen, M.-S., Yu, P.S., Wu, K.L., 2003. Optimizing index allocationfor sequential data broadcasting in wireless mobile computing.IEEE Transactions on Knowledge and Data Engineering (TKDE)15 (1), 161–173.

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Hu, Q.L., Lee, W.-C., Lee, D.L., 2001. A hybrid index technique forpower efficient data broadcast. Distributed and Parallel Databases9 (2), 151–177.

Imielinski, T., Viswanathan, S., Badrinath, B.R., 1994. Energyefficient indexing on air. In: Proceeding of ACM SIGMOD. pp.25–36.

Imielinski, T., Viswanathan, S., Badrinath, B.R., 1997. Data on air:organization and access. IEEE Transactions on Knowledge andData Engineering 9 (3), 353–372.

Lee, W.C., Lee, D.L., 1996. Using signature techniques for informa-tion filtering in wireless and mobile environments. Distributed andParallel Databases 4 (3), 205–227.

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Spatial Datasets, Available from: < http://dias.cti.gr/ytheod/research/datasets/spatial.html>.

Xu, Y., Athman, B., 2002. Broadcast-Based Data Access in WirelessEnvironments. In: Proceeding of EBDT, pp. 553-571.

Xu, J., Tang, X., Lee, D.L., 2003. Performance analysis of location-dependent cache invalidation schemes for mobile environments.IEEE Transactions on Knowledge and Data Engineering 15 (2),474–488.

Xu, J., Zheng, B., Lee, W.-C., Lee, D.L., 2004. D-tree: an indexstructure for planar point queries in location-based wirelessservices. IEEE Transactions on Knowledge and Data Engineering(TKDE) 16 (12), 1526–1542.

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Zheng, B., Xu, J., Lee, D.L., 2002. Cache invalidation and replacementstrategies for location-dependent data in mobile environments.IEEE Transactions on Computers 51 (10), 1141–1153.

Further reading

Xu, J., Zheng, B., Lee, W.-C., Lee, D.L., 2003. Energy Efficient Indexfor Querying Location-Dependent Data in Mobile BroadcastEnvironments. In: Proceeding of ICDE. pp. 239–250.

http://dias.cti.gr/ytheod/research/datasets/spatial.html

http://dias.cti.gr/ytheod/research/datasets/spatial.html

adaptive data dissemination schemes for location-aware mobile services

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