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The Journl of Systems nd Softwre xxx (2008) xxx xxx Contents lists ville t ScienceDirect The Journl of Systems nd Softwre journl homepge: www.elsevier.com/locte/jss Energy-efficient rel-time oject trcking in multi-level sensor networks y mining nd predicting movement ptterns q Vincent S. Tseng *, Eric Hsueh-Chn Lu Deprtment of Computer Science nd Informtion Engineering, Ntionl Cheng Kung University, No. 1, T-Hsueh Rod, Tinn, Tiwn, ROC rticle info strct Article history: Received 24 Jnury 2008 Received in revised form 1 August 2008 Accepted 7 Octoer 2008 Aville online xxxx Keywords: Sensor networks Loction prediction Rel-time oject trcking Dt mining A numer of studies hve een written on sensor networks in the pst few yers due to their wide rnge of potentil pplictions. Oject trcking is n importnt topic in sensor networks; nd the limited power of sensor nodes presents numerous chllenges to reserchers. Previous studies of energy conservtion in sensor networks hve considered oject movement ehvior to e rndom. However, in some pplictions, the movement ehvior of n oject is often sed on certin underlying events insted of rndomness completely. Moreover, few studies hve considered the rel-time issue in ddition to the energy sving prolem for oject trcking in sensor networks. In this pper, we propose novel strtegy nmed multi-level oject trcking strtegy () for energy-efficient nd rel-time trcking of the moving ojects in sensor networks y mining the movement log. In, we first conduct hierrchicl clustering to form hierrchicl model of the sensor nodes. Second, the movement logs of the moving ojects re nlyzed y dt mining lgorithm to otin the movement ptterns, which re then used to predict the next position of moving oject. We use the multi-level structure to represent the hierrchicl reltions mong sensor nodes so s to chieve the gol of keeping trck of moving ojects in rel-time mnner. Through experimentl evlution of vrious simulted conditions, the proposed method is shown to deliver excellent performnce in terms of oth energy efficiency nd timeliness. Ó 2008 Elsevier Inc. All rights reserved. 1. Introduction As wireless technologies progress, sensor nodes re getting smller with more powerful communiction cpility nd functions. Such sensor nodes cnnot only detect the sttus of specific oject, ut cn lso trnsfer this informtion to se sttions or dtses in wireless systems. Oject trcking sensor networks (OTSNs) hve ttrcted much ttention recently due to the scope nd utility of their pplictions. In OTSNs, the sensor nodes form d hoc networks (Akyildiz et l., 2002; Hr et l., 2004) tht cn trck the position of moving oject in the sensor networks in timely mnner. Structurlly, sensor networks cn e divided into two ctegories, the peer-to-peer or hued networks. In peer-to-peer (or d hoc) sensor network, the network structure is grphicl nd ech sensor node cn ccess only its neighoring sensor nodes. q This pper is n extended version of Tseng et l. (2005), entitled An Energy- Efficient Approch for Rel-Time Trcking of Moving Oject in Multi-Level Sensor Networks, y V.S. Tseng, Eric H.C. Lu nd K.W. Lin, which ppered in Proceedings of the IEEE Interntionl Conference on Emedded nd Rel-Time Computing Systems nd Applictions, August, 2005, Hong Kong. * Corresponding uthor. Tel.: +886 62757575x62536; fx: +886 62747076. E-mil ddress: tsengsm@mil.ncku.edu.tw (V.S. Tseng). URL: http://id.csie.ncku.edu.tw/tsengsm (V.S. Tseng). The routing concept is used to communicte etween sensor nodes. In hued sensor network, the network structure is tree in which the leves corresponds to sensor nodes with wek computtionl nd communiction ilities; nd the internl node corresponds to powerful device tht cn communicte with ll sensor nodes within the region. One scenrio envisioned for OTSNs is the trcking nd mngement of vehicles in industril regions. In lrge-scle regions, vehicle mngement is complex mtter. One solution to vehicle mngement is control y control center vi wireless communiction. However, this strtegy not only requires incresed humn resources, ut lso increses the risk of ccidents due to drivers eing forced to drive vehicle while reporting their position to the control center. Therefore, this scenrio is suitle for OTSNs in which sensor nodes sense vehicle movement within given region nd ctively report to the control center. This pproch cn increse the efficiency of vehicle trcking nd mngement. One prolem presented y this type of ppliction is the power consumption ottleneck tht ffects the lifetime of OTSNs. Conserving energy y inctivting idle sensor nodes is populr pproch (Cerp et l., 2001; Xu et l., 2004). In Cerp et l. (2001), the uthors proposed the Frisee scheme in which only limited zone (nmely frisee) of the network tht is close to the event is kept in its fully ctive stte. However, it is difficult to determine 0164-1212/$ - see front mtter Ó 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.jss.2008.10.011

2 V.S. Tseng, E.H.-C. Lu / The Journl of Systems nd Softwre xxx (2008) xxx xxx Fig. 1. Clustering of sensor nodes. 2 0 4 the crucil rdius of the frisee. In Xu et l. (2004), the uthors presented the Prediction-sed Energy Sving () scheme to conserve scrce energy resources y exploiting the most recent detected or verge velocity of n oject to predict the next node it will visit. However, this kind of pproch might cuse OTSNs to lose trck of the oject. Hence, it is importnt to recpture missing ojects in rel-time mnner. Oviously, energy efficiency nd timeliness re two importnt issues tht should e considered in designing oject trcking methods for OTSNs. In this pper, we explore efficient nd rel-time trcking of moving ojects in sensor networks y mining the movement log. By contrst with pst studies tht focused primrily on the design of sensor network structure for energy sving, we im t discovering the hidden movements of moving ojects. Pst studies on energy sving in sensor networks considered the oject s movement ehvior to e rndom. In some pplictions, however, the movement ehvior of n oject is often sed on certin underlying events insted of totl rndomness. Therefore, the movement logs enefit for moving ojects trcing. Furthermore, we consider tht sensor networks cn e orgnized in hierrchicl structures. For exmple, the gte re in n industril region my incur higher frequencies of incoming nd outgoing events. As such, we my deploy more sensor nodes round the gte re. Thus, we cn construct hierrchicl structure for the sensor network nd predict the loction of moving oject ccording to tht hierrchicl structure. In this wy, oject recovery is fesile ccording to such structures. As fr s we know, no studies hve explored oth issues of timeliness nd energy efficiency simultneously in OTSNs through the mining of moving ptterns. In this pper, we propose new pproch concerning oth timeliness nd energy efficiency t the sme time. First, we cluster the sensor nodes in the sensor networks nd use multi-level structure (Lu, 1997) to represent the hierrchicl reltionship mong sensor nodes. For exmple, Fig. 1 shows sensor deployment with sensor nodes clustered nd Fig. 2 displys the multi-level structure corresponding to the sensor nodes in Fig. 1. Furthermore, we discover the movement ehviors of ojects nd generte the relevnt movement ptterns y which we my predict the next loction of n oject. Therey, only the sensor node t the predicted loction will e ctivted. If the prediction fils, the ctivting region will e extended using the sensor node hierrchy to ctivte more sensor nodes until the missing oject is recptured. In this wy, oth energy efficiency nd timeliness cn e improved y the proposed method for trcking ojects in OTSNs. Through empiricl evlution of vrious simultion conditions, our proposed method is shown to ctully chieve such excellent results. The rest of this pper is structured s follows. Relted works re given in Section 2. In Section 3, we descrie the prolem; nd the proposed dt mining lgorithm, nmely multi-level oject trcking strtegy (), is presented in Section 4. Empiricl evlution is mde in Section 5. Conclusions nd future work re provided in Section 6. 2. Relted work 1 5 6 3 7 9 8 R: Region R1 R11 R12 R21 R22 In this section, we review pst studies in terms of three ctegories relted to this reserch: clustering methods, energy sving pproches, nd dt mining techniques. For the clustering issues in sensor networks, severl methods hve een proposed. In Beyens et l. (2005), Beyens et l., proposed cluster-sed rchitecture for wireless sensor networks in which ech cluster hed mintins locl prediction model tht is used to select suitle node of the cluster to e ctivted. The ide is to put sensor node to sleep when there re no ojects in its sensing region. Severl centrlized clustering techniques, such s K-mens (Hn nd Kmer, 2000) nd hierrchicl clustering (Hn nd Kmer, 2000) use this method. In Dimoks et l. (2007), the uthors proposed network-topology-sed distriuted clustering protocol nmed GESC in wireless sensor networks. GESC is sed on loclized metric for mesuring the cpility of neighorhood coverge while node is rerodcsting. In Younis nd Fhmy (2004), the uthors proposed nother energy-sed distriuted clustering protocol nmed HEED in d hoc sensor networks. The protocol proilisticlly selects severl nodes s cluster heds ccording to their residul energy, nd then reminder nodes re joined into clusters to minimize the communiction cost etween them nd corresponding cluster heds. In Bnerjee nd Khuller (2001), Krishn et l. (1997), nd Wng nd Zhu (2006), the uthors proposed scheme for node clustering in wireless networks which dynmiclly orgnizes topology into k-clusters, where nodes in cluster re mutully rechle vi k-hop pths. For energy sving issues, most previous studies hve focused on optimizing the communiction cost y inctivting the RF rdios of idle sensor nodes (Estrin et l., 1999; Goel nd Imielinski, 2001; Heinzelmn et l., 2000; Ko nd Vidy, 2000). There exist lso numer of reserch works concerning energy-efficient medi ccess control (MAC) such s Shih et l. (2001), Woo nd Culler (2001), Ye et l. (2002). However, they did not tke the movement ehvior of ojects nd rel-time issues into considertions. Their sic energy sving method is to inctivte sensor node whenever there re no ojects locted in its sensing region. In Cerp et l. (2001), the uthors proposed the Frisee scheme in which only limited zone (nmely frisee) of the network tht is close to the event is kept in its fully ctive stte. However, it is oth difficult nd crucil to choose good rdius of the Frisee. In Xu et l. (2004), the uthors presented the Prediction-sed Energy Sving () scheme to conserve scrce energy resources y exploiting most recent detected or verge velocity of n oject to predict the next node(s) tht the oject might visit. To select the oject velocity nd direction, three models nmed Heuristics INSTANT, Heuristics AVERAGE, nd Heuristics EXP_AVG were lso proposed. R2 Fig. 2. (). Sensor network. (). Multi-level structure. Level 2 Level 1 Level 0

V.S. Tseng, E.Hsueh-Chn Lu / The Journl of Systems nd Softwre xxx (2008) xxx xxx 3 In the prediction phse, three mechnisms were proposed, nmely Heuristics DESTINATION, Heuristics ROUTE, nd Heuristics ALL_NBR. Heuristics DESTINATION utilizes only the velocity informtion for ctivtion, while Heuristics ROUTE ctivtes ll nodes long the route. The Heuristics ALL_NBR mechnism ctivtes ll neighoring nodes of the destintion. If the prediction mechnism fils to recover the oject within specified dedline, the flooding recovering strtegy (Cerp et l., 2001) will e ctivted to recover the missing oject. However, there exists no work tht considers oth of rel-time nd energy efficiency issues in OTSNs simultneously. However, the methods descried ove focus on physicl properties nd ignore the hits of moving ojects. Thus, it is very importnt to tke dvntge of ptterns of motion to grsp the loction of moving ojects more ccurtely. In recent yers, numer of studies hve een mde out using dt mining techniques to discover useful rules/ptterns from WWW (Pei et l., 2000), trnsction dtses (Agrwl nd Sriknt, 1994, 1995), nd moility dt (Tseng nd Lin, 2004, 2007; Tseng nd Tsui, 2004; Peng et l., 2006). In Pei et l. (2000), the uthors proposed n lgorithm nmed WAP-Mine for efficiently discovering the we ccess ptterns from we logs y using tree-sed dt structure without cndidte genertion. In (Agrwl nd Sriknt, 1994), the uthors proposed method for discovery of the time-ordered ptterns nmed sequentil ptterns from trnsction dtses. Most of these pst studies focused only on movement ehvior nlysis or loction trcking (Tseng nd Lin, 2004; Tseng nd Tsui, 2004). Tking into considertion the temporl chrcteristics tht re crucil to OTSNs, dt mining techniques re intensively studied nd successfully pplied to mny pplictions. In (Peng et l., 2006), Peng et l. proposed heterogeneous trcking model nmed HTM to efficiently mine oject moving ptterns nd trck ojects. In Tseng nd Lin (2007), Tseng et l. first proposed dt mining method nmed TMP-Mine with tree structure nmed TMP-Tree to discover efficiently the temporl movement ptterns of ojects in sensor networks. Moreover, the uthors proposed novel loction prediction strtegies tht utilize the discovered temporl movement ptterns to reduce the prediction errors for energy svings. However, this work did not consider the following issues. (1) Refinement of sensor network rchitecture: In lrge sensor networks, the energy consumption on messge communiction will e lrge when the network rchitecture is not considered; (2) Timeliness for recovering missing ojects: In the prediction-sed method for oject trcking prolem, the trcked oject is still missing if the prediction fils. Hence, the timeliness for recpturing missing ojects is criticl requirement. However, to our knowledge, there exist no studies tht integrte energy sving nd rel-time constrints for prediction strtegies in OTSNs. 3. Sttement of the prolem In this section, we outline the prolem studied in this reserch. Then, we descrie sensor network environments nd ehvior issues surrounding ojects. The performnce mesurements we utilized re lso descried in the end of this section. We give the simple definitions of movement pth nd movement dtset s follows. Let S=<l 1,l 2,...,l m > e movement pth of n oject with length equl to m, where l i represents the loction of sensor node tht were visited sequentilly y n oject etween its entering nd leving, where "1 6 i 6 m. Let D=<S 1,S 2,...,S n > e movement dtset of ojects with size equl to n, which is the collection of movement pths generted y moving ojects. The trgeted prolem is to predict the next position of moving oject nd keep trcking moving ojects in rel-time mnner. We dopt sensor network model for OTSNs s proposed in Xu et l. (2004), in which sensors re ctivted only if there is moving oject in its sensing region. Moreover, some ssumptions re mde in estlishing our model nd experimentl nlysis: (1) the movement log of ojects is collectle through the pproches studied in Mni (2003); (2) the sensor nodes re distriuted rndomly; (3) the communiction routing etween sensor nodes hs een worked out; (4) the movement log of the moving oject cn e otined from the OTSNs; (5) there exists server sensor node in ech sensor region nd this server sensor node cn communicte with ll the sensor nodes within the region; nd (6) oject movement ehvior is often sed on certin underlying events insted eing completely rndom (Tseng nd Tsui, 2004; Tseng nd Lin, 2006; Yvs et l., 2005). In the following, we descrie some importnt performnce mesurements in solving the trgeted prolem: Averge Serch Time (AST): AST is the verge time tht is required to recover the missing moving oject. Averge Energy Consumption (AEC): AEC is the verge energy consumption tht is required to recover the missing oject. Miss Rte (MR): Let M e the numer of times of recovery ctions whose elpsed time is greter thn the predefined dedline threshold, nd T e the totl numer of times of recovery ctions, MR is defined s: MR ¼ M T 100% Oviously, for proposed oject trcking method, lower vlues of AST, AEC nd MR indicte etter performnce. Tht is, these mesures cn reflect the speed of method in recovering the missing oject nd functionl durtion of the sensor network. 4. Proposed method: In this section we descrie the proposed method for trcking moving ojects in OTSNs in detil. Fig. 3 shows the system rchitecture. The system workflow consists of three min phses: (1) clustering of sensor nodes; (2) discovery of movement ptterns; nd (3) prediction nd recovery of loctions of moving ojects. First, we conduct the hierrchicl clustering lgorithm to form hierrchicl model for the sensor nodes. Then, the movement logs of the moving ojects re nlyzed y dt mining lgorithm to otin the movement ptterns. Susequently, the movement ptterns re used to predict the next position of moving oject. 4.1. Clustering of sensor nodes Becuse the sensor nodes exhiit chrcteristics of locl clustering, we use the clustering mechnism to form the hierrchicl structure of the sensor networks. In fct, there exist numer of cluster methods such s GESC (Dimoks et l., 2007) nd HEED (Younis nd Fhmy, 2004) in sensor networks nd these methods cn generte good cluster structures for sensor network. However, the min issue ddressed in our reserch is oject trcking nd oject recovering. Thus we dopt K-mens (Hn nd Kmer, 2000) s our clustering method in the process due to its simplicity nd populrity. The messge cost of sensor clustering y using the K-mens lgorithm is out 6626 messge psses under defult settings. The reson is tht the K-mens lgorithm is n itertive clustering lgorithm in which the items re moved mong sets of clusters until the desired set is reched. Fig. 4 shows the SN_Clustering (mening Sensor Network Clustering) lgorithm, which performs clustering on sensor nodes sed on the K-mens method (Hn nd Kmer, 2000). Input dt includes ll loctions of sensor nodes, n 1...n n (line 02), nd

4 V.S. Tseng, E.H.-C. Lu / The Journl of Systems nd Softwre xxx (2008) xxx xxx Sensor Network 0 1 5 6 2 3 7 4 9 8 Dt Mining Mechnism Movement Log Mining Ptterns Clustering Structure Informtion Movement Ptterns (1) N2N. (2) N2R. Multi-Level Structure Oject Trcking Mechnism R: Region Level 2 R1 R2 R11 R12 R21 Level 1 R22 Level 0 Oject Recovery Methods: 1. 2. Flooding Loction Prediction Strtegies: 1. 2. Fig. 3. System rchitecture. Fig. 4. SN_Clustering lgorithm. n i ={x i, i}, for i =1ton. K is the numer of desired sensor regions to seprte the sensor network (line 03). Output dt is the sensor network (line 05) fter clustering. Initilly, we select K sensor nodes s initil regionl centers (line 07). Then, ech sensor node is ssigned to its closest sensor region (line 09). The distnce etween two sensor qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi nodes is clculted y Eucliden distnce, i.e., d ij ¼ dðn i ; n j Þ¼ ðx i x j Þ 2 þðy i y j Þ 2. At the sme time, the center of ech region is re-clculted (line 10). The ove step is repeted until there is no chnge mong the centers (line 08 11). Bsed on the clustering method descried ove, we devise mechnism nmed ML_SN_Clustering (mening Multi-Level Sensor Network Clustering) to construct hierrchicl sensor network structure. Two import prmeters, nmely Fnout nd Height re used in our model to model the rnch nd the depth of the hierrchicl structure, respectively. The initil vlue of K for SN_Clustering lgorithm is set s Fnout. The Fnout nd Height of hierrchy influence energy consumption nd the speed of oject recovery when the trcked oject is missed. Fig. 5 shows the ML_SN_Clustering lgorithm. This lgorithm is recursive procedure, where NodeAll is set of sensor nodes which is not clustered yet (line 02), nd h is prmeter to control the height of the hierrchicl structure (line 03). If h is smller thn the predefined height of multi-level clustering (line 07), this lgorithm continues clustering. In the eginning, we pply k-mens to cluster ungrouped sensor nodes (line 08). Then, putting sensor nodes into the Nset sensor node set from ech group (line 1015), we recursively pply the ML_SN_Clustering lgorithm to cluster within the prmeter Nset sensor nodes set (line 16). In this wy, multi-level sensor network is uilt. 4.2. Mining of movement ptterns Fig. 5. ML_SN_Clustering lgorithm. In the sensor network environment, we monitor the movement pth while the oject is moving. Tle 1 shows n exmple of movement log. We propose two kinds of movement ptterns: (1) Tle 1 A smple movement log. Oj_Pth_ID Movement pth 0 0,1,3,7,9 1 5,7,1,3,2,4 2 6,7,9,8,3,2,0 3 1,3,7,9,8,4,2 4 0,1,3,2,4 5 9,6,5,7,1,3,2,4

V.S. Tseng, E.Hsueh-Chn Lu / The Journl of Systems nd Softwre xxx (2008) xxx xxx 5 node-to-node ptterns, nd (2) node-to-region ptterns. A node-tonode pttern, denoted?, indictes tht n oject moves from node to node. A node-to-region pttern, denoted? R, indictes tht n oject moves from node to region R. Furthermore, movement informtion is derived from chnges in loction nd kept in the dtse. The frequency of the inference pttern is used to evlute the confidence of the pttern, nd tht with the highest frequency serves s the sis of the prediction (Agrwl nd Sriknt, 1994, 1995). Bsed on the ove description, every sensor node hs its own movement ptterns including oth node-to-node ptterns nd node-to-region ptterns. In cse the frequency of the different movement ptterns is the sme, we choose the most frequent destintion sensor node in the dtse for our prediction. This is ecuse it is resonle to expect the sensor node tht cptures more ojects to hve higher proility of eing the correct next position. Fig. 6 shows the Movement Pttern Genertion (MPG) lgorithm. For ech movement pth p in the movement log (line 06 12), the MPG lgorithm is le to clculte the numer of times of moving oject moves from the current node to its next loction in ech level (line 09). Finlly, we cn otin movement ptterns for most ojects. 4.3. Prediction nd recovery of loctions By using the movement ptterns, we re le to predict the next loction for moving oject in sensor networks nd to ctivte the lest sensor nodes. To compenste for prediction errors (i.e., n oject tht could e missed), we extend the scope of the region for sensor node ctivtion so s to recpture the missing oject. Fig. 7 shows the lgorithm for loction prediction nd oject recovery, nmely, PR-Algorithm. Our policy is to tke the sensor node with highest frequency s the first predicted node sed on the movement ptterns discovered y MPG (line 05 nd line 06). If the prediction fils (line 07), we extend the region consisting of the cluster contining the predicted sensor node in the higher level nd wke up ll sensor nodes within this region until the moving oject is found (line 08). In the worst cse, we ctivte ll sensor nodes in the network. Fig. 6. Algorithm for generting movement ptterns. Tle 2 The movement pttern tle. Sensor node ID Level 0 Level 1 Level 2 Level 3 0 1(3) 11(3) 1 3(5) 12(5) 1 * (3) 1 * (5) ** (3) ** (6) 7(1) 22(1) 2 4(3) 12(3) 2 * (1) 1 * (4) ** (4) 0(1) 11(1) 3 2(4) 12(4) 1 * (4) ** (6) 7(2) 22(2) 2 * (2) 4 2(1) 12(1) 1 * (1) ** (1) 5 7(2) 22(2) 2 * (2) ** (2) 6 7(1) 22(1) 2 * (2) ** (2) 5(1) 21(1) 7 9(3) 22(4) 2 * (4) ** (6) 1(2) 11(2) 1 * (2) 8(1) 8 3(1) 12(2) 1 * (2) ** (3) 9(1) 22(1) 2 * (1) 4(1) 9 6(1) 21(1) 2 * (3) ** (3) 8(2) 22(2) 4.4. An illustrtive exmple In this section, we illustrte our concept. Assume tht we lredy hve hierrchicl clustering sensor network generted y the ML_SN_Clustering lgorithm s shown in Fig. 2. The structure of the hierrchicl clusters is s shown in Fig. 2. In ddition, the movement log is depicted in Tle 1. In this exmple, the prmeters, Fnout nd Height, re set s 2 ecuse the rnch nd depth of the hierrchicl structure re 2. Initilly, we count the occurrences of distinct oject movements in ech level, i.e., node-to-node nd node-to-region, s in Tle 2. The column of Level 0 represents the node-to-node movement ptterns. The columns of Level 1, Level 2, nd Level 3 indicte the frequency of node-to-region movement ptterns. Note tht Level 3 indictes the worst cse, in which ll sensor nodes re ctivted. In the first column of Tle 2, sensor node ID indictes the lst sensor node tht detected the missing oject. Level 0 stores ll the possile next positions of ojects, with the lst visited sensor node eing the vlue of the sensor node ID. For instnce, if n oject is missing nd the lst detected loction is sensor node 1, ctivting node 3 insted of node 7 will result in the higher proility of recpturing the oject ecuse the numer of visited times for node 3 nd node 7 re 5 nd 1, respectively. If the prediction fils, the recovery mechnism is trigged to recover the missed oject. We first extend the region up to Level 1; nd the numer of times moving from sensor node 1 to region 12 (R12) to e 5 nd the numer of times to R22 to e 1 y looking up the pttern tle. In this wy, we wke up ll sensor nodes in R12, i.e., sensor node 2, 3, nd 4 nd detect whether the oject is within the scope of R12. The level is will e rised until the oject is cptured. If the numer of times of nodes is the sme, the sensor node with the highest frequency will e chosen. For instnce, the numer of times tht sensor node 6 moved to either sensor node 7 or to sensor node 5 re oth 1. In the cse, sensor node 7 will e chosen s the consequent ecuse the count of movements ending with sensor node 7 is 6, which cn e otined y scnning the tle, nd it is only 2 for sensor node 5. In our implementtion, we crete n index tle to record the count of ech sensor node to sve the scnning time. As descried ove, we follow this rule if Level 1 nd Level 2 hve the sme numer of times. 5. Experimentl evlution Fig. 7. Algorithm for loction prediction nd oject recovery. We conducted series of experiments to evlute the performnce of the proposed method under different system conditions

6 V.S. Tseng, E.H.-C. Lu / The Journl of Systems nd Softwre xxx (2008) xxx xxx y vrying the prmeters. The experiments include (1) the impct on AEC nd AST of vrying the numer of sensor nodes; (2) the impct on MR of vrying the dedline threshold; (3) the impct of AST nd AEC of vrying the size of the movement log; (4) the impct on AST nd AEC of vrying the velocity of moving ojects; nd (5) the impct on AST nd AEC of vrying the sensing rte. Menwhile, the effects of vrying system prmeters were lso studied. We employ Jv to implement our experiments on 1.8 GHz mchine with 768 MB of memory running Windows XP. the network my move y dhering to MR-Tle or rndomly. Prmeter N p (defulted s 100,000) represents the numer of moving ojects tht re used to generte the movement log in the network. The movement log contins 100,000 movement pths nd the verge length of ech movement pth is etween len min 4.0 5.1. Simultion model Tle 3 shows the mjor prmeters used in the simultion model with the defult setting. We designed simultion model with dt genertor which rndomly produces the distriution of sensor nodes. We use n x y coordinte xis to represent the loction of sensor node. Some ssumptions re mde s follows. We ssume no distinct sensor nodes re in the sme loction. Any distnce etween two distinct sensor nodes does not exceed the user-defined threshold for voiding the outliers. In the se experimentl model, the size of the sensor network is represented y the prmeters H (defulted s 100 m) nd W (defulted s 100 m). There re N s (defulted s 1000) sensor nodes tht re deployed in the sensor network. We ssume tht the ehvior of moving ojects in the OTSNs is event-driven insted of rndomness completely. Hence, we use the prmeters P e to model the event proility, which represents the proility for n oject to dhere to certin event. This proility is modeled y norml distriution with men P e (defulted s 0.5). The events of node re structured y Movement Rules Tle (MR-Tle) which mnifests the movement ehviors of most ojects is creted eforehnd. In the defined MR-Tle, for instnce, most ojects t sensor node 1 currently tend to move to sensor node 5. In the movement pths genertion of ojects, sensor node is selected rndomly s the strting loction of n oject. Ech oject in Tle 3 Mjor prmeters of the simultion model. Averge Energy Consumption (J) Averge Serch Time (time unit) 3.5 3.0 2.5 2.0 1.5 1e+6 8e+5 6e+5 4e+5 2e+5 500 750 1000 1250 1500 The Numer of Sensor Nodes Prmeters Description Defult vlue H Height of sensor network 100 W Width of sensor network 100 L Level of multi-level sensor network 4 K s Averge numer of sensor nodes in ech region 4 K r Averge numer of su-regions in ech region 4 N s Totl numer of sensor nodes. (N s = K r (L 1) * K s ) 1000 r mx Mximum distnce etween ny pir of sensor nodes 30 V Averge velocity of the moving oject 30 CR Sensing coverge rnge of sensor nodes 10 P e Averge event proility on ech node 0.5 N p Numer of moving pth log 100,000 Len mx Mximum length of ll moving pth 20 Len min Minimum length of ll moving pth 10 Sr Sensing rte of. 0.3 D Dedline threshold (in time units) 4 0 500 750 1000 1250 1500 The Numer of Sensor Nodes Fig. 8. () AST with numers of sensor nodes vried. (). AEC with numers of sensor nodes vried. Miss Rte (%) 80 60 40 20 Tle 4 Energy consumption on WINS nodes. Component Mode Power (mw) MCU Activte 360 MCU Sleep 0.9 Sensor Activte 23 Rdio Trnsmission 720 Rdio Receiving 369 0 1 2 3 4 5 Dedline Threshold Fig. 9. MR with dedline threshold vried.

V.S. Tseng, E.Hsueh-Chn Lu / The Journl of Systems nd Softwre xxx (2008) xxx xxx 7 nd len mx (defulted s 10 nd 20, respectively). We ssume tht the sensing coverge rnge is 10 m nd the verge oject velocity is set s 30 m/s. We utilize well-known routing lgorithm nmed shortest pth multi-hop (Xu et l., 2004) for communictions etween the sensor nodes nd the se sttions. In simulting the energy consumption, we dopted the Rockwell s WINS node (Project, 0000) s the sis. Tle 4 lists the energy consumption on WINS nodes (Xu et l., 2004). More detiled power nlysis of WINS nodes cn e found in Rghunthn et l. (2002), Tseng nd Tsui (2004), nd Project, 0000. By referring Egle et l. (2006), Hung et l. (2003), Lin et l. (2006), Wu et l. (2001), nd Xu et l. (2004), the defult vlues of prmeters setting in the simultions cn reflect resonle nd compct environment for n OTSN. For ll experiments, we compre the proposed method with under vrious prmeter settings. The min reson for choosing scheme in comprisons is tht it is the most representtive nd populr method for predicting the loction of moving ojects in oject trcking sensor networks. For the simulted dt generted y the dt genertor, 70% of the dt re used for trining to otin movement ptterns nd the remining 30% re used for predictions. 5.2. Impct of vrying the numer of sensor nodes This experiment nlyzes required serch time nd energy consumption when the numer of sensor nodes is vried. Fig. 8 nd shows tht our proposed method outperforms in terms of AST nd AEC under vrying numers of sensor nodes. We oserve tht y incresing the numer of sensor nodes, energy consumption increses while the serch time nerly remins constnt. The verge rte of improvement of over is 52.4% for AST nd 49.4% for AEC. The improvement of AST is mde primrily due to the underlying structure of hierrchicl sensor networks. When n oject is missing, the upper-ound of verge serch time is the height of the hierrchicl sensor network structure. However, the flooding recovering strtegy of is sed on the flooding rdius. Therefore, the distnce etween the missing oject nd the flooding center is n importnt fctor to flooding strtegy. AEC improvement occurs ecuse our proposed prediction method is sed on the oject s ehviors nd hierrchicl sensor network structures, ut uses the most recently detected or verge velocity of n oject to predict its current loction. Therefore, more energy is required y the network with more sensor nodes to recpture the missing oject. By comprison, our model is not s dversely ffected y the numer of sensor nodes s is. 5.3. Impct of vrying dedline threshold This experiment nlyzes the miss rte when vrying the dedline threshold. The dedline cn e defined s the mximum serch time llowed when recpturing missing oject. Fig. 9 shows tht 4.0 6.5e+5 Averge Serch Time (time unit) 3.5 3.0 2.5 2.0 Averge Energy Consumption (J) 6.0e+5 5.5e+5 5.0e+5 4.5e+5 4.0e+5 3.5e+5 3.0e+5 1.5 10K 50K 100K 150K 200K The Size of Movement Logs 2.5e+5 10K 50K 100K 150K 200K The Size of Movement Logs c 1000 800 Mining Time (ms) 600 400 200 0 10K 50K 100K 150K 200K The Size of Movement Logs Fig. 10. () AST using vried size of movement logs. (). AEC using vried size of movement logs. (c). Mining time using vried size of movement logs.

8 V.S. Tseng, E.H.-C. Lu / The Journl of Systems nd Softwre xxx (2008) xxx xxx the miss rte is constnt for, while it increses with the dedline threshold decresed for. In other words, when the dedline threshold is strict, the miss rte of is high. In this experiment, the height of multi-level structure of sensor networks is set to 4. outperforms when the dedline threshold is vried from 1 to 5. On verge outperforms y 64.1% for MR. This is ecuse our proposed method is sed on the hierrchicl network rchitecture. When the oject is missing, the upperound of recovery serch time is the height of the hierrchicl sensor network structure. By contrst, the serch time required y the flooding recovering strtegy used y is determined y the flooding rdius. Hence, the distnce etween current loction of the missing oject nd the flooding center is n importnt fctor. In such network, if the dedline threshold is lrger thn the height of the hierrchicl sensor network structure, our method never misses the oject. In the rel pplictions, cn djust the height of multi-level structure of sensor networks in ccordnce with the dedline threshold specified. 5.4. Impct of vrying the size of movement log This experiment nlyzes the required serch time nd energy consumption when the size of the movement log is vried. Fig. 10 nd shows tht outperforms in terms of AST nd AEC under vrying sizes of movement logs. We oserve tht with the numer of movement logs incresing, serch time nd energy consumption re constnt. Furthermore, outperforms when the size of movement logs is vried from 10 K to 200 K. The verge improvement of over is 52.9% for AST nd 49.5% for AEC. This is ecuse the size of the movement log influences only the execution time (see Fig. 10c). We oserve tht with the size of the movement log incresed, execution time lso increses. After the prediction model is uilt, serch time nd energy consumption re less ffected y the numer of movement logs. Therefore recpturing the oject using the flooding recovering strtegy will increse the worklod to the system. 5.5. Impct of vrying the velocity of moving ojects This experiment nlyzes the required serch time nd energy consumption when the moving oject s velocity is vried. Fig. 11 nd shows tht outperforms in terms of AST nd AEC. We oserve tht oth serch time nd energy consumption increse with the increse of oject velocity. In ddition, outperforms except when the speed of the oject is very low. The verge improvement of over is 53.6% for AST nd 42.9% for AEC. This is ecuse is region sed method, nd there is no impct on the method regrdless of the speed of the oject. However, the higher the speed of the oject, the esier to miss trcking it in ; which leds to the rising trend of verge serch time nd verge energy consumption. 5.6. Impct of vrying the sensing rte This experiment nlyzes the required serch time nd energy consumption when the sensing rte of is vried. Fig. 12 6 5.0 Averge Serch Time (time unit) 5 4 3 2 Averge Serch Time (time unit) 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1 10 20 30 40 50 Velocity (m/s) 1.0 0.1 0.3 0.5 0.7 0.9 Sensing Rte 9e+5 8e+5 Averge Energy Consumption (J) 8e+5 7e+5 6e+5 5e+5 4e+5 3e+5 2e+5 Averge Energy Consumption (J) 7e+5 6e+5 5e+5 4e+5 3e+5 2e+5 1e+5 1e+5 10 20 30 40 50 Velocity (m/s) 0 0.1 0.3 0.5 0.7 0.9 Sensing Rte Fig. 11. () AST with oject velocity vried. () AST with oject velocity vried. Fig. 12. () AST with sensing rte vried. () AEC with sensing rte vried.

V.S. Tseng, E.Hsueh-Chn Lu / The Journl of Systems nd Softwre xxx (2008) xxx xxx 9 nd shows tht outperforms in terms of AST nd AEC under vried sensing rtes. It is oserved tht serch time nd energy consumption re constnt for with decrese sensing rte. By contrst, oth serch time nd energy consumption increse with the decrese of sensing rte for. The verge improvement of over is 53.6% for AST nd 42.9% for AEC. This is ecuse with the increse of the sensing rte, sensor nodes tend to not miss moving ojects. Lower sensing rtes result in the less power consumption, ut it is esier to miss the oject s well. cuses incresed serch time nd more power consumption y using flooding. This is ecuse the shorter sensing time mkes determining the speed of moving ojects more difficult. In our proposed method, outperform in oth verge serch time nd verge energy consumption. 5.7. Impct of vrying the sensor deployment Averge Energy Consumption (J) Averge Serch Time (time unit) 4.0 3.5 3.0 2.5 2.0 1.5 6.0e+5 5.5e+5 5.0e+5 4.5e+5 4.0e+5 3.5e+5 3.0e+5 2.5e+5 2.0e+5 Rndom Grid Skewed Sensor Deployment Rndom Grid Skewed Sensor Deployment Fig. 13. () AST with sensor deployment vried. () AEC with sensor deployment vried. This experiment nlyzes the required serch time nd energy consumption when the sensor deployment is vried. Fig. 13 nd shows tht outperforms in terms of AST nd AEC under vried sensor deployment. The verge improvement of over is 52.4% for AST nd 52.1% for AEC. InFig. 13, it is oserved tht serch time is constnt under vrious sensor deployments for nd. In Fig. 13, energy consumption in grid deployment is slightly higher thn tht in rndom nd skewed deployments for. On contrst, energy consumption in skewed deployment is slightly lower thn tht in rndom nd grid deployments for. Hence, the performnce under the grid deployment is etter thn tht of rndom nd skewed deployments. The effect on network rchitecture refinement for the grid deployment is not significnt y using sensor node clustering lgorithm. Hence, the energy consumption of under grid deployment is slightly higher thn tht of other types of deployment. For skewed deployment, the distnce etween neighor nodes in the denser regions is smller thn other deployment wys. Hence, the energy consumption of velocity-sed method under skewed deployment is slightly lower thn tht of other deployment wys. In overll, our proposed method outperforms in terms of oth verge serch time nd verge energy consumption. 6. Conclusions nd future work In this pper, we hve proposed novel oject trcking strtegy nmed sed on multi-level rchitecture for efficient oject trcking nd rel-time recovery of missing ojects y mining the movement log in sensor networks. Although there exists mny studies exploring vrious reserch topics regrding sensor networks, few of them discuss rel-time oject trcking nd recovery issues. To the est of our knowledge, this is the first work iming t the gol of rel-time oject trcking nd recovery while tking considertion of energy sving issues simultneously. Through empiricl evlution nd sensitivity nlysis under vrious system conditions, the proposed method is shown to perform excellently in terms of energy efficiency nd timeliness. To evlute the performnce of the proposed method, we conducted series of experiments using simultor reflecting resonle OTSNs environment, s in relted studies (Egle et l., 2006; Hung et l., 2003; Lin et l., 2006; Wu et l., 2001), nd (Xu et l., 2004). The evlution cn e divided into three prts: (1) log mining; (2) oject trcking; nd (3) oject recovery. For log mining, the size of movement log is vried to evlute the performnce of in terms of execution time, nd it is shown tht our method is sclle with the increse of movement log size. For the oject trcking, we oserved tht requires lower verge energy consumption thn does using different scles of sensor networks nd different velocities of moving ojects. For oject recovery, the verge serch time of is lower thn tht of under different velocities of moving ojects, nd the miss rte of is lso lower; even under the strict constrints of dedline threshold. The ove experiments demonstrte tht the proposed method outperforms under different kinds of system conditions. As to future work, we will try to deploy sensor network with multi-level structure to collect rel dt for further nlysis; we will lso try to pply other distriuted clustering lgorithms to uild different sensor network rchitectures. Moreover, we will pply to rel dtsets to evlute the performnce of the proposed strtegies. In ddition, since cn e exploited in wide pplictions, cn e lso pplied to other pplictions such s vehicle monitoring with the im to enhncing the qulity of new pplictions in sensor networks. Acknowledgements This reserch ws supported y Ministry of Economic Affirs, Tiwn, ROC under Grnt No. 95-EC-17-A-02-51-024, nd y Ntionl Science Council, Tiwn, ROC under Grnt No. NSC 96-2221-E-006-143-MY3. References Agrwl, R., Sriknt, R., 1994. Fst lgorithms for mining ssocition rules in lrge dtses. In: Proceedings of 20th Interntionl Conference on Very Lrge Dt Bses, Sntigo de Chile, pp. 487 499

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An energy-efficient MAC protocol for wireless sensor networks. In: Proceedings of the 21st IEEE Infocom, 2002, pp. 1567 1576. Younis, O., Fhmy, S., 2004. HEED: hyrid, energy-efficient, distriuted clustering pproch for d hoc sensor networks. IEEE Trnsctions on Moile Computing 3 (4), 366 379. Vincent S. Tseng received his Ph.D. degree from Ntionl Chio Tung University, Tiwn, R.O.C., in 1997, mjored in computer science. He ws invited s postdoctorl reserch fellow in Computer Science Division of University of Cliforni t Berkeley, USA during Jnury 1998 nd July 1999. Since August 1999, he hs een the fculty t Deprtment of Computer Science nd Informtion Engineering t Ntionl Cheng Kung University (NCKU), Tiwn. He hs cted s the director for Institute of Medicl Informtics of NCKU since August 2008. During Ferury 2004 nd July 2007, he hd lso served s the director for Informtics Center in Ntionl Cheng Kung University Hospitl, Tiwn. He is on the editoril ord of Interntionl Journl of Dt Mining nd Bioinformtics nd the ord memer of Tiwn Bioinformtics Society. He ws lso the ord memer of Tiwnese Assocition for Artificil Intelligence during 2003 nd 2007. He lso served s the dvisory memer for Deprtment of Helth, Tiwn on Tiwn s Hospitl Informtion System specifiction. Dr. Tseng hs wide vriety of reserch interests covering dt mining, iomedicl informtics, We technology nd multimedi dtses. He hs pulished more thn 100 reserch ppers in referred journls nd interntionl conferences. He hs lso held or filed more thn 10 ptents in USA nd R.O.C. He is memer of IEEE, ACM nd honorry memer of Phi Tu Phi Society. Dr. Tseng hs lso served s progrm committee/chirs for numer of interntionl conferences relted to dt mining nd iomedicl informtics. Eric Hsueh-Chn Lu received the B.S. degree in computer science nd informtion engineering from Ntionl Tiwn University of Science nd Technology, Tiwn, R.O.C., in 2003. He is currently pursuing the Ph.D. degree in the Deprtment of Computer Science nd Informtion Engineering, Ntionl Cheng Kung University, Tiwn, R.O.C. His reserch interests include dt mining, moile computing, oject trcking in sensor networks, nd intelligent trnsport systems.