Localization systems for wireless sensor networks

IEEE Wireless Communications • December 20076 1536-1284/07/$20.00 © 2007 IEEE

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WIR E L E S S SENSOR NE T W O R K I N G

INTRODUCTIONThe definition of a localization system amongsensor nodes is a fundamental issue for manyapplications of wireless sensor networks (WSNs).Because sensor networks may be deployed ininaccessible terrains or disaster relief operations[1], the position of sensor nodes may not be pre-determined. Thus, a localization system isrequired in order to provide position informa-tion to the nodes.

The importance of localization informationarises from several factors, many of which arerelated only to WSNs. These factors include theidentification and correlation of gathered data,node addressing, management and query ofnodes localized in a determined region, evalua-tion of nodes’ density and coverage, energy mapgeneration, geographic routing, object tracking,and other geographic algorithms. All of thesefactors make localization systems a key technolo-

gy for the development and operation of WSNs.In this article we address the localization

problem from the viewpoint of a WSN. In thenext two sections we briefly present an overviewand definition of localization systems for WSNsand their components. We show the main meth-ods used by localization systems to estimate dis-tances and angles. We show the techniques thatcan be used by a node to compute its position,and how all the estimated information of dis-tances and positions can be manipulated inorder to allow most or all of the nodes of a WSNto estimate their positions. Finally, we presentour conclusions.

PROBLEM STATEMENTA WSN can be composed of n nodes with a com-munication range of r, distributed in a two-dimensional squared sensor field Q = [0,s] ×[0,s]. For the sake of simplification, we considersymmetric communication link; that is, for anytwo nodes u and v, u reaches v if and only if vreaches u and with the same signal strength w.Thus, we represent the network by the Euclideangraph G = (V, E) with the following properties:• V = {v1, v2, …, vn} is the set of sensor nodes.• ⟨i, j⟩ ∈ Ε if vi reaches vj; that is, the distance

between vi and vj is less than r.• w(e) ≤ r is the weight of edge e = ⟨i, j⟩, the dis-

tance between vi and vj.Some terms can be used to designate the

state of a node:Definition 1 (Unknown Nodes — U): Also

known as free or dumb nodes, this term refers tothe nodes of the network that do not know theirlocalization information. To allow these nodes toestimate their positions is the main goal of alocalization system.

Definition 2 (Settled Nodes — S): These nodeswere initially unknown nodes that managed toestimate their positions by using the localizationsystem. The number of settled nodes and theestimated position error of these nodes are themain parameters for determining the quality of alocalization system.

AZZEDINE BOUKERCHE, UNIVERSITY OF OTTAWA

HORACIO A. B. F. OLIVEIRA, UNIVERSITY OF OTTAWA, FEDERAL UNIVERSITY OF AMAZONAS

EDUARDO F. NAKAMURA, FEDERAL UNIVERSITY OF MINAS GERAIS AND FUCAPIANTONIO A. F. LOUREIRO, FEDERAL UNIVERSITY OF MINAS GERAIS

ABSTRACT

Monitoring applications define an importantclass of applications used in wireless sensor net-works. In these applications the network per-ceives the environment and searches for eventoccurrences (phenomena) by sensing differentphysical properties, such as temperature, humidi-ty, pressure, ambient light, movement, and pres-ence (for target tracking). In such cases thelocation information of both phenomena andnodes is usually required for tracking and corre-lation purposes. In this work we summarize mostof the concepts related to localization systems forWSNs as well as how to localize the nodes inthese networks (which allows the localization ofphenomena). By dividing the localization systemsinto three distinct components — distance/angleestimation, position computation, and localiza-tion algorithm — besides providing a didacticviewpoint, we show that these components canbe seen as subareas of the localization problemthat need to be analyzed and studied separately.

LOCALIZATION SYSTEMS FORWIRELESS SENSOR NETWORKS

The authors summarize most ofthe concepts relatedto localization systems for WSNs as well as how tolocalize the nodes inthese networks(which allows thelocalization of phenomena).

BOUKERCHE LAYOUT 12/4/07 1:55 PM Page 6

IEEE Wireless Communications • December 2007 7

Definition 3 (Beacon Nodes — B): Also knownas landmarks or anchors, these are the nodesthat do not need a localization system in orderto estimate their physical positions. Their local-ization is obtained by manual placement orexternal means such as GPS. These nodes formthe base of most localization systems for WSNs.

The localization problem can then be definedas follows.

Definition 4 (Localization Problem): Given amultihop network G = (V, E), and a set of bea-con nodes B and their positions (xb, yb), for all b∈ B, we want to find the position (xu, yu) of asmany u ∈ U as possible, transforming theseunknown nodes into settled nodes, S.

THE COMPONENTS OF LOCALIZATION SYSTEMSLocalization systems can be divided into threedistinct components:• Distance/angle estimation: This component is

responsible for estimating information aboutthe distances and/or angles between twonodes. This information will be used by theother components of the localization system.

• Position computation: This component isresponsible for computing a node’s positionbased on available information concerning dis-tances/angles and positions of reference nodes.

• Localization algorithm: This is the main com-ponent of a localization system. It determineshow the available information will be manipu-lated in order to allow most or all of the nodesof a WSN to estimate their positions.Figure 1 depicts this component division.

Besides providing a didactic viewpoint, theimportance of such a division into componentscomes, as we will see, from the need to recog-nize that the final performance of a localizationsystem depends directly on each of these compo-nents. Also, each component has its own goaland methods of solution. They can thus be seenas subareas of the localization problem thatneed to be analyzed and studied separately.

DISTANCE/ANGLE ESTIMATION

Distance/angle estimation consists in identifyingthe distance or angle between two nodes. Suchestimates constitute an important component oflocalization systems, because they are used byboth the position computation and localizationalgorithm components.

Different methods can be used to estimatesuch information. Some of these methods are

very accurate, but with higher costs (in terms ofhardware, energy, and processor resources),while others are less accurate but already avail-able on most sensor nodes.

In the following sections some of the mainmethods used by localization systems to estimatedistances/angles will be studied. These methodsinclude received signal strength indication(RSSI), time of arrival/time difference of arrival(ToA/TDoA), angle of arrival (AoA), and com-munication range.

RECEIVED SIGNAL STRENGTH INDICATORRSSI can be used to estimate the distancebetween two nodes based on the strength of thesignal received by another node. As depicted inFig. 2a, a sender node sends a signal with adetermined strength that fades as the signalpropagates. The bigger the distance to thereceiver node, the lesser the signal strengthwhen it arrives at that node.

Theoretically, the signal strength is inverselyproportional to squared distance, and a knownradio propagation model can be used to convertthe signal strength into distance. However, inreal-world environments, this indicator is highlyinfluenced by noises, obstacles, and the type ofantenna, which makes it hard to model mathe-matically. In these cases it is common to make asystem calibration [2], where values of RSSI anddistances are evaluated ahead of time in a con-trolled environment.

This method, like the others, has both advan-tages and disadvantages. The main advantage isits low cost, because most receivers are capable

n Figure 1. Division of localization systems into three distinct components.

Localization

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n Figure 2. a) Decrease in signal strength; b), c) methods to derive the distance from the signal's arrival time; d) angle of arrival of thesignal.

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IEEE Wireless Communications • December 20078

of estimating the received signal strength. Thedisadvantage of this method is that it is very sus-ceptible to noise and interference, which resultsin higher inaccuracies of distance estimations.Some experiments [3] show errors from 2 to 3 min scenarios where all nodes are placed in aplane field 1.5 m from the ground with a com-munication range of 10 m.

Although RSSI shows plausible results in sim-ulations and controlled experiments, its usage inreal-world applications is still questionable [4].But, considering its low cost, it is possible that amore sophisticated and precise use of RSSI (e.g.,with better transmitters) could become the mostused technology of distance estimation from thecost/precision viewpoint [5]. However, this tech-nology is not yet available.

TIME [DIFFERENCE] OF ARRIVALDifferent methods try to estimate distancesbetween two nodes using time based measures.The most simple and intuitive is ToA [6]. In thiscase the distance between two nodes is directlyproportional to the time the signal takes topropagate from one point to another. This way,if a signal was sent at time t1 and reached thereceiver node at time t2, the distance betweensender and receiver is d = sr(t2 – t1), where sr isthe propagation speed of the radio signal (speedof light), and t1 and t2 are the times when thesignal was sent and received (Fig. 2b). This typeof estimation requires precisely synchronizednodes and the time at which the signal leaves thenode must be in the packet that is sent.

TDoA is based on:• The difference in the times at which a single

signal from a single node arrives at three ormore nodes

• The difference in the times at which multiplesignals from a single node arrive at anothernode

The first case, which is more common in cellularnetworks, requires precisely synchronized receiv-er nodes (in this case base stations). In the sec-ond case, more common and suitable for WSNs,the nodes must be equipped with extra hardwarecapable of sending two types of signals simulta-neously. These signals must have different prop-agation speeds, like radio/ultrasound [7] orradio/acoustic [2]. Usually, the first signal is thepacket itself, which propagates at the speed oflight (≈ 300.000 km/s), while the second signal issome kind of sound, because of its slower propa-gation (≈ 340 m/s), which is six orders of magni-tude slower then the first signal.

An example of TDoA suitable for WSNs isused by [3] and depicted in Fig. 2c, where anultrasound pulse is sent simultaneously with aradio signal. In this case nodes compute the dif-ference in the arrival times of the two signals.The distance can now be computed by the for-mula d = (sr – ss)*(t2 – t1), where sr and ss arethe propagation speed of the radio and ultra-sound signals, and t1 and t2 are the arrival timesof the radio and ultrasound signals, respectively.Another different and interesting way of com-puting distance among nodes using the TDoA isproposed by Fu et al. [8], and is based on thedirect sequence spread spectrum (DSSS) modu-lation technique.

The errors in the distance estimationsobtained by TDoA are approximately centime-ters. Experiments with ultrasound performed in[3] indicate errors of about 2 or 3 cm (smallerthan the sensor node) in scenarios where nodeswere separated by distances of 3 m. In [9] acous-tic sound was tested, and results showed errorsof about 23 cm, with nodes at distances of 2 m.

Despite the lower errors, these systems havedisadvantages. The first is the need for extrahardware to send the second signal, whichincreases node cost. The second disadvantage isthe generally limited range of the second signal,which is normally between 3 and 10 m with morepowerful transmitters.

ANGLE/DIRECTION OF ARRIVALThe AoA of the signal [7, 10] can also be usedby localization systems. This angle can be inrelation to the node itself, to an electronic com-pass, or to a second signal received by the node.

The estimation of the AoA is done by usingdirective antennas or an array of receivers — usu-ally three or more — that are uniformly separated.In the last case, based on the arrival times of thesignal at each of the receivers, it becomes possibleto estimate the AoA of this signal (Fig. 2d).

Experiments show that this method has aninaccuracy of some degrees (about 5° in [7]). Theneed for extra hardware and a minimum distancebetween the receivers results in some disadvan-tages in terms of the cost and size of nodes.

COMMENTS ABOUT THEDISTANCE/ANGLE ESTIMATION

The choice of which method to use to estimatethe distance between nodes in a localization sys-tem is an important factor that influences thefinal performance of the system. Usually, asshown in the next section, to estimate a position,a node uses at least three distance estimations,each with an associated error. On the otherhand, if only the accuracy of these methods wasimportant, we could just use TDoA since it hasfewer errors. However, factors including the sizeand cost (in terms of hardware, processor, andenergy) of the nodes must also be taken intoconsideration. Thus, the method chosen for esti-mating distances depends on the applicationrequirements as well as available resources.

POSITION COMPUTATION

When a node has enough information about dis-tances and/or angles and positions, it can com-pute its own position using one of the methodsstudied in this section.

Several methods can be used to compute theposition of a node. Such methods include trilat-eration, multilateration, triangulation, probabilis-tic approaches, bounding box, and the centralposition. The choice of which method to use alsoimpacts the final performance of the localizationsystem. Such a choice depends on the informa-tion available and the processor’s limitations.

TRILATERATION AND MULTILATERATIONTrilateration is the most basic and intuitivemethod. This method computes a node’s posi-

Different methods tryto estimate distancesbetween two nodesusing time basedmeasures. The mostsimple and intuitiveis ToA. In this case,the distance betweentwo nodes is directlyproportional to thetime that the signaltakes to propagatefrom one point toanother.



tion via the intersection of three circles, asdepicted in Fig. 3a. To estimate its positionusing trilateration, a node needs to know thepositions of three reference nodes and its dis-tance from each of these nodes. Distances canbe estimated using one of the methods explainedin the previous section.

The circles formed by the position and dis-tance to each of the references can be represent-ed by the formula ( x – xi)2 + ( y – yi)2 = di

2,where(x , y ) is the position we want to compute,(xi, yi) is the position of the ith reference node,and di is the distance of the ith reference nodeto the unknown node. In this case we have threeequations with two unknowns, which can besolved, theoretically, in one solution.

In real-world applications the distance esti-mation inaccuracies as well as the inaccurateposition information of reference nodes make itdifficult to compute a position. As depicted inFig. 3b, the circles do not intersect at only onepoint, resulting in an infinite set of possible solu-tions.

Furthermore, when a larger number of refer-ence points are available, we can use multilatera-tion to compute the node’s position. In this casean overdetermined system of equations must besolved. Figure 3 depicts this case. Usually,overdetermined systems do not have a uniquesolution. When considering n reference pointsand also the error of the distance estimations,which makes di = d i – ε, the system of equationsbecomes (x – xi)2 + (y – yi)2 = di

2 – e, where ε isnormally considered to be an independent nor-mal random variable with zero mean. This sys-tem can be linearized, by subtracting the lastequation, into Ax ≈ b. This linear system can besolved easily using standard methods like theleast squares approach.

The number of floating point operationsneeded to compute a position depends on themethod used to solve the system of equations. Inthe case of the least square method, (m + n/3)n2

floating point operations (where m is the num-ber of unknowns and n is the number of equa-tions) are required to estimate a position.

BOUNDING BOXThe bounding box method proposed in [12] usessquares — instead of circles as in trilateration —to bound the possible positions of a node. Anexample of this method is depicted in Fig. 3d.

For each reference node i, a bounding box isdefined as a square with its center at the posi-tion of this node (xi, yi), with sides of size 2di(where d is the estimated distance) and withcoordinates (xi – di, yi – di) and (xi + di, yi +di). The intersection of all bounding boxes canbe easily computed without any need for float-ing point operations by taking the maximum ofthe low coordinates and the minimum of thehigh coordinates of all bounding boxes. This isthe shaded rectangle in Fig. 3d. The final posi-tion of the unknown node is then computed asthe center of the intersection of all boundingboxes.

Despite the final error of this method, whichis greater than trilateration, computing the inter-section of squares uses fewer processor resourcesthan computing the intersection of circles.

TRIANGULATIONIn triangulation [7, 10] information about anglesis used instead of distances. Position computa-tion can be done remotely (Fig. 3e) or by thenode itself; the latter is more common in WSNs.In this last case, depicted in Fig. 3f, at leastthree reference nodes are required. Theunknown node estimates its angle to each of thethree reference nodes and, based on these anglesand the positions of the reference nodes (whichform a triangle), computes its own position usingsimple trigonometrical relationships. This tech-nique is similar to trilateration. In fact, based onthe AoAs, it is possible to derive the distances toreference nodes [10].

n Figure 3. a) Theoretical model of trilateration; b) a more realistic model of trilateration; c) multilateration; d) bounding box;e), f) triangulation; g), h) probabilistic approach (from [11]).

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PROBABILISTIC APPROACHES

The uncertainty in distance estimations has moti-vated the appearance of probabilistic approachesfor computing a node’s position. An example of aprobabilistic approach is proposed in [11], wherethe errors in distance estimations are modeled asnormal random variables. When an unknownnode receives a packet from a reference node, itcan be in any place around the reference nodewith equal probabilities. When another packet isreceived from another reference node, theunknown node computes its position again asdepicted in Fig. 3g. When new position informa-tion is received from other nodes, it becomespossible to identify the probable location of theunknown node, as depicted in Fig. 3h.

The main drawbacks of this approach are thehigh computational cost and the space requiredto store the information.

COMMENTS ABOUT POSITION COMPUTATIONA number of other methods exist that aim to com-pute the position of a node. Location fingerprint-ing is a method in which the signal characteristicsobtained from a set of locations are catalogued,and the position computation of a node consists ofcomparing its current signal characteristics withthose catalogued previously. This technique isused by Bahl et al. [13] and other indoor localiza-tion systems, but the need to generate a signal sig-nature database makes this technique unfeasiblefor most scenarios of WSNs. He et al., in theAPIT algorithm [4], use triangles formed by threebeacon nodes, and a node decides if it is inside oroutside these triangles by comparing its signalstrength measurements with the measurements ofits neighbors. The position of the node is comput-ed by finding the centroid of the intersection ofthe beacon triangles the node is within.

Other work concentrates all informationabout distances between the nodes into a centralnode and uses mathematical optimization tech-niques to compute the positions of the nodes. Asan example, we have the work of Doherty et al.[14] who formulate the localization problem as aconvex optimization problem based only on con-nectivity-induced constraints and use a semidefi-nite program (SDP) to solve the problem. Thereis also the work of Shang et al. [15], who usedmultidimensional scale (MDS).

LOCALIZATION ALGORITHM

The localization algorithm is the main compo-nent of a localization system. This componentdetermines how the information concerning dis-tances and positions is manipulated in order toallow most or all of the nodes of a WSN to esti-mate their positions.

Localization algorithms can be classified intoa few categories: distributed [6, 16, 17] or cen-tralized position computation [14]; with [16, 17]or without an infrastructure [6, 7, 18]; relative[19, 20] or absolute positioning [6, 16]; designedfor indoor [7, 18] or outdoor scenarios [6]; andone hop [6, 18] or multihop [16, 17].

Here some proposed localization algorithmsare discussed to show how this component dif-ferentiates from the other components. These

algorithms are the Ad Hoc Positioning System(APS), Recursive Position Estimation (RPE),and Localization with a Mobile Beacon (LMB).

AD HOC POSITIONING SYSTEMIn APS [17] a reduced number of beacon nodes(e.g., three or more) is deployed with theunknown nodes. Then each node estimates its dis-tance to the beacon nodes in a multihop way.Once these distances are estimated, the nodes cancompute their positions using trilateration. Threemethods of hop-by-hop distance propagation areproposed: Dv-Hop, Dv-Distance, and Euclidean.

In Dv-Hop APS the beacon nodes start thepropagation of their position information (Fig.4a). Working as an extension of the distance vec-tor algorithm, all nodes receive the position infor-mation of all beacon nodes as well as the numberof hops to these beacons. When a beacon nodereceives the position information of the otherbeacon nodes, it has enough information to com-pute the average size of one hop based on its ownposition, the position of the other beacon nodes,and the number of hops between them (Fig. 4b).This last value is then flooded in a controlled wayinto the network as a correction factor. When anunknown node receives a correction, it is able toconvert its distance to the beacon nodes fromnumber of hops to meters (Fig. 4c). The complex-ity of message exchange in this algorithm is drivenby the total number of beacon and normal nodes,which is O(n*(m + 1)), where n is the number ofnodes and m is the number of beacon nodes.

An advantage of the APS is that its localiza-tion algorithm requires a low number of beaconnodes in order to work. However, the way dis-tances are propagated, especially in Dv-Hop andDv-Distance, as well as the way these distancesare converted from hops to meters in Dv-Hop,result in erroneous position computation, whichincreases the final localization error of the system.

RECURSIVE POSITION ESTIMATIONIn RPE [16] nodes estimate their positions basedon a set of initial beacon nodes (e.g., 5 percentof the nodes) using only local information. Local-ization information increases iteratively as newlysettled nodes become reference nodes.

The RPE algorithm can be divided into fourphases, as depicted in Fig. 5. In the first phase anode determines its reference nodes. In the sec-ond phase the node estimates its distance tothese reference nodes using, for example, RSSI.In the third phase the node computes its posi-tion using trilateration (becoming a settlednode). In the final phase the node becomes areference node by broadcasting its newly esti-mated position to its neighbors. When a nodebecomes a reference, it can assist other nodes incomputing their positions as well.

An advantage of this algorithm is that thenumber of reference nodes increases quickly, insuch a way that the majority of the nodes cancompute their position. But this technique has thedisadvantage of propagating localization errors.This means that the inaccurate position estimationof one node can be used by other nodes to esti-mate their positions, increasing this inaccuracy.Furthermore, a node must have at least three ref-erence neighbors in order to compute its position.

In APS, a reducednumber of beaconnodes (e.g., three ormore) is deployedwith the unknownnodes. Then, eachnode estimates itsdistance to the beacon nodes in amultihop way. Oncethese distances areestimated, the nodescan compute theirpositions using trilateration.



LOCALIZATION WITH A MOBILE BEACONSome recent work [21] has proposed the use ofmobile beacons to assist the nodes of a WSN inestimating their positions. A mobile beacon is anode that is aware of its position (e.g., equippedwith a GPS receiver) and has the ability to movearound the sensor field. This beacon can be ahuman operator, an unmanned vehicle, an air-craft, or a robot.

The system operation in [21] is quite simple.Once the nodes are deployed, the mobile beacontravels through the sensor field broadcastingmessages that contain its current coordinates.When a free node receives more than three mes-sages from the mobile beacon it computes itsposition, using a probabilistic approach, basedon the received coordinates and RSSI distanceestimations. Figure 6 illustrates this scenario andthree possible trajectories for the mobile beacon.The communication cost for the WSN is null,since none of the nodes (except the mobile bea-con) need to send any packets.

An advantage of this algorithm is that positionestimations are computed based on the samenode (mobile beacon), thus keeping the meanlocalization error low and preventing the propa-gation of this error. In addition, this algorithmavoids the use of nodes equipped with GPS,except for the mobile beacon. On the other hand,in this technique a sensor node can estimate itsposition only when the mobile beacon passes nearthis node, which may take a long time dependingon such factors as the size of the sensor field, thebeacon’s mobility capacity, and the node’s trajec-tory. Also, the mobile beacon may never passnear some nodes, because of either the trajectoryor a problem with the mobile beacon.

COMMENTS ABOUT LOCALIZATION ALGORITHMSSeveral localization algorithms focus on differentaspects such as errors, number of beacons, num-ber of settled nodes, or GPS usage, among otherthings. Usually, these algorithms [19, 20] try toreduce or completely remove the need for GPSreceivers on beacon nodes. Other algorithms takeadvantage of certain network features, such asbeacons with high-powered transmitters [4], a

directed localization recursion [22], or a beaconinfrastructure [18]. The choice of which algorithmto use depends on the resources available, the sce-nario, the requirements of the application, and themean localization error acceptable to the nodes.

FINAL REMARKS

This article has addressed the localization prob-lem from the viewpoint of a WSN. We dividedlocalization systems into three components: dis-tance/angle estimation, position computation,and localization algorithm.

The importance of such a division of localiza-tion systems into components lies in the need torecognize that the final performance of localiza-tion systems depends directly on each of thesecomponents. For instance, a localization systemshould achieve better results if the TDoA

n Figure 4. Example and phases of APS Dv-Hop.

2 hops, 54 m

4 hops, 110 m3 hops, 68 m

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Settled node

n Figure 5. Example and phases of RPE.

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n Figure 6. Operation and possible trajectories of a mobile beacon.

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method is used instead of RSSI to estimate dis-tances. The same principle applies to the othercomponents. These components can be seen assubareas of the localization problem that needto be studied separately.

A general rule in WSNs is that there is not aperfect solution suitable for every scenario. Thesame rule applies to the localization problem.This article has shown a number of proposedlocalization systems, each with an emphasis on aspecific scenario and/or application. The necessi-ty of different solutions for different applicationsand also the high number of possible applica-tions of WSNs have greatly motivated the studyand proposals of new solutions to the localiza-tion problem.

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[5] J. Bachrach and C. Taylor, “Localization in Sensor Net-works,” Handbook of Sensor Networks: Algorithms andArchitectures, I. Stojmenovic, Ed., Wiley, Sept. 2005.

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[8] Y. Fu et al., “The Localization of Wireless Sensor Net-work Nodes Based on DSSS,” Electro/Infor. Tech., 2006IEEE Int’l. Conf., 2006, pp. 465–69.

[9] K. Whitehouse, “The Design of Calamari: An Ad HocLocalization System for Sensor Networks,” Master’s the-sis, UC Berkeley, 2002.

[10] D. Niculescu and B. Nath, “Ad Hoc Positioning System(APS) Using AOA,” Proc. INFOCOM ’03, San Francisco,CA, 2003.

[11] V. Ramadurai and M. L. Sichitiu, “Localization in Wire-less Sensor Networks: A Probabilistic Approach,” Proc.ICWN 2003, Las Vegas, NV, June 2003, pp. 275–81.

[12] S. Simic and S. Sastry, “Distributed localization inwireless ad hoc networks,” UC Berkeley, Tech. rep.UCB/ERL M02/26, 2002.

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BIOGRAPHIESAZZEDINE BOUKERCHE ([email protected]) is a fullprofessor and holds a Canada Research Chair position atthe University of Ottawa. He is the founding director of thePARADISE Research Laboratory at the university. Prior tothis, he held a faculty position at the University of NorthTexas and worked as a senior scientist at the SimulationSciences Division, Metron Corporation, San Diego, Califor-nia. He was also employed on the faculty of the School ofComputer Science, McGill University, and taught at Poly-technic of Montreal. He spent a year at the JPL/NASA-Cali-fornia Institute of Technology where he contributed to aproject centered on the specification and verification of thesoftware used to control interplanetary spacecraft operatedby JPL/NASA Laboratory. His current research interestsinclude wireless ad hoc and sensor networks, wireless net-works, mobile and pervasive computing, wireless multime-dia, QoS service provisioning, performance evaluation andmodeling of large-scale distributed systems, distributedcomputing, large-scale distributed interactive simulation,and parallel discrete event simulation. He has publishedseveral research papers in these areas. He was the recipientof the Best Research Paper Award at IEEE/ACM PADS ’97and ACM MobiWac’06; of the 3rd National Award forTelecommunication Software in 1999 for his work on a dis-tributed security system on mobile phone operations; andhas been nominated for the Best Paper Award at IEEE/ACMPADS ’99 and ACM MSWiM ’01. He is a holder of anOntario Early Research Excellence Award (previously knownas the Premier of Ontario Research Excellence Award), anOntario Distinguished Researcher Award, and a GlinskiResearch Excellence Award. He is a co-founder of QShine,the International Conference on Quality of Service for Wire-less/Wired Heterogeneous Networks, served as a GeneralChair for the 8th ACM/IEEE Symposium on Modeling, Anal-ysis and Simulation of wireless and mobile systems, andthe 9th ACM/IEEE Symposium on distributed simulationand real-time application; has been Program Chair for ACMWorkshop on QoS and Security for Wireless and MobileNetworks, ACM/IFIPS Europar 200, IEEE/SCS Annual Simula-tion Symposium ’02, ACM WWW ’02, IEEE MWCN ’02,IEEE/ACM MASCOTS ’02, IEEE Wireless Local Networks WLN’03–’04, IEEE WMAN ’04–’05, and ACM MSWiM ’98–’99;and has been a Technical Program Committee member ofnumerous IEEE and ACM sponsored conferences. He hasserved as a Guest Editor for several publications.

ANTONIO A. F. LOUREIRO ([email protected]) holds a B.Sc.and an M.Sc. in computer science, both from the FederalUniversity of Minas Gerais (UFMG), and a Ph.D. in comput-er science from the University of British Columbia, Canada.Currently he is an associate professor of computer scienceat UFMG, where he leads the research group on wirelesssensor networks. His main research areas are wireless sen-sor networks, mobile computing, and distributed algo-rithms.

EDUARDO FREIRE NAKAMURA ([email protected]) isa researcher and full professor at the Center of Researchand Technological Innovation, Brazil. He received his Ph.D.in computer science from the Federal University of MinasGerais, Brazil, in 2007. His research interests includedata/information fusion, distributed algorithms, localizationalgorithms, wireless ad hoc and sensor networks, andmobile and pervasive computing. He has published severalpapers in the area of WSNs, and served as a Technical Pro-gram Committee member of the 2nd Latin American Auto-nomic Computing Symposium, supported by the IEEEComputer Society.

HORACIO A. B. F. OLIVEIRA ([email protected]) is aprofessor of computer science at Federal University ofAmazonas (UFAM), Brazil. He is currently working toward aPh.D. degree in computer science at UFMG) with a partialdoctoral fellowship at the University of Ottawa, Canada.He holds an M.Sc. in computer science from UFMG and aB.Sc. in computer science from UFAM. His research inter-ests include localization and synchronization algorithms,distributed algorithms, and wireless ad hoc, vehicular, andsensor networks. He is author of several papers in differentareas of his research interests.

The necessity of different solutions fordifferent applicationsand also the highnumber of possibleapplications of WSNs has greatlymotivated the studyand proposals ofnew solutions to thelocalization problem.


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Localization systems for wireless sensor networks