Tracking Targets in Quantized Areas with WirelessSensor Networks

Efren Lopes SouzaDepartment of Computer ScienceFederal University of Amazonas

Manaus, Brazilefren@dcc.ufam.edu.br

Andre CamposDepartment of Computer ScienceFederal University of Amazonas

Manaus, Brazilandre.campos@acm.org

Eduardo Freire Nakamura†Analysis, Research and Technological

Innovation Center (FUCAPI),Manaus, AM, Brazil

eduardo.nakamura@fucapi.br

Abstract—Target tracking is an important application of sensornetworks, particularly interesting in applications for Ecology formonitoring and tracking animals. In this context, understandingthe movement pattern and the territorial occupation of animalsare fundamental for understanding their habits. In practice,target tracking often operates on quantized areas (divided intocells). In this work, we propose and evaluate a quantized targettracking approach in such a way that the network is organized ina grid, where each cell is a region occupied by the target (animal).The cell size is determined according to the desired granularity.The computation of the target’s position obeys a voting scheme,so the technique is simple and low cost. To estimate the target’sposition, we use the Kalman or Particle filters. Results show thatposition computation errors are close to two cells, depending onthe scenario.

Index Terms—target tracking, kalman filter, particle filter

I. INTRODUCTION

A Wireless Sensor Network (WSN) [1] is a special typeof ad-hoc network composed of resource-constrained devices,called sensor nodes. These sensors are able to perceive theenvironment, collect data, and locally process and disseminatesuch data.Target tracking is an important application of these net-

works, being composed of three parts: (i) target detection(such as animals, people and vehicles), (ii) current positioncomputation, and (iii) future position estimation [2]. Usually,such application dependends on the position knowledge of thenodes detecting the target. In this case, nodes need to eitherknow their locations a priori or compute that information byusing a localization algorithm [3].In this paper, we propose and evaluate a target tracking

approach based on a voting scheme to determine the areawhere the target is. In this context, the network is organizedinto a grid, whose cells are the possible target positions. In ourapproach, the target position computation is made by simplevoting, in which all nodes have the same weight, or weightedvoting, in which nodes that are closer to the event have greaterweight. These positions are measures passed to the Kalmanfilter (KF) or Particles filter (PF) [2]. Then, these Bayesianfilters predict the next target position.This approach was proposed for the tracking of the bare-

faced tamarin (saguinus bicolor), which is a small ape endemicto the Amazon Forest that is threatened with extinction. This is

a typical application of WSNs for Ecology, where the sensorsare usually arranged in grids due to existing access roadsin small ecological reserves. In this case, it is important todetermine the movement pattern of these animals, correlating,for example with their eating habits and social behavior. In thisclass of practical application, the exact target position is notnecessary, so knowing a limited region (grid cell) occupiedby the target is enough. Considering an area as the targetposition is relevant to the application, since we can reducehardware/software requirements and optimize energy costs.The remainder of the paper is organized as follows. Sec-

tion II presents related work and background knowledgerequired for target tracking. In Section III, we present ourproposal for tracking in sensor networks. In Section IV,we present our experimental methodology and quantitativeevaluation. Finally, in Section V we discuss our conclusionsand outlook.

II. BACKGROUND AND RELATED WORKS

The target tracking problem involves not only the determi-nation of the route traveled by the target, but also in predictingthe next target position (discrete time) [2], increasing theproblem complexity.In this work, we use two Bayesian filters for target tracking

– Kalman and Particles – considered canonical solutions tothe problem [2], [3]. The Kalman filter (KF) is a very popularfusion method used to fuse low-level redundant data [2]. Ifa linear model can describe the system and the error canbe modeled as Gaussian noise, the Kalman filter recursivelyretrieves statistically optimal estimates. Particle filters (PF) arerecursive implementations of sequential Monte Carlo meth-ods (SMC) [4]. Although the KF is a classical solution, PFrepresent an alternative for applications with non-Gaussiannoise.Li et al. [5] propose a source localization algorithm for

a system equipped with asynchronous sensors, and evaluatethe performance of Kalman filter variations (EKF and UKF[2]) for non-linear systems for source tracking. Vercauterenet al. [6] propose a collaborative Particle filter for jointlytracking several targets and classifying them according totheir motion pattern. These traditional approaches depend onlocation information that are subject to severe errors.

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Few studies consider the errors introduced by the local-ization algorithms. Oliveira et al. [7] show the localizationerrors influence on density control and routing. Souza et al. [3]shows how the localization errors affects the target trackingand conclude that Bayesian filters do not filter these errorssatisfactorily.The mobility of a target can be obtained by recording

the objects movements (traces) in real scenarios or by usingthe mobility models. These models describe the position,speed, direction and other dynamic states of objects in motion,describing a pattern trajectory [8].The Correlated Random Walk (CRW) is the mobility model

used in this work, since it is well-known for approximatelyrepresenting animal moving patterns [9]. This model modelhas two parameters: the step size and the correlation de-gree (0 ≤ ω ≤ 1). The closer one is the value of ω ismore deterministic model, becoming more random when ω

approaches zero. The event detection model used in this paperwas proposed by Nakamura et al. [10].

III. PROPOSED APPROACH

In the proposed target tracking model, the network topologyis arranged in a grid with I rows and J columns. The inter-sections of these rows and columns represent N sensors nodesspaced at a distance d. Each node ni,j , with 0 ≤ i < I − 1and 0 ≤ j < J − 1, know the row and column to which itbelongs to, this enables data fusion to determine the currentposition is acquired only with basic mathematical operationswith integers values (most of sensor architectures do not havea float point unit).Unlike traditional approaches, which use absolute coordi-

nates to identify the target position, our approach considers thetarget position as a cell cx,y, with 0 ≤ x < I and 0 ≤ y < J .Thus, there are I×J possible cells. Figure 1 illustrates, amongother things, the nodes’ placement, the cells, and nodes’ rangedetecting the target.The target announces its presence at defined time intervals

(e.g. through a beaconing collar in the monkey’s neck). Thisevent is detected by the grid nodes that forward data to thesink. Then, the sink node fuses all data to estimate the currenttarget cell and predict to which cell it is moving to. Theannounce time interval (ti) shall be adjusted based on targetspeed (v) and cells side length (d) as ti = d

v. To avoid which

the delay caused during the multihop forwarding to the sinkinterfere in the vote, the packets sent by target to announceits presence have identifiers for the target and for the event.Thus, different targets can be treated separately, soon increasethe number of targets does not affect the tracking accuracy. Inaddition, packages of previous events to the current event arediscarded.Ideally, the nodes range must be at least d (cell dimension is

d×d), to enable communication between all nodes. However,in real scenarios that range should be higher to compensate forthe environment interference or any inaccuracies in the griddistribution. The distance between the nodes determines thearea of the cells. Then, depending on the target size, we can

to adjust the distance d and the nodes range to obtain cells ofappropriate size.A voting process is conducted to determine the current target

cell (at instant t). The prediction of the next target positionestimation (instant t + 1) is performed with Bayesian filters(Kalman and Particle).

A. Computing Current Target Position

Computing the current target position consists in definingwhere the target cell is at the present time t. To accomplishthat, we use the coordinates (i, j) contained in nodes, performa vote among the nodes detecting the target at time t. Thisvoting process may be simple or weighted.1) Simple Voting: In the simple voting, each node assigns

a vote for their candidate cells. The candidate cells of a nodeni,j are the cells around them. The target position is definedas the most voted cell.As proof-of-concept, the position estimation and tracking

processes are centralized in the sink node. The sensor nodesthat detect the target at time t forward to the sink a messageindicating that they detected the target. This message containsthe position (i, j) of the node that originated the message.Based on this information, the sink determines the candidatecells and chooses the most voted. The Figure 1 exemplifies thesimple voting process when four nodes detect the event. In thiscase, there are nine candidate cells, the cell c1,1 receives a voteof all the nodes, therefore, chosen as the target position.

n = {c ; c ; c ; c }

n = {c ; c ; c ; c }

n = {c ; c ; c ; c }

n = {c ; c ; c ; c }

0,0 0,0 0,1 1,0 1,1

0,1 0,1 0,2 1,1 1,2

1,0 1,0 1,1 2,0 2,1

1,1 1,1 1,2 2,1 2,2

0,0 0,1 0,2

2,0 2,1 2,2

1,0 1,21,1

0,0 0,1 0,2

2,0 2,1 2,2

1,0 1,21,1

d

Fig. 1. Simple Voting.

Ties may occur in the voting process of an event. A possiblecase of a tie occurs when few nodes (one or two) detect theevent. Ties also often occur when the target detection radius(to announce its position) is greater than the distance betweensensor nodes. In the tie cases, the cell chosen is the mostcentral among the candidates, and it is computed as c(x, y) =

c(∑

mk=1

xk

m,∑

mk=1

yk

m

), in which m is the number of candidate

cells and (xk, yk) is the coordinate of the k-th candidate cell.2) Weighted Voting: The weighted voting uses distance

estimates (obtained from signal strength) to classify the sensornodes with higher vote weight. So, nodes supposedly closerto the event, have a greater weight. The vote weight of a nodeni,j is given by vi,j = e−di,j , in which di,j is the distanceestimated between node ni,j and the target.

B. Target Position Estimation

Estimate the target position is determine the position (cell)where the target is at time t+1, based only on the position at

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time t. The Figure 2 shows an example in which the trackingalgorithm at time t estimated the cell c1,5 as the target positionat time t+ 1.

0,0 0,1 0,2

2,0 2,1

1,0

0,3 0,4 0,5

1,1 1,5

2,4 2,5

3,0 3,1 3,5

4,0 4,1 4,2 4,3 4,4 4,5

3,3

1,2

2,3

3,4

1,3

2,2

3,2

1,4

Fig. 2. Target position estimation.

In this work, we used Kalman and Particle filters as esti-mators. In both cases, the input data are the cell estimationcomputed by simple or weighted voting schemes (position att), and the output is the prediction (position at t + 1). TheseBayesian filters are commonly used to work with continuousdata, but in this case study, in which the coordinates arerepresented by integer and discrete values, the filters onlyperform operations with integer values (lower cost).

IV. EVALUATION

A. Methodology

The evaluation is based on simulations with Sinalgo [11].As proof-of-concept the position estimation and tracking areperformed by the sink. The default network consists of2401 sensor nodes distributed as a grid in a sensor fieldof 500×500m2. The sink node and the target are randomlypositioned. The nodes are 10m away from each other, resultingin 50×50 cells with 10×10m2.The communication range is 10m for all sensor nodes. The

data routing is done with a routing tree where the sink is theroot. Already the target range is 30m, so more than three nodesdetect the event.The target mobility model is the CRW with a correlation

degree of 0.99 and speed of 1m/s. We schedule 300 events(target announcements), one announcement every 10s.The sink computes the target position by simple (Sec-

tion III-A1) or weighted (see Section III-A2) voting. The targettracking is performed with Kalman or Particle filters.The Table I shows the energy parameters used in the

simulation. We consider that each message has a size of the64bytes.Considering that cx1,y1

is the cell where the target is,and that cx2,y2

is the estimated cell, the error is given byε (cx1,y1

, cx2,y2) = max (|x1 − x2|, |y1 − y2|).

Each point averages 50 random topologies, and error barsrepresent the confidence interval for 99% of confidence.

Parameter Value

Initial energy 50 joules

Transmit 342×10−7 joules/byte

Listening 1888×10−8 joules/s

TABLE IENERGY PARAMETERS.

B. Simulation Results

1) Target Range: The target range (detection radius) de-termines the number of nodes that can detect the event. Iffewer than three nodes detect the target, we cannot accuratelydetermine the cell in which the target is, but you can guess anearby cell.The number of nodes partaking of the voting scheme

influences on the tracking accuracy. To evaluate that influence,we vary the target range from 10m to 50m. The model usedfor event detection is fixed radius, so is possible performa tracking satisfactorily with this variation. The results areshown in Figure 3.Figure 3(a) depicts for a target range of 10m, simple and

weighted voting schemes are equivalent. The reason is thatweights are irrelevant when the number of voting nodes issmall (≤ 4). When the target range increases, weighted votingis a better choice, missing the correct position only whenthe target reaches the edges of the sensor field. On the otherhand, simple voting eventually make mistakes in tie situations.Figure 3(b) shows the Kalman filter outperforms Particle filtersno matter the voting scheme, because the scenario conditionsare suitable for Kalman filter (linear system and Gaussianerrors). When the target range increases more nodes detect thetarget, so the associated costs increase accordingly Figure 3(c).2) Distance Estimation Inaccuracy: The distance estimated

by sensor nodes is not perfect. Depending on the monitoredenvironment, the associated errors are greater, which affectsthe tracking performance. In general, these errors depend onthe distance and can be modeled by a zero-mean Gaussianvariable, in which the standard deviation is a percentage ofthe actual distance [7].In our approach, the weighted voting depends on the

distance estimates. Thus, to evaluate different situations, wevaried the standard deviation of 0% to 50% of the distance.Figure 4(a) shows that weighted voting is not perfect even

when distance estimates are perfect. These errors occur whenthe target is in a border of the sensor field, since the target isnot surrounded by the nodes of the grid. The distance estima-tion inaccuracy directly affects the performance of weightedvoting, since the assigned weights are incorrect. The estimatesare also affected by measurement errors, but on a smallerscale (Figure 4(b)). The difference in measurement errorbetween the inaccuracies of 0% and 50% is approximately onecell, but the same difference in the estimates is approximately0.15 cells. The reason is that the filters reduce the influenceof measurement errors.

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0.00

0.20

0.40

0.60

0.80

1.00

10 20 30 40 50

Mea

sure

men

t Err

or (

cells

)

Target Range (m)

SimpleWeighted

(a) Measurements.

1.60

1.80

2.00

2.20

2.40

2.60

2.80

10 20 30 40 50

Est

imat

ion

Err

or (

cells

)

Target Range (m)

Simple−KFSimple−PF

Weighted−KFWeighted−PF

(b) Estimates.

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

10 20 30 40 5049.20

49.30

49.40

49.50

49.60

49.70

49.80

49.90

Mes

sage

s S

ent (

100,

000

un)

Res

idua

l Ene

rgy

(joul

es)

Target Range (m)

MessagesEnergy

(c) Messages and energy.

Fig. 3. Impact of target range.

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0.0 0.1 0.2 0.3 0.4 0.5

Mea

sure

men

t Err

or (

cells

)

Inaccuracy (% of the distance)

Weighted

(a) Measurements.

1.60

1.80

2.00

2.20

2.40

2.60

2.80

0.0 0.1 0.2 0.3 0.4 0.5

Est

imat

ion

Err

or (

cells

)

Inaccuracy (% of the distance)

Weighted−KFWeighted−PF

(b) Estimates.

Fig. 4. Impact of distance estimation inaccuracy.

V. CONCLUSIONS

This paper proposes and evaluates a technique for targettracking in sensor networks when we have a quantized area(grid structure). Target positions are computed by using twovoting schemes: (a) simple voting – all nodes have the sameweight; and (b) weighted voting – nodes closer to the targethave greater weight. The tracking is performed with KF or PF,fed with the cells coordinates obtained by the vote.As a general conclusion, the simple vote is suitable when the

target range (event radius) and distance between nodes in thegrid are the same. However, when we consider communicationfailures, weighted voting is preferable, even in situations inwhich the distance estimation inaccuracy is high. Furthermore,the Kalman filter outperforms the Particle filter in the evaluatedscenarios.

ACKNOWLEDGEMENTS

This work is supported by the Brazilian National Councilfor Scientific and Technological Development (CNPq), underthe grant numbers 575808/2008-0 (Revelar), 55.4087/2006-5(Sauim), and 47.4194/2007-8 (RastroAM).

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