Energy efficient target tracking algorithm using cooperative sensors

Journal of Systems Engineering and Electronics

Vol. 23, No. 5, October 2012, pp.640–648

Energy efficient target tracking algorithm usingcooperative sensors

Chun Zhang1,2,* and Shumin Fei1,2

1. School of Automation, Southeast University, Nanjing 210096, P. R. China;2. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education,

Southeast University, Nanjing 210096, P. R. China

Abstract: Target tracking is one of the applications of wirelesssensor networks (WSNs). It is assumed that each sensor hasa limited range for detecting the presence of the object, and thenetwork is sufficiently dense so that the sensors can cover thearea of interest. Due to the limited battery resources of sensors,there is a tradeoff between the energy consumption and trackingaccuracy. To solve this problem, this paper proposes an energy ef-ficient tracking algorithm. Based on the cooperation of dispatchers,sensors in the area are scheduled to switch their working modeto track the target. Since energy consumed in active mode ishigher than that in monitoring or sleeping mode, for each sam-pling interval, a minimum set of sensors is woken up based onthe select mechanism. Meanwhile, other sensors keep in sleepingmode. Performance analysis and simulation results show that theproposed algorithm provides a better performance than other exi-sting approaches.

Keywords: target tracking, wireless sensor network, energy con-sumption, tracking accuracy.

DOI: 10.1109/JSEE.2012.00080

1. Introduction

Wireless sensor networks (WSNs) consist of many sensornodes deployed in a region of interest. Each sensor node iscapable of sensing, storing, and processing environmentalinformation, and communicating with other sensors via aradio transceiver [1,2]. The WSNs can be used in manyapplication areas. One of the most applications is targettracking, which is used in military affairs, environmentalsurveillance, wildlife studies, and health care [3–6].

The object of target tracking is to track the location of atarget within the accuracy of the range of sensors. The tar-get moves through the sensor field randomly, and the sen-sor field is assumed to be dense enough so that the entire

Manuscript received June 22, 2011.*Corresponding author.This work was supported by the National Natural Science Foundation

of China (60835001).

area of interest is covered by sensors. End users obtainthe location of the target through sink. Sink is the controlcentre.

Due to the limited battery resources of sensors and thedifficult access to sensors once deployed, energy efficiencyis a very important aspect of target tracking algorithms [7–9]. The usage of sleeping sensors for tracking in sensornetworks has been studied in [8,10,11] to conserve en-ergy usage. A novel dynamic self-organization algorithmwas proposed in [11]. To facilitate the sensor clustering,this algorithm determined whether a sensor should trackand locate a target. Targets within the coverage of a clus-ter were tracked by the cluster of sensors only, and othersensors were in sleep mode. Due to the uncertain mobil-ity patterns of the target, many prediction-based protocols[12–14] were presented. Raza proposed an adaptive yawrate aware sensor wakeup protocol for target predictionand tracking [12]. First, a yaw rate aware sensor wakeupprotocol (YAP) was introduced for the prediction of futuretarget locations. Second, the YAP was improved throughthe incorporation of adaptability, and relevant sensors wereselected to determine the target track. A prediction-basedprotocol was presented in [13] to predict the location ofmobile targets and it allowed a number of sensors to trackthe target. To choose a minimal number of sensors, face-aware routing was used to model the network as a unitdisk graph and, eventually, sensors nearest to the targetwere activated. The problem of reducing the sensing de-lay and data transmission delay was also studied in [15].The authors designed a holistic infrastructure to dissem-inate the target information in the network. References[16–19] used the long term history information of the tar-get to predict the path of the target. In the previous research[18] for target tracking, a prediction-based tracking algo-rithm using sequential patterns was designed to achievereduction in the energy consumption while maintaining

Chun Zhang et al.: Energy efficient target tracking algorithm usings cooperative sensors 641

acceptable missing rate levels. Garcia proposed a globalprediction based algorithm [16], which scheduled sensors’energy states based on the history of the target’s move-ments. Sensors can access target’s future location from the“global profile” and only nodes expected to host the targetwere woken up. In order to dispense with many technicalsupports in the global algorithms, a self-organizing track-ing algorithm was put forward [20]. This method avoidsthe need for keeping global position information of eachsensor and broadcasting global positions during system ini-tialization. Therefore, the addition of sensors and occur-rence of failures will not affect the management of sensors.RARE-area and RARE-node algorithms focus on decreas-ing energy consumption by limiting the number of sensorsused to track a target through monitoring their data qualityand by limiting the amount of data being transmitted to thecluster head [21].

In general, each sensor has three working modes duringtarget tracking process: monitoring, active, and sleeping.Monitoring contains sensing and tracking the mobile targetin the area of interest, while active mode ensures the trans-mission of the target’s information collected by the sensorsto end user. The energy consumed in data communicationbetween sensors is generally considered to be magnitudehigher than the energy consumed in monitoring.

Taking this fact into account, in order to conserveenergy, the sensors may be put in monitoring or sleep-ing mode. However, sleeping action at sensors can re-sult in the tracking errors. Therefore, one challengingproblem is the tradeoff between the energy saving andtracking accuracy. In this paper, we solve this prob-lem by placing several dispatchers in the area of interestto schedule ordinary sensors to switch their working modesand track the target. Dispatchers are sensors equippedwith additional energy resource. When a dispatcherconfirms the presence of a target in its supervised re-gion, it will send alarm messages and awaken a spe-cific set of sensors to track the target based on a se-lect mechanism. When the target moves out of the re-gion supervised by this dispatcher, it will alarm otherdispatchers according to the target’s moving direction.

Another challenging problem in the tracking task is thatdue to environment condition or other factors, active sen-sors may have not detected the target at a snapshot, whichimplies that sensors have missed the target. To solve thisproblem, this paper proposes a strategy. When a dispatcherjudges the failure of tracking task at a snapshot accordingto the information sent by sensors, it will send a floodingrequest message over the region to locate the target at first.If the target has moved out of this region, it will inform thesink of the situation. Then the sink will send a flooding

request to all the dispatchers to search the target.The rest of this paper is organized as follows. Section 2

states the problem and provides the proposed cooperativedispatchers based target tracking algorithm. Simulation re-sults and concluding remarks are given in Section 3 andSection 4, respectively.

2. System architecture

2.1 Problem statement

In this paper, we propose a target tracking algorithm basedon cooperative dispatchers (CDTA) to achieve the best en-ergy consumption.

In this section, we introduce the problem formulationand the basic idea of CDTA. Our research focuses on de-veloping an energy efficient tracking algorithm through thecooperation of dispatchers and the organization of sensors.To achieve optimum tradeoff between energy consump-tion and tracking accuracy, for each sampling interval, dis-patchers select a minimal set of sensors to wake up accord-ing to the movement of the target and schedule them to ful-fill the tracking task. To reduce the energy consumption, adefinition of maximum sensing time is proposed. If a sen-sor has not detected the target during the maximum sensingtime after it was awakened, it will switch to sleeping modeautomatically.

The following assumptions are made for our considerednetwork environment in this paper.

(i) Sensors are distributed in the area of interestrandomly, and all the sensors are static;

(ii) The target being tracked is single and finite;(iii) Each sensor knows its location;(iv) Each dispatcher knows the bounds of the square

region which it supervises and the locations of sensorswhich are located within its transmission range;

(v) Sensors sample the target’s movement under thecontrol of the dispatcher for a given time. During thistime, the dispatcher locates the target’s current site basedon the report messages transmitted by sensors and decidesto wake up which sensors for the next sampling interval.

2.2 Energy model

In this paper, each sensor has three components: microcontrol unit (MCU), sensing unit, and radio unit. The threeindividual components are allowed to be turned on sepa-rately. The energy consumption model in [16] is used asthe reference for our work. The total energy consumed bya sensor consists of three parts, i.e., (i) Ec : communica-tion; (ii) Es : sensing; (iii) Edc : data compute.

The communication energy of a sensor consists of trans-mission energy Etr and reception energy Ere on the routefrom the origin sensor to the destination sensor. Equation

642 Journal of Systems Engineering and Electronics Vol. 23, No. 5, October 2012

(1) shows the total energy of a sensor, which is a summa-tion of the individual component’s energy depletion:

Etotal = Ec + Es + Edc = Etr + Ere + Es + Edc. (1)

Generally considered, the energy consumed in datacommunication is much more than other two components.And the energy consumption for transmission is larger thanthat consumed for reception. Additionally, when turningon the radio component of a sensor, a large amount of en-ergy is consumed. Thus the startup energy consumption inthe transceiver circuitry should not be neglected [17].

2.3 Tracking system structure

In this section, we discuss the structure of the target track-ing system. It is composed of two types of sensors, i.e.,executants and dispatchers. The executants are sensors de-signed to be distributed in the monitored area. We assumethe sensing coverage of executants can cover the moni-tored area. To make sure the sensor placement has idealcoverage, each sensor has 4–6 close neighbors, which iscalled a healthy sensor [22]. The dispatchers are sensorsequipped with more energy than executants. The execu-tants detect the moving target, and then report the senseddata to the dispatchers. The data collected by severaldispatchers is transmitted to the sink. End user can usethe gathered real-time information to make application re-search. The dispatchers are intended to serve multiplefunctions.

(i) Under normal conditions, an optimum set ofexecutants tracks the target under a dispatcher’s command.Meanwhile, other executants run in sleeping mode anddispatchers sparingly communicate to save energy;

(ii) When the target moves out of the square regionsupervised by the current dispatcher, it will send alarmmessages to other dispatchers to locate the target;

(iii) If the tracking task failed at a sampling interval,dispatchers will take measures to relocate it.

The architecture of the target tracking system is shownin Fig. 1.

The favorite definition of sensing coverage is the cir-cular model [23]. As shown in Fig. 1, the dispatchershave a transmission radius Rd, whose value could be aconstant like Rd = 40 m. This implies when an execu-tant is in the transmission radius, it can communicate withthe dispatcher directly. We assume the monitored area issquare. Since executants are commanded by dispatchers,to enhance the tracking efficiency, the area is divided intoseveral regions. And in each region, there is a dispatchercentrally located. When a target moves through a region,it is tracked by executants under the command of the dis-patcher in this region. Executants in each region are man-

aged by the corresponding dispatcher, which implies thatthey are attached to the dispatcher. To make sure the moni-tored area is covered by the supervised regions of dispatch-ers and the overlaps are small, the regions are designed tobe squares. The side length of each region is calculated asfollows:

d =√

2Rd. (2)

Fig. 1 The tracking system architecture

2.4 Cooperative dispatchers based target tracking

algorithm

The goal of CDTA is to track target accurately, and mean-while, prolong the lifetime of WSNs. Therefore, on thepremise of insuring the accuracy of a tracking task, thenumber of executants being woken up during each stage ofthe algorithm should be as few as possible. Based on thetarget’s motion state, the algorithm consists of four steps.

Step 1 InitializationInitially, the target is outside the area of interest, called

monitored area. Executants near the area edges are wokenup by the sink. We suppose the sensing range of execu-tants is Re. If the vertical distance between the area borderand an executant is shorter than Re , the probability thatthis executant senses the target is 1 when the target crossesthe boundary into the area and moves towards it. Mean-while, other executants and dispatchers keep in sleepingstate. As illustrated in Fig. 1, when the target follows thetrack L1 to the square region Q1, it will be sensed by ex-ecutants located on the fringe of Q1. Then dispatcher D1will be noticed. Another circumstance also exists. The tar-get can be detected by executants placed in two regions.For instance, when the target moves into the area throughthe intersection of two neighboring dispatchers’ supervisedregions, the two dispatchers will be noticed. According tothe tracking approach in Step 2, the location of the target


at the time can be calculated. Each of the two dispatcherschecks whether the target is within its supervised region.If not, it will switch to sleeping mode.

Step 2 Tracking within a square region

Each executant, which has detected the target transmitsa message to the corresponding dispatcher, is containingits identification, residual energy and the target’s motioninformation. Then the dispatcher assigns three executantsto track the target according to the select mechanism pro-posed in Section 2.5. One is called primary tracker to hostthe target, and other two are called subordinate trackers. Inthis paper, the traditional trilateration [24] is used to locatethe target. The present location of the target is obtainedthrough distances from the target to the primary tracker andsubordinate trackers. As the target continues moving, thedispatcher will reselect three trackers for the next samplinginterval. Since the moving direction of the target is uncer-tain, the executants, which the target may move towards,should be awaken to monitor the target. Section 2.3 haspointed out that each executant has 4–6 close neighbors.Meanwhile, the distance which the target moves away fromthe previous primary tracker will not be long during a shorttime interval. Therefore the previous primary tracker andits neighbors are commanded to be in sensing state. Hence,they can detect the target during the next sampling interval.The dispatcher will select trackers from them. Additiona-lly, as proposed in Section 2.2, a large amount of energyis consumed when the radio unit of a sensor is turned on.Hence, to reduce the number of turning on the radio unit,the awakened executants will keep active until the targetmoves out the region.

Step 3 Tracking from a square region to another

Each dispatcher’s transmission range has three or foursections in its neighboring dispatchers’ supervised region.And as stated in Section 2.1, the locations of executantsin these sections are known by the dispatcher. Hence adispatcher can also schedule these executants to track thetarget. If the target is detected within one of these sec-tions, the current dispatcher will notice the correspondingneighboring dispatcher and send the target’s current loca-tion to the dispatcher. Then it switches to sleeping mode.During the next sampling interval, the corresponding dis-patcher wakes executants up which are within the distanceRe from the target’s current location. And the target islocated using the approach proposed in Step 2. As illus-trated in Fig. 1, when the target follows the track L1 to theshaded area S1, dispatcher D1 will alarm dispatcher D3.Then D1 changes its state to sleeping state and D3 wakesup to organize executants tracking the target. There is an-other circumstance. The target can move from one squareregion to another through a vertex of the square, which

implies that when the target moves out the current region,the dispatcher cannot judge which region it will move to.We also take Fig. 1 as an example. After the target fol-lows the track L1 to the vertex of Q3, it may either moveto Q4, Q5, Q6 or return to Q3. Thus, when the target isclose to vertex, dispatcher D3 will notice D4, D5, and D6.Then the three dispatchers will awaken executants withinthe distance Re from the vertex. At the next time, when adispatcher detects the target within its square region, otherthree dispatchers will be noticed to move to sleeping mode.

Step 4 Remedial workWhen the current active dispatchers fail to track the tar-

get, two measures will be taken. At First, the dispatcherwill transmit a flooding message to executants attached toit to search for the target. If the target has not been found,the sink will wake up all dispatchers to track the target.

In the CDTA, in order to achieve the efficient tradeoffbetween energy consumption and tracking accuracy, dis-patchers and executants adjust their working state to thetarget’s movement. Fig. 2 gives the transform conditionsof working modes for a sensor.

Fig. 2 Transform conditions of working modes for a sensor

Fig. 2 shows that during a sampling interval, most sen-sors are in sleeping state, since the energy consumed incomputing unit and sensing unit is much less than that inradio unit. Only sensors close to the target are noticed tochange their state to be active. When the target movesaway, those active sensors change to sensing state if it isalso within the current region. Since the target may returnto its previous location, if these sensors have been asleep,turning on the radio unit will consume a large amount ofenergy. The transform mechanism is based on the selectmechanism. The detailed select approach used in CDTAwill be proposed in Section 2.5.

2.5 Select mechanism

As presented in Section 2.4, at each stage, the dispatcherselects several executants to sense the target, which arecalled candidates. The dispatcher will assign three exe-cutants from these candidates to be trackers.


The trackers are intended to serve two functions as fol-lows.

(i) According to the distances from the trackers to thetarget, the target location is obtained through trilateration;

(ii) Candidates for the next sampling interval are rese-lected according to the location of current primary tracker.The detailed approach has been stated in Section 2.4.

The probability of selecting an executant as tracker isdetermined by three factors: (i) distance from the target;(ii) the residual energy; (iii) the moving state of the target.

Some definitions are given as follows:(i) C(t) : the set of candidates at time t;(ii) ETi : the executant whose identification (Id) is i;(iii) θi(t) : the distance factor of the ETi at time t;

θi(t) =1

di(t)(3)

where di(t) is the distance between the ETi and the targetat time t;

(iv) wi(t) :the energy factor of ETi at time t;

wi(t) =Eresiduali(t)Einitiali(t)

(4)

where Eresiduali(t) and Einitiali(t) represent the residualenergy and initial energy of ETi at time t respectively.In order to balance energy consumption, executants withenough battery power have more chances of becomingtrackers.

(v) ηi(t) :the movement factor at time t;This factor implies that the target is moving to or mov-

ing away from ETi at time t [20]. The movement directionof the target is assumed to be invariable for a short time in-terval.

ηi(t) ={

k, di(t − 1) > di(t); k > 11, di(t − 1) < di(t)

. (5)

Concluded from the three factors, the probability of theETi assigned to be a tracker is defined as follows:

Pi(t) =θi(t)wi(t)ηi(t)∑

ETj∈C(t)

θj(t)wj(t)ηj(t). (6)

After candidates send data packets to the dispatcher, theprobability of each candidate is obtained. The candidatewith maximum probability will be assigned to be the pri-mary tracker.

In the area of interest, the target is tracked through coo-peration among executants, dispatchers and the sink. Sinceseveral executants share a channel, in order to increase thedata packets throughput and decrease the transmission col-lision, the communication between executants and the dis-patcher needs a unique communication mechanism. Fig. 3

illustrates the communication mechanism between execu-tants and the dispatcher.

Fig. 3 Communication mechanism between candidates and the dis-

patcher

After a dispatcher wakes the selected candidates, thesecandidates start to detect the target and send data packetsto the dispatcher, which include their residual energy, thetarget information detected and so on. As shown in Fig. 3,from time t1 to t2, the number of candidates is m1 andeach candidate is assigned a time slot to transmit data. Thetime slots m1 + 1 and m1 + 2 are reserved for the dis-patcher. During this time, the missions of dispatcher aregiven as follows:

(i) To assign three trackers according to the informationreported by the candidates from t1 to t2, and compute thelocation of the target at t1;

(ii) To receive commands from the sink;(iii) To select new candidates for the next time interval

(from t3 to t4 ), and notice them to sense the target.Since candidates are dynamically adjusted according to

the target’s moving state, the number of time slots assignedfor candidates is changeable for different sampling inter-vals. The sampling interval is affected by the length of atime slot as follows:

LSpresent = (mpresent + 2) × LTpresent. (7)

where LSpresent and LTpresent are the lengths of samplinginterval and time slot at present, respectively, and mpresent

is the number of candidates at present.In order to capture the target in time, the length of the

time slot should be shortened. Therefore the data packetsof candidates can be sent out as soon as possible.

3. Simulation results

In this section, we give some simulation results to ascertainthe efficiency of our proposed algorithm. We compare ourproposed CDTA with the other two tracking algorithms,i.e., RARE algorithm proposed in [21] and prediction-based tracking using sequential pattern (PTSP) algorithm


proposed in [18], using the following four performancemetrics.

(i) The number of data packets exchanged: the totalnumber of transmissions and receptions in the network;

(ii) Average energy consumption: the average energyconsumption of sensors during each sampling interval;

(iii) Average number of active sensors: the averagenumber of sensors in active mode during each samplinginterval;

(iv) Distance error.In our simulations, sensors are distributed uniformly in

a square field. The monitored field is 400 m× 400 m with-out obstacles. The target follows a randomwaypoint modeland it is outside the sensor field at the beginning. Then itmoves into the field with random motion. The sink lo-cates on the boundary of the field. Sensors track the targetand report its location message to the sink until the tar-get moves away from the field. Simulation results are theaverage results over 50 similar scenarios. The RARE al-gorithm and PTSP algorithm have been implemented andare run on the same trajectories as CDTA. The parametervalues used in the simulation are listed in Table 1.

Table 1 Simulation parameters

Parameter ValueRadio bandwidth of nodes/Kbps 200Radio bandwidth of sink/Mbps 20

Sensing range of sensor/m 50Transmission range of sensor/m 100Size of the simulation area/m 400×400

Energy consumption of active MCU/mW 360Energy consumption of sleep MCU/mW 0.9

Energy consumption of radio transmission/mW 720Energy consumption of radio reception/mW 369

Energy consumption of sensing component/mW 23Event packet size/bytes 100

Request packet size/bytes 25Packet header size/bytes 25

In the simulation, we have considered a range for themovement factor k of CDTA: from 1 to 3. The factor isvaried in order to determine its effect on our algorithm.The number of sensors in the WSN will be varied from130 to 170.

The results for the average energy consumption dur-ing each sampling interval in the simulation are shownin Fig. 4(a). CDTA achieves less energy consumptionthan RARE algorithm and PTSP algorithm. Dispatch-ers in CDTA awaken different sensors during differentsampling intervals according to the target’s motion state,while the residual sensors are in sleeping mode. Thismechanism limits the sensors participating in tracking,while in the RARE algorithm, all the sensors keep insensing state. Sensors which detect the target will per-form RARE-area and RARE-node algorithms to obtain

the distance information. This means that compared withthe RARE algorithm, fewer sensors are used to track thetarget in CDTA, so the energy is saved. In the case ofthe PTSP algorithm, sensors use a prediction model topredict the future movement of a moving object. Mean-while, the current sensor finds the destination sensor basedon the paths with a high occurrence frequency in the


Fig. 4 Comparison of performance metrics for PTSP algorithm,

RARE algorithm and CDTA

database. Hence, when there are a certain number of pathsthat a target may follow, the missing rate of the PTSP al-gorithm will become higher than CDTA. Thus, the PTSPalgorithm requires a greater recovery process and the en-ergy consumption of the network will increase. The aver-age number of active sensors (see Fig. 4(b)) in CDTA islow when k is 1, around 77% of the active sensors neededin the RARE algorithm. This result explains why it is moreenergy efficient when k is 1. As k increases from 1 to 3, westill obtain savings, but the amount of energy consumptiongenerally increases. The value of k represents the effect oftarget’s motion status upon the election of trackers. If thevalue of k is large, sensors which are far from the targethave great probability to be trackers. Then these trackers’neighbors will be awaked for the next sampling interval.Since active sensors do not switch to sleeping mode untilthe target moves away from this region, with a large move-ment factor, more sensors will take participate in trackingthe target. As regards the PTSP algorithm, when the tar-get follows the path which the current sensor predicts, thenumber of the active sensor is few. But in application ar-eas, the target chooses a random path, so the predictionfor the path is hard. Compared with CDTA, the missingrate of the PTSP algorithm is higher, and eventually therecovery process will awaken more sensors to search thetarget. Hence, the average number of active sensors in thePTSP algorithm is larger. In fact, as illustrated in Fig. 4(c),the number of transmissions and receptions in CDTA islower than in RARE algorithm and PTSP algorithm. Hav-ing more sensors been awaken means having more chancesof reporting target messages sensed. Meanwhile, whenthe density of sensors in the monitored area increases, the

number of sensors which are activated in the recovery pro-cess will increase. Hence the number of transmissions andreceptions in the PTSP algorithm is larger than the CDTA.In addition, sensors assigned to be candidates only senddata packets to the corresponding dispatchers in CDTA,whereas sensors which have sensed the target will sendbeacons to their neighbors in the RARE algorithm. Hencevarying the density of sensors in the monitored area willcause a change of the number of transmission and recep-tion in the RARE algorithm. Fig. 4(d) illustrates the dif-ferences in distance error among CDTA, RERA algorithmand PTSP algorithm. When the three algorithms are used,the next sampling interval starts after the target locationof the current sampling interval has been obtained. If thesampling interval is long, the tracking accuracy will de-crease. When the number of transmissions and receptionsincreases, the length of sampling interval will increase. Wenote from the graph that compared with CDTA, applicationof RARE algorithm and PTSP algorithm results in an in-crease in the distance error.

Fig. 4 shows that CDTA obtains the best performancewhen the value of k is 1. Hence, when CDTA is used inapplication area, the value of k is set as 1. Fig. 5 high-lights the differences among CDTA, RARE algorithm andPTSP algorithm. In Fig. 5(a), we observe that the energyconsumption ratios of RARE/CDTA and PTSP/CDTA arealways over 100%, which implies a better energetic perfor-mance for CDTA. The average number of active sensorsradio (see Fig. 5(b)), number of data packets exchangedradio (see Fig. 5(c)) and the distance error radio (Fig.5(d)) are also over 100% although they are not linear. Aspresented in Fig. 5(c), RARE/CDTA and PTSP/CDTAdecrease when the density of sensors in the network in-creases. This result implies that when the number of sen-sors is not large enough, the PTSP algorithm and RAREalgorithm need to use large communication resources totrack the target. But the application of CDTA can conserve


Fig. 5 Ratios of the other two algorithms to CDTA on performance

metrics

considerable communication resources under this scenario.These simulation results indicate that CDTA can be usedfor tracking targets in a WSN and our algorithm providesa better performance than the RARE algorithm and PTSPalgorithm.

4. Conclusions

In this paper, we have proposed a CDTA. The area of in-terest is divided into several regions and each region is

supervised by a dispatcher. When a target moves into a re-gion, the corresponding dispatcher will organize a specificset of executants to locate it for each sampling interval.Once the target moves from a region to another, the cur-rent dispatcher will notice other corresponding dispatchersto track it. The transform conditions of working mode forsensors ensure that a minimum set of sensors cooperatesin tracking during each time interval. If the tracking taskfails, two remedial measures will be taken.

We have compared our algorithm with the RARE algo-rithm and PTSP algorithm. Simulation results show thatCDTA provides energy savings without having a negativeeffect on tracking accuracy.

Acknowledgment

The authors would like to thank the associate editor andthe reviewers for their constructive comments and criti-cisms which were very helpful in the improvement of themanuscript.

References[1] A. Mahapatra, K. Anand, D. P. Agrand. Qos and energy aware

routing for realtime traffic in wireless sensor networks. Com-puter Communication, 2006, 29(4): 437–445.

[2] C. L. Na, D. X. Lu, T. X. Zhou. Distributed power controlalgorithm based on game theory for wireless sensor networks.Journal of Systems Engineering and Electronics, 2007, 18(3):622–627.

[3] M. Kuorilehto, M. Hannikainen, T. D. Hamalainen. A surveyof application distribution in wireless sensor networks. Jour-nal on Wireless Communications Networking, 2005, 5: 774–788.

[4] I. F. Akyildiz, I. H. Kasimoglu. Wireless sensor and actorsnetworks: research challenges. Ad Hoc Networks, 2004, 2(4):351–367.

[5] M. Arattano, L. Marchi. Systems and sensors for debris flowmonitoring and warning. Sensors, 2008, 8(4): 2436–2553.

[6] H. W. Tsai, C. P. Chu, T. S. Chen. Mobile object tracking inwireless sensor networks. Computer Communications, 2007,30(8): 1811–1825.

[7] W. L. Yeow, C. K. Tham, W. C. Wong. Energy efficient mul-tiple target tracking in wireless sensor networks. IEEE Trans.on Vehicular Technology, 2007, 56(2): 918–928.

[8] J. A. Fuemmeler, V. V. Veeravalli. Energy efficient multi-object tracking in sensor networks. IEEE Trans. on SignalProcessing, 2010, 58(7): 3742–3750.

[9] Q. Hao, F. Hu, Y. Xiao. Multiple human tracking and identi-fication with wireless distributed pyroelectric sensor systems.IEEE Systems Journal, 2009, 3(4): 428–439.

[10] J. A. Fuemmeler, V. V. Veeravalli. Smart sleeping policies forenergy efficient tracking in sensor networks. IEEE Trans. onSignal Processing, 2008, 56(5): 2091–2101.

[11] Y. H. Hu, X. H. Sheng. Dynamic sensor self-organization fordistributive moving target tracking. Journal of Signal Process-ing Systems, 2008, 51(2): 161–171.


[12] M. T. Raza, Z. H. Mir, A. H. Akbar, et al. Adaptive yawrate aware sensor wakeup schemes protocol (A-YAP) for tar-get prediction and tracking in sensor networks. IEICE Trans.on Communications, 2008, E91B(11): 3524–3533.

[13] M. Z. A. Bhuiyan, G. J. Wang, L. Zhang, et al. Prediction-based energy-efficient target tracking protocol in wireless sen-sor networks. Journal of Central South University of Techno-logy, 2010, 17(2): 340–348.

[14] X. Q. Liu, G. Zhao, X. L. Ma. Taget localization and trackingin noisy binary sensor networks with known spatial topology.Wireless Communications & Mobile Computing, 2009, 9(8):1028–1039.

[15] Z. H. Zhou, X. J. Xiang, X. Wang, et al. A holistic sensornetwork design for energy conservation and efficient data dis-semination. Computer Network, 2011, 55(1): 131–146.

[16] O. Garcia, A. Quintero, S. Pierre. A global profile-based al-gorithm for energy minimization in object tracking sensor net-works. Computer Communications, 2010, 33(6): 736–744.

[17] E. Cayirci, I. F. Akyildiz. User mobility pattern scheme forlocation update and paging in wireless systems. IEEE Trans.on Mobile Computing, 2002, 1(3): 236–247.

[18] S. Samarah, M. Al-Hajri, A. Boukerche. A predictive energy-efficient technique to support object-tracking sensor networks.IEEE Trans. on Vehicular Technology, 2011, 60(2): 656–663.

[19] K. Wu, J. Harms, E. S. Elmallah. Profile-based protocols inwireless mobile ad hoc networks. Proc. of the 26th AnnualIEEE Conference on Local Computer Networks, 2001: 568–575.

[20] Z. Xu, L. Y. Rui. A self-organizing bearings-only target track-ing algorithm in wireless sensor network. Chinese Journal ofAeronautics, 2009, 22(6): 627–636.

[21] M. Guo, E. Olule, G. Wang, et al. Designing energy efficienttarget tracking protocol with quality monitoring in wirelesssensor networks. Supercomputing, 2010, 51(2): 131–148.

[22] H. Chizari, M. Hosseini, T. Poston, et al. Delaunay triangula-

tion as a new coverage measurement method in wireless sensornetwork. Sensors, 2011, 11(3):3163–3176.

[23] S. Parikh, V. M. Vokkarane, L. Xing, et al. Node-replacementpolicies to maintain threshold-coverage in wireless sensor net-works. Proc. of the 16th International Conference on Com-puter Communications and Networks, 2007, (13/16): 760–765.

[24] D. Niculescu, B. Nath. Ad hoc positioning system. Proc.of the IEEE Global Telecommunications Conference, 2001:2926–2931.

Biographies

Chun Zhang was born in 1985. She received herM.S. degree in control theory and control engineer-ing from Anhui University of Technology, Maan-shan, China, in 2008. She is currently pursuingthe Ph.D. degree at the College of Automation,Southeast University, Nanjing, China. Her mainresearch interests include wireless sensor networksand RFID.

E-mail: [email protected]

Shumin Fei was born in 1961. He received thePh.D. degree from Beihang University, Beijing,China, in 1995. From 1995 to 1997, he did post-doctoral research in the Research Institute of Au-tomation at Southeast University, Nanjing, China.He is now a professor, doctoral tutor and the pres-ident of the School of Automation, Southeast Uni-versity, Nanjing, China. His research interests in-

clude robust control, wireless sensor networks, adaptive control and anal-ysis and synthesis of time-delay systems, non-line control system designand integration, hybrid systems analysis, and neural network.E-mail: [email protected]

Documents

Energy efficient target tracking algorithm using cooperative sensors