www.elsevier.com/locate/comcom

Computer Communications 29 (2006) 2494–2505

Distributed and energy-efficient target localization and trackingin wireless sensor networks

Jeongkeun Lee *, Kideok Cho, Seungjae Lee, Taekyoung Kwon, Yanghee Choi

School of Computer Science and Engineering, Seoul National University, San 56-1 Shilim-dong, Kwanak-gu, Seoul, Republic of Korea

Available online 3 March 2006

Abstract

In this paper, we propose and evaluate a distributed, energy-efficient, light-weight framework for target localization and tracking inwireless sensor networks. Since radio communication is the most energy-consuming operation, this framework aims to reduce the num-ber of messages and the number of message collisions, while providing refined accuracy.

The key element of the framework is a novel localization algorithm, called Ratiometric Vector Iteration (RVI). RVI is based on dis-tance ratio estimates rather than absolute distance estimates which are often impossible to calculate. By iteratively updating the estimatedlocation using the distance ratio, RVI localizes the target accurately with only three sensors’ participation.

After localization, the location of the target is reported to the subscriber. If the target is stationary or moves around within a smallarea, it is wasteful to report (almost) the same location estimates repeatedly. We, therefore, propose to dynamically adjust a reportingfrequency considering the target’s movement so that we can reduce the number of report messages while maintaining tracking quality.Extensive simulation results show that the proposed framework combining RVI and the movement-adaptive report scheduling algorithmreduces the localization error and total number of the transmitted messages up to half of those of the existing approaches.� 2006 Elsevier B.V. All rights reserved.

Keywords: Target localization; Tracking; Ratiometric; Energy-efficient; Sensor network

1. Introduction

Wireless sensor networks are systems of small, low-pow-ered networked sensing devices deployed over an interestedarea to monitor interested events and perform application-specific tasks in response to the detected events. One of themost significant and elementary application is localizationand tracking moving targets. The type of interested signalsincludes temperature, sound, light, magnetism and seismicvibration: a sensing modality is determined based on thetypes of targets to be tracked.

Regardless of the various types of targets and trackingenvironments, there are four common procedures involvedin distributed (or decentralized) target tracking applica-tions: First, sensors should be localized prior to participat-ing in target tracking tasks. The importance of sensor

0140-3664/$ - see front matter � 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.comcom.2006.02.004

* Corresponding author.E-mail address: jklee@mmlab.snu.ac.kr (J. Lee).

nodes’s location information have been emphasized in theliterature and many sensor network localization tech-niques, e.g., [8,9,6,10–13] can be used. We assume that eachnode is aware of its location and consider only the otherprocedures in the below.

Second, target localization is required. Here, if the targetis able to communicate with sensors (e.g., a householder inthe intelligent home network), the localization problemgets easier and the same localization technique with sensornetwork localization can be used. In many cases, however,targets are not cooperative with sensors, e.g., enemy vehi-cles and unregistered victims in disaster areas. Most ofthe previous localization algorithms use absolute point-to-point distance estimates. If the target is cooperative withthe sensor network, it is possible to know the originalsignal (interested signal) strength at the target source as apre-defined parameter or through communication. In thenon-cooperative cases, however, the absence of the originalsignal strength information prevents the use of absolute

J. Lee et al. / Computer Communications 29 (2006) 2494–2505 2495

distance estimates. Instead, one can estimate the originalsignal strength by collecting and analyzing a number ofsensing data, which often requires non-linear optimizationtechniques [16,19]. Because of the high computation andcommunication overhead, however, those techniques arenot suitable for low-cost sensors in distributed environ-ments. In order to tackle this problem, we present alight-weight localization algorithm, dubbed Ratiometric

Vector Iteration (RVI) that is based on relative distanceratio estimates rather than absolute distance estimates.RVI not only achieves high accuracy, but also enables adistributed operation in the low-cost sensors.

Third, collaborative data processing among nodes isdesirable because sensing information collected from differ-ent sensors may be redundant and contribute to the localiza-tion result with different importance. Thus, we need a sensor

collaboration algorithm considering sensory data as well asconstraints on energy consumption, latency and other costs.Therefore, the leader is often selected to manage collabora-tive data processing and sensor grouping. The leader alsomaintains the target’s state such as location, speed and direc-tion. Because the target is moving, the state information hasto be forwarded along with the moving target: leader selec-

tion is required. Most previous solutions use explicit groupmanagement (for sensor selection) and leader selectionapproaches which incur the control message overhead and/or assume hierarchical node-cluster deployment. In ourframework, the leader selection and group managementare accomplished implicitly without any additional messageoverhead. With the help of received-signal-strength(RSS)-based backoff timer, we ensure that only a sufficient number(3 in our case) of sensors broadcast messages to determinethe target location with a low collision probability.

Fourth, the tracking system (in particular, the leader)should report the location of the target to a sink node ina timely manner. Usually, the sink is a gateway connectingthe sensor network to the subscriber. Therefore, reportingto the sink normally requires multi-hop message relayingwhose message transmission overhead is proportional tothe hop distance between the leader and the sink. We pro-pose a movement-adaptive report scheduling algorithmthat reduces the report message overhead, while keepingsubstantial tracking quality at the subscriber side.

Because of the low cost requirement and small-form fac-tor design of sensor nodes, the resources available to indi-vidual nodes are highly limited. Although the limitations ofprocessor bandwidth and small memory are expected toweaken with a development of fabrication techniques, theenergy constraint is likely to last for decades. Typical sen-sor nodes are powered by small batteries that are difficultto replace even if not impossible considering the slow pro-gress in battery capacity [1]. In wireless sensor networks, inparticular, communication is the most energy-consumingoperation, with each bit transmission costing as much ener-gy as about 1000 instructions processing [2]. With this inmind, the goals of this paper and the proposed solutionsare given as follows:

• achieving good localization quality with low communi-cation cost: Ratiometric Vector Iteration

• minimizing the number of collisions among messages:RSS-based backoff timer

• reducing the number of transmitted messages: move-ment-adaptive report scheduling.

We begin with Section 2 which gives related work. InSection 3, we introduce a novel localization algorithm,RVI, and show its advantages. In Section 4, we give someassumptions and describe the target tracking cycle com-posed of three steps. The message collision probability isanalyzed in Section 5 and we present simulation results inSection 6. Section 7 concludes this paper.

2. Related work

Localization: Many existing localization algorithms forwireless sensor networks employ a centralized operationand/or use an absolute distance estimates. A large volumeof sensing data are gathered from sensors and processed ina centralized manner. The gathered data are applied tomultidimensional scaling [8], convex optimization [9], max-imum likelihood testing [6], and so on. High computationaloverhead, however, prevents the use of those centralizedlocalization methods in distributed, low-cost, low-powersensor networks. Moreover, the centralized methodrequires all sensory data to be delivered to the high-endnode, which incurs high communication overhead.

Absolute point-to-point distances estimated from RSS ortime-of-arrival (TOA) or time-difference-of-arrival (TDOA)information are often used for localization. Cricket [10] isthe well-known solution based on absolute distances esti-mated from TDOA between a radio signal and an ultrasound signal. Some distributed localization algorithms[11,12] do not use the absolute distance estimates but requirecommunication between the target and sensors, which isimpractical in non-cooperative scenarios assumed in thispaper. Centroid [13] is a distributed solution which is notdependent on absolute distance estimates. Nodes localizethemselves to the centroid of reference points (target-detect-ing sensors in this paper) considering poor distance esti-mates from the RSS. The proposed Weighted Centroidalgorithm in this paper is an advanced version of Centroid.

All of the above solutions are suitable for localizing sen-sor network itself or cooperative targets. For the non-coop-erative target localization, several solutions [16,19] havebeen proposed in the literature. As will be discussed in Sub-section 3.2, the previous solutions have some limitations ofhigh computation overhead and/or hierarchical sensordeployment.

Target tracking: Collaborative data processing and in-network processing have been extensively studied to reduceredundant communication in target tracking applications.Clustering or group-based techniques such as informa-

tion-driven sensor query (IDSQ) [14] and dynamic convoy

tree-based collaboration (DCTC) [15] are well-known solu-

2496 J. Lee et al. / Computer Communications 29 (2006) 2494–2505

tions for collaborative data processing. They explicitlyselect and manage sensors that participate in target track-ing. However, the group management and the hierarchicalstructure maintenance overhead are not negligible andeventually incur additional expenditure of energy for com-putation and communication. Our approach performs col-laborative target tracking implicitly with small messageoverhead through RVI and RSS-based backoff timer thatguarantees low collision probability. A dynamic clusteringand Voronoi diagram-based target tracking framework isproposed in [16] for acoustic target tracking. Pre-construc-tion of neighborhood Voronoi diagram and the use of two-phase random backoff timer enable efficient cluster-headand sensor selections. However, it requires a designatedpowerful cluster heads’ deployment and the localizationaccuracy depends on the nature of the approximation tech-nique’s poor accuracy. The proposed approach of thispaper supports a fully distributed operation while render-ing good accuracy.

Some algorithms such as [17,18] predict the future targetposition based on the assumption of a linear target trajecto-ry. Such prediction-based approaches, however, are notrobust when the prediction is wrong. Our tracking frame-work does not predict or maintain target history but keepsonly the most recently reported target location and thereporting time instance. Also proposes an adaptive protocolthat controls the frequency of localization based on thevelocity of moving sensors [18]. The idea of reducing messageoverhead by adapting to the sensor movement is similar tothe proposed report scheduling algorithm of this paper.However, [18] considers the localization of moving sensors

while we deal with the tracking of a moving target in thelocalized sensor network. We also discuss the additionaluse of report messages for local time synchronization.

3. Target localization

3.1. Sensing model

We use a conventional received-signal-strength (RSS)based sensing model in which RSS decreases exponentiallywith the propagation distance:

ri ¼a

jX � Sijaþ ni; 1 6 i 6 N ; ð1Þ

where ri is the sensed RSS value in the ith sensor, a is theoriginal signal strength at the target source, X is the targetlocation in two-dimensional coordinate system, Si is thelocation of ith sensor, |X � Si| means the Euclidean dis-tance between X and Si, a is the pathloss exponent and ni

is the White Gaussian noise with Nð0; rÞ. N is the numberof sensors around the target. Again, X and Si are elementsof R2 in this paper. We assume that ri, Si, and a are knownvalues, while a and X are unknown. Since a is unknown, itis difficult to estimate an absolute point-to-point distance.

We can use a relative distance ratio, ri/rj, (i „ j). By dis-regarding the noise, the ratio between RSS in the ith sensor

and jth sensor, r�1=ai =r�1=a

j represents the ratio of the target-to-ith-sensor distance to the target-to-jth-sensor distance asfollows:

r�1=ai : r�1=a

j ¼ jSi � X j : jSj � X j; ð2Þ

where i „ j, 1 6 i, j 6 N.Before describing the proposed RVI algorithm, we first

introduce two existing approaches for the purpose ofcomparison.

3.2. Previous approaches

Approximation: The location of a sensor with the highestreceived-signal-strength can be used as the approximate

location of the target [16]. This is the simplest method,but its localization accuracy is poor: the localization erroris about a half of the inter-sensor distance. In [16], a Voro-noi diagram is exploited to bound the error of the aboveapproximation in the presence of sensing noise. This voro-noi-diagram based approach, however, has some limita-tions. First, the noise still affects the approximation errorwhen the Voronoi diagram is constructed. Second, thisapproach requires a static backbone of hierarchicallyplaced high-capability sensors. Third, because a measuredRSS of a sensor should be compared with that of all Voro-noi neighbors of the sensor, the number of participatingsensors (Voronoi neighbors) may increase overwhelminglyas the sensor node density increases, especially in a randomsensor distribution.

Nonlinear optimization: Given a pair of sensing data(ri, rj) and the locations of both sensors (Si, Sj), the locusof the potential location of the target X can be shown tobe an Apollonius circle: the set of all points whose distanc-es from two fixed points (Si,Sj) are in a constant ratio|X � Si| : |X � Sj|. For noisy measurements, the unknowntarget location X is estimated by solving a nonlinear leastsquare problem of the form [16,19]:

X ¼ arg minXm

k¼1

jX � Okj2 � p2k

p2k

;

where m is the number of given ratios, Ok is the centercoordinates, and pk is the radius of the Apollonius circleassociated with the kth ratio. Newton’s method [20] canbe used to solve the nonlinear least square optimizationproblem. This sophisticated method promises good local-ization results at the expense of high computationalcomplexity: it is not a suitable solution for low-costsensors.

3.3. Proposed algorithm description

We describe a novel localization algorithm using a dis-tance ratio, Ratiometric Vector Iteration (RVI). Beforedescribing RVI, we first introduce the weighted centroidtechnique which will be used to obtain an initial guess closeto the true target location for RVI’s iterative estimations.

J. Lee et al. / Computer Communications 29 (2006) 2494–2505 2497

Weighted Centroid: Given locations of k (k P 3) sensors(S1, . . . ,Sk) and sensing data (r1, . . . ,rk) from those sensors,the location of target in a two-dimensional Euclidean planecan be estimated as a weighted centroid of sensor’s loca-tions where each weight (w1, . . . ,wk) on each sensor’s loca-tion is characterized by

w1 : w2 : . . . : wk

¼ 1

jS1 � X jb:

1

jS2 � X jb: . . . :

1

jSk � X jb;

¼ rb=a1 : rb=a

2 : . . . : rb=ak ;

where b determines a weight function. That is, b = 1 meansthat the weight is an inverse of distance and b = 2 implies asquare inverse function. Note that, if a = b, sensing datacan be used as weights without additional algebraic opera-tion, that is wi = ri. The target location estimate by Weight-ed Centroid, XWC, is given by

X WC ¼Pk

i¼1wiSiPki¼1wi

.

We call this algorithm as a weighted centroid.Ratiometric Vector Iteration: In Fig. 1, if X and Xj rep-

resent the true location of the target and the estimatedlocation at the jth iteration respectively, we can move Xj

‘‘toward’’ X such that the difference between a distance

ratio jS1X��!j : jS2X

��!j : jS3X��!j ¼ 2 : 1 : 2 and a distance ratio

jS1X j��!j : jS2X j

��!j : jS3X j��!j ¼ 1 : 3 : 3 decreases. Note that

jSiX j��!j ¼ jSi � X jj. Vectors SiX j

��!are multiplied by the ratio

difference and summed up to compose a vector V j�!

whichis eventually added to the Xj. This vector translation of Xj

by V j�!

is repeated until Xj is sufficiently close to the truelocation X.

As inputs to the RVI algorithm, locations of k (k P 3)sensors (S1,S2, . . . ,Sk) and sensing data (r1,r2, . . . , rk) fromthose sensors are given. Then, the distance ratio is repre-sented by using sensing values r�1=a

i , and we normalize itby the sum

Pki¼1r�1=a

i for the purpose of comparison:

Fig. 1. Ratiometric Vector Iteration.

jS1X��!j : . . . : jSkX

��!j ¼ r�1=a1 : . . . : r�1=a

k

¼ g1 : . . . : gk

where gi ¼r�1=a

iPki¼1r�1=a

i

:

The goal of RVI is to update Xj such that the differencebetween jS1X j

��!j : . . . : jSkX j��!j and g1: . . . :gk decreases. This

translation of Xj is iterated until the direction of Vj drasti-cally changes or the normalized moving distance of Xj

becomes smaller than a pre-determined threshold sizeCth. A generalized algorithm description is given as follows:

0. Initialization: The target location estimate at the jthiteration is denoted by Xj. The location estimatedby weighted centroid, XWC is used as the startingpoint for the first iteration, i.e., X0 = XWC. The mov-ing vector is initialized as zero, i.e., V 0 ¼ 0

!. Set the

iteration index j = 0.1. Normalization: For comparison with g1: . . . :gk, the

ratio jS1X j��!j : . . . : jSkX j

��!j is also normalized by thesum

Pki¼1jSiX j��!j and is given by

jS1X j��!j : . . . : jSkX j

��!j ¼ g1;j : . . . : gk;j;

where gi;j ¼��SiX j��!��

Pk

i¼1

��SiX j

���!�� :2. RVI Move: The move vector V j

�!is given by

V j�! ¼Xk

i¼1

ðgi � gi;jÞSiX j��!jSiX j��!j :

Each element vector SiX j��!

is normalized by jSiX j��!j be-

cause the vector only indicates the direction and thevector size is determined by the difference between ra-tios (gi � gi,j).

3. New Estimate: Update

X jþ1 ¼ X j þ V j�!

. ð3Þ4. Stationary Point: The algorithm terminates if

V j�1��! � V j

�!< 0

or

jX jþ1 � X jjDinter sensor

< Cth

where Dinter_sensor denotes the pre-determined averageinter-sensor distance, and Cth is the threshold for a ter-minating condition. In other words, the algorithmstops when the direction of V j

�!drastically changes

from the direction of the previous iteration vectorV j�1��!

, or the moving distance at the jth iteration (nor-malized by the average inter-sensor distance) becomessmaller than Cth. The condition V j�1

��! � V j�!

< 0 pre-vents infinite pingpong repetition around the target.Onthese termination conditions, RVI stops and returnsthe target location estimate as the algorithm output:

Fig. 2. Examples of Ratiometric Vector Iteration.

2498 J. Lee et al. / Computer Communications 29 (2006) 2494–2505

X RV ¼ X jþ1.

Otherwise, the index j is incremented, and the algo-rithm goes to step 1.

Fig. 2 shows the six results of our test program withthree sensors (k = 3) which forms a regular triangle assum-ing noiseless sensing. An arrow indicates the true targetlocation and small points represent a convergence of targetestimates during iterations. An eyeball-like circle at thebeginning indicates the starting point XWC. When the tar-get resides inside the triangle (convex area) formed by threesensors, the RVI algorithm accurately estimates the targetlocation as shown by the case A. Moreover, the case Cshows that the target location estimate is accurate evenwhen the target is outside the triangle but inside the largedotted circle. Generally speaking, if the target is locatedinside the circumscribed circle passing through all threesensors, RVI accurately estimate the target location. Ifthe target is outside the circumscribed circle, as shown bycases B and D, the target tends to be localized at the oppo-site of the true target location along the line passing the tar-get location and the center of the circumscribed circle. Asthe target goes farther from the circle, the estimated loca-tion becomes closer to the center of the circle, which meansthe estimation error increases.

As shown in Fig. 2, this approach takes a large number(up to 50) of iterations to accurately point the true loca-tion. It is because the ratio difference (gi � gi, j) and, even-tually, the step size j V j

�!j gets smaller as Xj becomescloser to the target location X. Thus, in order to reducethe number of iterations for convergence, we rewrite Eq.(3) to set the step size as a constant, c:

X j ¼ X j�1 þ Dj;

Dj ¼c

V j�!�� V j�!�� if j > N init and j V j

�!j < c;

V j�!

otherwise.

8>><>>: ð4Þ

In the first Ninit number of iterations1, the moving distanceDj is allowed to be smaller than c so that iteration vectorscan progressively change its direction toward the targetlocation. If the initial iterations have an inappropriate largevector size, the direction of the move vector would changedrastically, and the algorithm will stop by the conditionV j�1��! � V j

�!< 0 before getting closer to the true location.

The algorithm terminates in a finite number of iterationsby the termination conditions. With Eq. (4), in particular,the condition V j�1

��! � V j�!

< 0 guarantees the termination ofalgorithm around the true target location X because theconstant step size may not achieve fine-grained conver-gence, and eventually the direction of the move vectorchanges drastically (more than 90�) around the truelocation.

1 Ninit = 3 in this paper.

There is a tradeoff between the estimation accuracy andthe total number of iterations. As the step size c increases,the algorithm converges to the target location with the lessnumber of iterations, and the estimation error |XRV � X|increases, and vice versa. When the new Eq. (4) is appliedto the test program with c = 0.1 while the inter-sensor dis-tance is 1.0, the maximum number of iterations decreasesto 5 and the algorithm typically performs 3–4 iterations.With this configuration, the average error is about 0.05,half of the step size c.

It can be shown that the time complexity of RVI is O (kl)where RSS and location information are given from k sen-sors, and l is the number of iterations. We can limit thenumber of participating sensors (in this paper, three sen-sors) and limit the number of iterations to a constant num-ber (by adjusting the step size c): the complexity becomesO (1).

3.4. Target-in-triangle/target-in-circle issues

In the previous subsection, as long as the targetresides within the circumscribed circle, RVI achievesgood localization results (half of the step size, on theaverage). In contrast, if the target is outside the circum-scribed circle, the estimation error increases up to thedistance between the target location and the center ofthe circle.

To observe the probabilities that the target is inside thetriangle (convex area) and the circumscribed circle of thethree closest sensors, an intensive simulation work is per-formed by using Qualnet network simulator [4]. A totalof 400 sensors are deployed in the area of 190 · 190 m2.To be free from the edge effect, the target moves only inthe inner area of 150 · 150 m2 and the edge area within20 m from the area boundary are excluded. Table 1 showsthe probability of having the target inside the triangle/cir-

Table 1Probability of target-in-triangle/circle

Sensor placement Grid Uni Rnd

Triangle 1.0 0.41 0.25Circumscribed circle 1.0 0.82 0.73

J. Lee et al. / Computer Communications 29 (2006) 2494–2505 2499

cumscribed-circle formed by the three closest sensors fromthe target. Grid, uniform (Uni)2, and random (Rnd) nodeplacement strategies are examined. Note that the targetlocation is inside the circumscribed circle with high proba-bility (0.73–1.0) compared to the triangle case. This is themerit of RVI because it can more effectively handle the geo-metric dilution of precision (GDOP) problem than tradi-tional geometric positioning systems.

By using additional information about target states,such as its trajectory and moving direction, more appropri-ate convex-area-forming sensors can be selected such thatthe target resides within the triangle/the circumscribed cir-cle with higher probabilities. The use of Voronoi diagram[16] can find 3 convex-area-forming sensors with the extracomputation and communication overhead. Another wayto increase the above probabilities is to make a more num-ber of sensors (k > 3) to participate in a target localization.However, the increased number of sensors indicates theincreased message overhead: in this paper, we confine tothe case with three sensors which is the minimum numberto localize a target in a two dimensional space.

4. Target tracking framework

The proposed framework consists of three main proce-dures: (a) sensing and buzzing, (b) leader selection andlocalization, and (c) reporting to sink nodes. Before elabo-rating on each procedure, we first introduce networkassumptions.

4.1. Assumptions

First, we assume that each sensor is aware of its ownlocation and stationary. These are common assumptionsfor many sensor network applications. For simplicity andease-of-description, we assume isotropic signal propaga-tion from the target. Thus, the sensing area of a sensor isshaped as a circle with a radius R centered at the locationof the sensor. Likewise, the sensing area for a target alsoforms a circle centered at the location of the target. Becausethe original signal strength at a target, a, is unknown in thesensing model of (1), the radius R is also unknown. Weassume that a has its upper bound amax and the ith sensordetects the target when the RSS value ri exceeds a pre-de-termined threshold rth. Therefore, a sensing range R alsohas an upper bound Rmax which is derived from (1):

2 In uniform distribution, the area is equally divided into a number ofcells based on the number of sensors. Within each cell, a sensor is placedrandomly.

ri ¼ada

i

> rth () di < a1=a � r�1=ath

) Rmax ¼ a1=amax � r

�1=ath ;

ð5Þ

where di = |X � Si|. And we assume that sensors can directlycommunicate with the neighboring sensors within a radius atleast 2Rmax. This assumption is typical and even conservativeconsidering the field data. For example, MICA II Berkeleymote [3] has a transmission range of up to 300 m in openspace, while the sensing range is at most 30 m even withlong-range infrared sensors. Photoelectric sensors andacoustic sensors have a sensing range of about 10 m.

The second assumption is a coarse-grained time syn-chronization among neighboring nodes, which can be eas-ily achieved by current millisecond-level synchronizedsolutions. This level of time synchronization is alsorequired by MAC protocols such as S-MAC [5]. In ourframework, the leader periodically reports the estimatedtarget location, which will help the sensors nearby the tar-get to be synchronized.

Third, it is assumed that different targets are far enoughapart that each sensor can detect only one target at a time.Detecting the presence of multiple targets and trackingthem require additional sensing and signal processing algo-rithms [6,7] which are beyond the scope of this paper.

4.2. Target tracking cycle

4.2.1. Target sensing and buzzing

Fig. 3 shows that the three procedures compose a track-ing cycle. Each sensor that detects a signal whose RSSexceeds the threshold rth broadcasts a buzz to its one-hopneighbors. A buzz from the ith sensor contains the sensor’scoordinates (Si) and the RSS (ri).

Each target-detecting sensor sets a backoff timer andwaits until the timer expires. Specifically, the backoff timerof the ith sensor, Bi, is

Bi ¼ W size �r�1=a

i

r�1=ath

; ð6Þ

where Wsize denote the backoff window size. In summary, therange of the backoff timer value is [0, Wsize]. Before the timerexpires, the ith sensor keeps listening to other sensors’ buzzesand if it receives three or more buzzes, it cancels its backofftimer. This ensures that only three sensors generate buzzes.Those sensors will have the highest RSS values among tar-get-detecting sensors with a high probability.

In Section 5, we will show that Bi is linearly proportion-al to the distance between the target and the ith sensor.This property guarantees that the first three buzzes willbe transmitted successfully with a low collision probability(Lemma 1).

4.2.2. Leader selection and localization

After Wsize time has passed from the time of backoff timersetting, the sensor with the highest RSS volunteers to become

Fig. 4. Traking cycle timing.

Fig. 3. Target tracking cycle.

2500 J. Lee et al. / Computer Communications 29 (2006) 2494–2505

a leader. Then, the leader localizes the target using three RSSvalues including its own RSS, and uses the location of threesensors are obtained. Suppose the total number of target-de-tecting sensors is less than three or the number of successfullytransmitted buzzes without collision is less than three. Then,the leader cannot perform RVI (neither weighted centroid).In this case, the target location is estimated by approxima-tion of Subsection 3.2. Otherwise, at least three RSS valuesare available: the leader performs RVI.

4.2.3. Movement-adaptive report scheduling

After localizing the target, the leader is obliged to reportthe estimated location to the sink node. Usually, the sink isa gateway connecting the sensor network to subscribers.Therefore, reporting to the sink normally incurs multi-hop message relaying and multiple transmissions whichdepends on the hop distance between the leader and thesink. If the target is stationary or moves around within asmall area, the estimated location does not change notablyas time goes by and the same location estimate (or possiblywith small deviation) is repeatedly reported to the sink. Inthese cases, it is not necessary and even wasteful to report(almost) same location estimates repeatedly.

Rather than reporting every time leader performs locali-zation, we propose to dynamically schedule a reporting peri-od considering the target’s movement (e.g., velocity).Specifically, the current leader compares the currentlocation estimate Xcurrent with the previously reported loca-tion estimate Xprevious and report to the sink if the distancebetween them |Xcurrent � Xprevious| is longer than Dth. If thetarget has been stationary for a long time, the sink wouldhave received no report and may conclude that the target islost or disappeared. To prevent this, we set an upper boundof the reporting period: if more than Tth time has passed afterthe previous-report-reception time Tprevious, the leaderreports to the sink regardless of the above distance condition.In summary, the leader reports if and only if

jX current � X previousj > Dth

or

T current � T previous > T th; ð7Þwhere Tcurrent denotes the current time. Note that bothTcurrent and Tprevious are not absolute time but relative timeinstance according to the leader’s clock.

If the condition (7) is satisfied, the leader sends out thereport when Wsize time has passed from the backoff-tim-er-setting time in order to avoid collisions between thereport and the buzzes. However, there is a possibility ofcollisions among report messages. Suppose a collisionoccurs between the first two buzzes with the highest andthe second highest RSS values. Then, three more buzzeswill be broadcast by sensors which have detected the third,fourth, and fifth highest RSS values. Because the sensorswith the first and second highest RSS values do not knowthe collision, both of them deem their RSS values as thehighest and volunteer as a leader. Moreover, the sensorwith the third highest RSS also deem itself as a leaderbecause it does not receive the first and second buzzes.Consequently, the three sensors will try to transmit reportssimultaneously. This case only happens when the first buzzcollides (with the second buzz), and the first buzz collisionprobability is derived in the next section. One solution forthis problem is to use a report backoff timer although wedo not include the report backoff timer in the simulation.Because only three reports contend, the report backoff tim-er may have a small window size.

4.3. Tracking cycle timing

Fig. 4 shows a timing diagram of the tracking cycledescribed in the previous subsection. The total cycle timeTcycle is

T cycle ¼ W size þ T tx þ T gap;

where Ttx is the one-hop transmission time of a report mes-sage (we ignore the processing time for localization and thepropagation delay), and Tgap is the time between the re-port-reception time and the backoff-setting time.

Fig. 5. The limitation of Dth for time synchronization.

J. Lee et al. / Computer Communications 29 (2006) 2494–2505 2501

Due to the assumption of 2Rmax communication range,all target-detecting sensors are assumed to have received areport message from the previous leader. Therefore, thetime when sensors receive (actually overhear) the reportmessage from the leader can be used for time-synchroniza-tion among target-detecting sensors. However, thisassumption limits Dth as illustrated by Fig. 5. DLT denotesthe maximum distance between the previous leader (thatsent the previous report) and the previous target location,and Emax is the maximum localization error. In the caseof a grid sensor distribution, DLT can be approximatedusing the inter-sensor distance: DLT ¼

ffiffi2p

2Dinter sensor. The

solid circle and the dotted circle denote the target’s sensingrange and the previous leader’s communication range,respectively. Fig. 5 shows the worst case when the distancebetween the current target location and the previous leaderis the longest. For the purpose of time synchronization, allsensors that detect the current target should reside withinthe communication range of the previous leader:

DLT þ Emax þ Dth þ Emax þ R 6 2Rmax;

) Dth 6 2Rmax � R� Dinter sensorffiffiffi2p � 2Emax.

Suppose that R = Rmax = 25 m (worst case), Dinter_sensor

= 10 m and Emax = 5 m. Then, approximately, Dth must be7.9 m or less.

Because the reporting interval can be extended up toTth(>Tcycle), the leader may not send a report at every Tcycle

interval if the target is stationary. Therefore, a target-de-tecting sensor may have to send buzzes without receivinga report up to b T th

T cyclec times.

5. Analysis of buzz collision probability

In this section, we analyze the probability of successfulbuzz transmission and show how many buzz transmissionsare needed to receive three buzzes successfully. Randomsensor distribution is assumed and the sensor density is giv-en by q.

According to Eq. (6), the backoff timer, Bi, is determin-istically related to the sensing RSS ri. Because the RSS ri is

larger than the threshold value rth by Eq. (5), the termr�1=a

i

r�1=ath

of Eq. (6) ranges (0,1), and from Eq. (5), this term can beexpressed as

r�1=ai

r�1=ath

¼ amax

a

� �1=a� 1

Rmax

� d; ð8Þ

where a is the original signal strength at the target source,amax is the upper bound of a,Rmax is the upper bound ofthe sensing range R, and d denotes the distance betweenthe target and a sensor. Note that a, amax, R and Rmax areconstants, while d is a variable which ranges (0, R). Eq. (8)implies that the backoff timer, Bi is proportional to d. Bythe definition of Eq. (6) and the implication of Eq. (8), therange of backoff time window (0,Wsize) linearly maps to therange of distance (0,R), where the sensing range R is deter-mined by setting ri = rth and a = 2 as follows:

R ¼ dðri¼rthÞ ¼ffiffiffiffiffiffiffiffiffi

aamax

r� Rmax. ð9Þ

We assume that a collision occurs if the transmissiontimes for two buzzes overlap. Due to the carrier sensingcapability, a sensor can withhold its transmission if it sens-es other sensor’s transmission for a carrier sensing time ormore. In other words, if one buzz starts transmission attime t, then any other transmission starting between t � Tcs

and t + Tcs will cause a collision, where Tcs is the carriersensing time (duration). Due to the linear mapping of thebackoff time range (0,Wsize) and the distance range (0,R),Tcs is translated to Dcs, which is the minimum differenceof distances between the target and two sensors requiredto avoid a collision: Dcs ¼ R T cs

W size. Also, the time instance t

(from the timer-setting time) is mapped to the locus of posi-tions whose distance from the target is d: d ¼ R t

W size.

Then, the expected number of transmissions that collidewith a transmission started at time instance t is given by

EcolðdÞ ¼ q �Z minðR;dþDcsÞ

maxð0;d�DcsÞ2px dx. ð10Þ

Then, the probability that a transmission started at timeinstance t is successful without a collision is given by

P sucðdÞ ¼1� EcolðdÞ if EcolðdÞ < 1;

0 if EcolðdÞP 1.

�ð11Þ

Psuc (d) monotonically decreases as d increases. From Eq.(11), Lemma 1 provides an important observation.

Lemma 1. Early buzzes (possibly from the sensors close tothe target) have a higher successful transmission probabil-ity than late buzzes. Therefore, the first three buzzes will betransmitted successfully with a high probability.

Finally, the expected number of attempted transmis-sions in the range of (0, t) is

HðtÞ ¼ H dðdÞ ¼ q �Z d

0

2pxdx ¼ q � p � d2 ð12Þ

and the expected number of the successful transmissions inthe range of (0, t) is

Fig. 6. The average numbers of the attempted transmissions and thesuccessful transmissions by the time t.

Table 2Parameters for the proposed tracking framework

Notation Parameter Default

Wsize Window size (ms) 100Tcycle Cycle time (ms) 1000Tcs Carrier sensing time (ms) 0.32Rmax Maximum sensing range (m) 25c RVI step size (m) 1.5Dth Distance threshold for reporting (m) 5Tth Time threshold for reporting (ms) 5000

The first four parameters are for general configurations; the fifth one isspecific to RVI; the remaining parameters are specific to movement-adaptive reporting.

2502 J. Lee et al. / Computer Communications 29 (2006) 2494–2505

GðtÞ ¼ GdðdÞ ¼ q �Z d

0

ðP sucðxÞ � 2pxÞ dx. ð13Þ

Fig. 6 plots H (t) (dotted line) and G (t) (solid line) whenparameters are set as specified in Table 2, amax = 1, and 400sensors are randomly placed in the area of 200 · 200 squaremeters, i.e., q ¼ 400

2002 sensors=m2. As the two lines get closer,the collision probability becomes ignorable. The expectednumber of buzz transmissions until receiving three success-ful buzzes is H (t 0) when G (t 0) = 3. As shown in Fig. 6, witha = 0.2, H (t 0) is about 3.09 when G(t 0) = 3. Thus, 3.09 buzztransmissions are needed in order to receive three buzzessuccessfully. With a = 1.0, H (t 0) is about 3.22 whenG (t 0) = 3.

With a = 0.2, G (t 0) @ 1 and H(t 0) is about 1.017 whichmeans that the first buzz collision probability is1:017�1

1:017� 0:017. Likewise, with a = 1.0, H(t 0) such that

G(t 0) = 1 is about 1.04 which means that the first buzz col-lision probability is about 0.038.

6. Numerical results

In this section, we evaluate the proposed tracking frame-work which is implemented on QualNet simulator [4] andMatlab [21]. The surveillance area is 190 · 190 m2. Fourhundred sensors including a sink are deployed in this areawith three topologies: grid, uniform, and random distribu-tion. A static routing path forming a tree rooted at the sinkis used in the network layer and IEEE 802.11b is adoptedas the MAC layer protocol. The sensing range of a sensordevice is 25 m and the transmission range is 50 m (twice thesensing range). We adopt the random-waypoint model as amobility model for the target, which is moving aroundwithin the surveillance area. The maximum and minimumspeed of the target movement are 20 and 1 m/s, respective-ly. The default pause time is set to 20 s unless a specificcomment is given. Throughout the simulation experiments,Tcycle is one second. Each distance estimate from a corre-sponding RSS measurement contains a random noisecomponent which follows the White Gaussian noise modelof Nð0; rÞ, and noise magnitude (r) has a default valueof 1.6 m. The default sensor topology is a uniformdistribution. Other default parameter settings are givenby Table 2.

The performance metrics are as follows:

1. Localization error: the difference between the true targetlocation and the estimated location at the leader.

2. Tracking error: the difference between the true targetlocation and the most recently reported location at thesink.

3. The total number of reports sent by the leaders.4. The total number of transmitted messages: the sum of

the buzz messages, the report messages sent by the lead-ers, and the report-forwarding messages relayed byintermediate sensors between the leaders and the sink.

5. The number of iterations until RVI converges.

Four different combinations of localization and reportscheduling algorithms are compared:

• approximation + non-movement-adaptive (Appr),• Newton’s method + non-movement-adaptive (Newton),• RVI + non-movement-adaptive (RVI + Non), and• RVI + movement-adaptive (RVI + Adap).

Here, Newton’s method refers to the nonlinear optimiza-tion approach in Section 3.2.

The performance comparison of the four combina-tions are shown in Table 3, which comes from ten runsof the simulation experiments. Overall, with 1.6 m noisemagnitude, RVI shows about half of the localizationerror compared to the approximation localization algo-rithm and reduces the standard deviation. Moreover,the localization error and standard deviation of RVI

Table 3Performance comparison under uniform sensor distribution and 1.6 mnoise magnitude

Localizationerror (m)

Tracking error(m)

Totalreports

Totalmessages

Average SD Average SD

Appr 4.35 2.12 4.43 2.33 1000 10,097Newton 2.21 1.54 2.29 1.76 1000 10,228RVI + non_adap 2.41 1.61 2.48 1.84 1000 10,252RVI + adap 2.41 1.61 2.83 2.27 439 6,444

Other parameters follow Table III.

0

2000

4000

6000

8000

10000

12000

1

Pause time (s)

Tot

al m

essa

ges

Non-adaptiveAdaptive

2 3 4

Fig. 8. Effect of the pause time of the target on the number of the totalmessages.

0

1

2

3

4

0.5 1 1.5 2

Step Size (m)

Tra

ckin

g E

rror

(m

)

0

1

2

3

4

5

6

Avg

. RV

I Ite

ratio

ns

Random:Tracking ErrorUniform:Tracking ErrorGrid:Tracking ErrorRandom:RVI IterationsUniform:RVI IterationsGrid:RVI Iterations

Fig. 9. Effect of the RVI step size on the tracking error and the number ofRVI iterations.

J. Lee et al. / Computer Communications 29 (2006) 2494–2505 2503

are close to the performance of the sophisticated New-ton’s method. Also, the adaptive reporting frameworkreduces the total report messages from the leaders downto less than a half of that of the non-adaptive reportingframework. However, this reduction of the report mes-sages slightly increases the tracking error compared tothe localization error.

Fig. 7 plots the tracking error versus the noise magni-tude. Since the approximation localization algorithm hasa coarse-grained localization error, the effect of small noisemagnitude is not noticeable. However, as the noise magni-tude increases, all of the four combinations show increas-ing tracking errors. Also, the difference of the localizationerror between the approximation algorithm and the RVIalgorithm declines as the noise magnitude increases.

Fig. 8 compares the number of the total messages (buzzmessages, report messages, and report-forwarding messag-es) of non-movement-adaptive reporting and movement-adaptive reporting. Obviously, as the pause time (betweentwo moving periods) increases, the number of the totalmessages falls off while slightly decreasing tracking errors(that is not shown in the graph). Note that even whenthe pause time is zero, the total number of messages ofthe adaptive reporting framework is notably less than thatof the non-adaptive reporting framework.

Fig. 9 shows two kinds of outcomes. First, it plots thetracking error as the step size of the RVI algorithm increas-es in three topologies: random, uniform, grid. As also hinted

0

1

2

3

4

5

6

0.2 0.4 0.8 1.6 3.2

Noise Magnitude (m)

Tra

ckin

g E

rror

(m

)

ApprNewtonRVI+NonRVI+Adap

Fig. 7. Effect of the noise magnitude on the tracking error.

0

1

2

3

4

0

Time Threshold (s)

Tra

ckin

g E

rror

(m

)

0

3000

6000

9000

12000T

otal

Mes

sage

sDis. Th=5:Tracking ErrorDis. Th=3:Tracking ErrorDis. Th=1:Tracking ErrorDis. Th=1:Total MessagesDis. Th=3:Total MessagesDis. Th=5:Total Messages

1 3 5

Fig. 10. Effect of the distance and time threshold of the adaptive reportscheduling algorithm on the tracking error and the number of totalmessages.

2504 J. Lee et al. / Computer Communications 29 (2006) 2494–2505

in Table 1, this figure indicates that the more regularly thesensor nodes are deployed, the less becomes the localiza-tion error (and hence the tracking error). Second, as thestep size of RVI increases, the number of RVI iterationsdecreases with a small turnover at the step size 1.5.

Fig. 10 shows the tracking error and the number of thetotal messages with different distance thresholds (Dth) asthe time threshold (Tth) varies. The noise magnitude is0.2 m. The time threshold of zero second indicates thenon-movement-adaptive reporting. Note that there is atradeoff between the tracking error and the message over-head. As the time threshold and/or the distance thresholdincreases, the tracking error increases and the messageoverhead decreases. The message overhead is more sensi-tive in response to the change of the time threshold thanthe tracking error because the tracking error can not be lessthan the localization error that depends on the noise mag-nitude and the step size.

7. Conclusions

The proposed framework consists of three main compo-nents: RVI localization, 3-neighbor sensor discovery, andthe movement-adaptive reporting mechanism. RatiometricVector Iteration (RVI) is based on distance ratio estimatesrather than absolute distance estimates which are oftenimpossible to calculate. In the RVI algorithm, only threeclosest sensors (to the target) broadcasts their received sig-nal strengths, which are called buzzes. The proposed RSS-based backoff timer enables successful buzz transmissionsof the three closest sensors with a low collision probability.Also, a reporting algorithm that is adaptive to the target’smovement is introduced to further reduce the energy con-sumption. That is, the energy efficiency is achieved byreducing the number of messages, while maintaining theaccuracy required by applications. Extensive simulationresults show that the proposed RVI algorithm renders lessthan half of the localization error of the approximationalgorithm and this result is close to that of nonlinear opti-mization. Moreover, the proposed report scheduling algo-rithm reduces the total number of transmitted messagessubstantially compared to the baseline framework whichis not adaptive to the target movement.

Acknowledgements

This work was supported in part by the Brain Korea 21project of the Ministry of Education, 2005, Korea.

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Jeongkeun Lee. He received a B.S. degree in com-puter engineering from Seoul National Universityin 2001. He is an Ph.D. candidate in the Multimediaand Mobile Communications Lab., School ofComputer Science and Engineering, SeoulNational University. He was a visiting researcher atUniversity of California at Santa Cruz and Fra-unhofer FOKUS, Germany in 2002 and 2005,respectively. His research interests include wirelessmesh networks, RFID systems, and user localiza-tion and tracking in wireless networks.

Kideok Cho. He received a B.S. degree in com-puter science and engineering from SeoulNational University in 2004. He is a mastercourse student in the Multimedia and MobileCommunications Lab., School of Computer Sci-ence and Engineering, Seoul National University.His research interests include wireless sensor net-works, RFID systems, and mobility management.

un

Seungjae Lee. He received a B.S. degree in com-puter science and engineering from Seoul

National University in 2005. He is a mastercourse student in the Multimedia and MobileCommunications Lab., School of Computer Sci-ence and Engineering, Seoul National University.His research interests include internet identifiersystem and distributed hash table.

J. Lee et al. / Computer Comm

Taekyoung Kwon. He is an assistant professor in

Multimedia and Mobile Communications Lab.,School of Computer Science and Engineering,Seoul National University. He received his Ph.D.,M.S., and B.S. degrees in computer engineeringfrom Seoul National University in 2000, 1995, and1993, respectively. He was a visiting student at IBMT.J. Watson Research Center in 1998 and a visitingscholar at the University of North Texas in 1999.His recent research areas include radio resourcemanagement, wireless technology convergence,mobility management, and sensor network.

Yanghee Choi. He received B.S. in electronicsengineering from Seoul National University, M.S.

in electrical engineering from Korea advancedInstitute of Science, and Doctor of Engineering inComputer Science from Ecole Nationale Superi-eure des Telecommunications (ENST) in Paris, in1975, 1977 and 1984, respectively. Before joiningthe School of Computer Engineering, SeoulNational University in 1991, he has been withElectronics and Telecommunications ResearchInstitute (ETRI) during 1977–1991, where he

served as director of Data Communication Section, and Protocol Engi-

ications 29 (2006) 2494–2505 2505

neering Center. He was research student at Centre National d-Etude desTelecommunications (CNET), Issy-les-Moulineaux, during 1981–1984. Hewas also Visiting Scientist to IBM T.J. Watson Research Center for theyear 1988–1989. He is now leading the Multimedia CommunicationsLaboratory in Seoul National University. He was editor-inchief of KoreaInformation Science Society journals. He was chairman of the SpecialInterest Group on Information Networking. He has been associate deanof research affairs at Seoul National University. He was president of OpenSystems and Internet Association of Korea. His research interest lies in thefield of multimedia systems and high-speed networking.