SecLoc – secure localization in WSNs using CDS

SECURITY AND COMMUNICATION NETWORKSSecurity Comm. Networks 2011; 4:763–770

Published online 6 April 2011 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/sec.278

SPECIAL ISSUE PAPER

SecLoc -- secure localization in WSNs using CDSAvinash Srinivasan*

Pennsylvania Center for Digital Forensics, Bloomsburg University of Pennsylvania, Bloomsburg, PA 17815, U.S.A

ABSTRACT

Originally, the development of wireless sensor networks (WSNs) was motivated by military applications such as battlefieldsurveillance and land-mine detection. Over time, however, WSNs have found a wide range of applications in diversedomains such as industrial automation and monitoring, environmental and habitat monitoring, health-care applications,home automation, traffic regulation, smart hospitals, etc. In all these domains, the data sensed by the sensor nodes arereported to a central server called a base station, which then initiates appropriate actions based on the reported data. Tothis end, the location of sensors is very critical since the monitored event can be detrimental causing irreversible damage --such as forest fire, if the location of sensors is compromised and/or inaccurate. In this paper, we propose SecLoc -- anovel localization method for WSNs, which can be easily extended to other wireless and mobile and ad-hoc networks. Theproposed method exploits the connected dominating set (CDS) property of a network graph. SecLoc, to the best of ourknowledge, is the first localization model to exploit the CDS property for accurate and secure node localization in WSNs.In out proposed method, a set of specialty nodes, called the beacon nodes, with large resource base, assume the role ofDominant nodes. The beacon nodes are responsible for both accurate and secure localization of nodes. We confirm theefficiency and robustness of our model through simulation results. Copyright © 2011 John Wiley & Sons, Ltd.

KEYWORDS

beacon nodes; connected dominating set (CDS); localization; security; wireless sensor networks (WSNs)

*Correspondence

Avinash Srinivasan, Pennsylvania Center for Digital Forensics, Bloomsburg University of Pennsylvania, Bloomsburg, PA 17815, U.S.A.E-mail: [email protected]

1. INTRODUCTION

Wireless sensor networks (WSNs), which have become theepitome of pervasive technology, are shaping many activi-ties in our lives. They have become an inevitable part of bothmilitary applications such as -- battlefield surveillance andland-mine detection, as well as civilian applications suchas -- industrial automation, equipment monitoring, environ-mental, and habitat monitoring, health-care applications,home automation, traffic regulation, smart hospitals, etc.One common requirement, though, in all of these diverseapplication domain of WSN’s is the criticality of the loca-tion of sensors. Localization is the process by which a nodedetermines its current geographic location. Several methodshave been proposed over the last two decades to accomplishaccurate node localization.

The core function of a WSN is to detect and report eventsto a central base station. These reported data/events canbe meaningfully assimilated and responded to, only if theaccurate location of the event is known. In some applica-tion domains, the WSN also initiates response mechanismsin addition to reporting the sensed event. One such appli-cation domain is fire detection in which the sensor network

is also delegated the responsibility of initiating a fire extin-guishing mechanism rather than merely reporting the eventto a base station. Such response initiation responsibilitiesare built into the WSN to mitigate the inevitable damage toa minimum till the response team arrives at the the eventlocation.

The method employed to compute the actual locationdepends on the signal feature used. This can be classifiedinto three main groups as follows and has been capturedintuitively in Figure 1.

(i) Triangulation: The triangulation method involvesgathering angle of arrival (AoA) measurements at thesensor node from at least three sources. Then usingthe AoA references, simple geometric relationshipsand properties are applied to compute the location ofthe sensor node.

(ii) Trilateration: Trilateration is a method of deter-mining the relative positions of objects using thegeometry of triangles similar to triangulation. Unliketriangulation, which uses AoA measurements tocalculate a subject’s location, trilateration involvesgathering a number of reference tuples of the form (x,

Copyright © 2011 John Wiley & Sons, Ltd. 763

Secure localization in WSNs A. Srinivasan

Figure 1. (a) Triangulation, (b) trilateration, and (c) multilateration.

y, d). In this tuple, d represents an estimated distancebetween the source providing the location referencefrom (x, y) and the sensor node. To accurately anduniquely determine the relative location of a pointon a 2D plane using trilateration, a minimum of threereference points are needed.

(iii) Multilateration: Multilateration is the process oflocalization by solving for the mathematical inter-section of multiple hyperbolas based on the timedifference of arrival (TDoA). In multilateration, theTDoA of a signal emitted from the object to three ormore receivers is computed accurately with tightlysynchronized clocks. When N receivers are used, itresults in N − 1 hyperbolas, the intersection of whichuniquely positions the object in a 3D space. When alarge number of receivers are used, N > 4, then thelocalization problem can be posed as an optimiza-tion problem that can be solved using, among others,a least squares method.

Localization problem can be addressed by simply mount-ing a GPS receiver on very sensor node ensuring accurateand secure localization of all sensor nodes. However, sincesensors are often deployed in very large numbers, this is nota feasible solution from an economic perspective. On theother hand manual configuration of sensor locations is toocumbersome and hence not feasible.

Many protocols have been devised to enable the locationdiscovery process in WSNs to be autonomous and func-tion independent of GPS and other manual techniques. [1,2]are two such works to this end. The focal point of loca-tion discovery, in the above literatures, has been a set ofspecialty nodes known as beacon nodes, also referred toby some researchers as anchor, locator, or seed nodes. Inthis paper, we shall use the term beacon node without theloss of generality. The beacon nodes know their location,either through a GPS receiver or through manual configu-ration. Beacon nodes transmit beacon signals that enableother sensor nodes to compute their location. Beacon sig-nals can be either transmitted in response to a request or atpredetermined intervals of time.

Using these beacon signals, sensor nodes compute theirlocation employing various one or more features of the sig-nals such as AoA, Received Signal Strength Indicator, etc,which is discussed in detail in Ref. [3]. It is, therefore, criti-cal that malicious beacon nodes be identified, isolated, and

prevented from providing false location information to sen-sor nodes that completely rely on the information providedto them by the beacon nodes for computing their location.

According to Ref. [3], localization also has securityrequirements, which are listed below. The breach of anyof these security requirements is indicative of compromisein the localization process.

(i) Authentication: Information for localization mustbe provided only by authorized sources. Therefore,before accepting location-related information, theprovider has to be authenticated.

(ii) Integrity: The information provided by the beaconnodes should be tamper resistant so that sensor nodescan use it to discover their location.

(iii) Availability: All the information required by a sensornode to compute its location must be available whenneeded.

(iv) Non-repudiation: Neither the source that providesthe location information nor the sensor nodes thatreceive the location information should be able denythe information exchange at a later time.

(v) Privacy: Location privacy is one of the most impor-tant security requirements. The source should onlyhelp the sensor node in determining its location.Neither the source’s location nor the sensor node’slocation should be disclosed at any point. Thisconstraint helps to prevent malicious nodes fromclaiming a different legitimate location in the net-work.

Errors in the estimated location of a sensor can be clas-sified into two groups: intrinsic and extrinsic [4]. Intrinsicerrors are most often caused by abnormalities in the sen-sor hardware and software. On the otherhand, extrinsicerrors are attributed to the physical effects on the mea-surement channel including shadowing effects, changes insignal propagation speed, obstacles, etc. Extrinsic errors aremore unpredictable and harder to handle.

According to Ref. [3] there are three visible advantagesof knowing the location information of sensor nodes. Theyare as follows:

(i) Location information is needed to identify the loca-tion of an event of interest. For instance, the locationof an intruder, the location of a fire, or the location of

764 Security Comm. Networks 2011; 4:763–770 © 2011 John Wiley & Sons, Ltd.DOI: 10.1002/sec

A. Srinivasan Secure localization in WSNs

Figure 2. (a) CDS before applying Rule-k (b) CDS after applying Rule-k.

enemy tanks in a battlefield is of critical importancefor deploying rescue and relief troops.

(ii) Location awareness facilitates numerous applicationservices, such as -- location directory services thatprovide doctors with the information of nearby med-ical equipment and personnel in a smart hospital,target-tracking applications for locating survivorsafter an earthquake in debris, or for locating enemytanks in a battlefield.

(iii) Location information can assist in various systemfunctionalities, such as geographical routing [5],network coverage checking [6], and location-basedinformation querying [7].

Additionally, according to Ref. [3], there are three impor-tant metrics associated with localization: energy efficiency,accuracy, and security. Our proposed model, SecLoc, willaddress all the above metrics. In particular, the focus willbe more on energy efficiency, given that sensors have verylimited resources. SecLoc is based on the connected dom-inating set (CDS) property of a network graph. The CDSis developed using the localized Rule-k algorithm proposedby Dai and Wu in Ref. [8].The contributions of this papercan be summarized as follows:

(i) CDS based localization of sensor nodes has beenproposed for the first time.

(ii) The proposed method address all three metrics oflocalization as discussed above.

(iii) To the best of our knowledge, password protectionand autowipe of beacon node contents has beenemployed for the first time to provide robustnessagainst node capture/compromise attacks.

(iv) The proposed method can be easily extended toMANETs. Also, the reputation and trust-basedmodel proposed in Ref. [9] can be easily integratedinto the proposed model to isolate and revoke mali-cious beacon nodes.

The rest of this paper is organized as follows. In Section 2,we provide a brief overview of dominating sets (DSs) andCDSs. In Section 3, we discuss the proposed SecLoc methodin detail. Later in Section 4, we discuss the simulation envi-

ronment and the results in detail. Then in Section 5, weprovide a detailed discussion on related work followed byconclusion and directions for future research in Section 6.

2. OVERVIEW OF CDS

We give a brief overview on CDS in this section. Consider anundirected graph G = (V, E), with V being the set of verticesand E being the set of edges. In G, a node p dominatesanother node q if and only if p = q or p and q are adjacent.For example, in Figure 2a, the set {1, 2, 11, 14} is a DS.Furthermore, let the CDS of G be a set of vertices VCDS suchthat VCDS ⊂ V. Now, we have the following:

Definition 1 A CDS of a graph G = (V, E) is a set ofvertices VCDS ⊂ V such that for every vertex q ∈ V − VCDS ,there is at least one vertex p ∈ VCDS that dominates q, andVCDS is connected.

We also briefly discuss the localized Rule-k algorithmproposed by Dai and Wu in Ref. [8] to reduce the size ofa CDS. Using Rule-k, a node p can be unmarked from theCDS if p is completely covered by a subset of its neighborsN′ and the following conditions are satisfied:

(i) Subgraph induced by N′ is connected.(ii) Every neighbor of p is adjacent to at least one node

in N′.(iii) All nodes in N′ have a higher priority than p.

For illustration, the set {1, 2, 4, 7, 8, 9, 10, 11, 13, 14}forms the CDS of the network shown in Figure 2a. It reducesto {4, 7, 8, 9, 13, 14} after applying Rule-k. In our example,when Rule-k is applied to the CDS in Figure 2a, CDS nodes1, 2, 10, and 11 are pruned, resulting in a smaller CDS (40%smaller) {4, 7, 8, 9, 13, 14}, as shown in Figure 2b. Notethat the CDS can be constructed using the vertex ID, Vertex-Degree, remaining battery power, or a combination of anyof these as the priority value when inducing nodes into theCDS. The CDS constructed in Figure 2a is based on vertexID priority (Figure 2a).

We have compared the size of the resulting CDS sizeby varying network diameter and range. In the comparison,we have compared the size of the CDS with both Vertex ID

Security Comm. Networks 2011; 4:763–770 © 2011 John Wiley & Sons, Ltd. 765DOI: 10.1002/sec


Figure 3. (a) Comparison of CDS size with Vertex-ID and Vertex-Degree as node priority with fixed d = 10 and Rbn = 15 m, (b) comparisonof CDS size with Vertex-ID and Vertex-Degree as node priority with fixed d = 20 and Rbn = 15 m, (c) comparison of CDS size with Vertex-ID and Vertex-Degree as node priority with fixed d = 30 and Rbn = 15 m, (d) comparison of CDS size for different transmission ranges

with diameter fixed at d = 20.

and Vertex degree as the priority parameter when includingnodes in the CDS (Figure 3).

3. SECLOC

SecLoc is a novel sensor node localization model based onthe CDS property of a network. In our model, the networkconsists of two kinds of nodes -- sensor nodes and beaconnodes. The CDS algorithm is run on the entire networkconsisting of both these types of nodes. The ratio of sensorto beacon nodes necessary for functioning of our model,i.e., the number of beacon nodes necessary to obtain a CDSin any network deployment, is determined empirically andthe results are presented in Section 4.

We are considering a static sensor network in our pro-posed model. A CDS is formed consisting of only beaconnodes, which have a large resource base. Additionally,only beacon nodes participate during the CDS construc-tion phase. This is accomplished by pre-loading the CDSand Rule-k algorithms only onto the beacon nodes [10]. Ini-tially after deployment, beacon nodes execute a neighbordiscovery algorithm the response to which clearly distin-guishes beacon and sensor neighbors of each beacon node.Then the CDS algorithm is executed to construct a CDS.

In case a CDS cannot be obtained, the algorithm is repeat-edly executed until a CDS is obtained. Executing the CDSalgorithm only on the beacon nodes ensures that sensorsdo not expend their limited precious resources. The reasonfor choosing beacon nodes with a large resource base toserve as the localization backbone is to ensures that sen-sors that are meant for monitoring are utilized only for theirprimary purpose. With the CDS of beacon nodes as thelocalization backbone, sensor nodes conserve their energyby off-loading the responsibility of transmitting beacon sig-nal to the beacon nodes. This ensures the energy-efficiencymetric of localization.

Note that a DS will suffice our need of secure localizationunder the assumption that an adversary cannot compromisebeacon nodes. However, with a CDS, each beacon node inthe CDS will be within the transmission range of at leastone other beacon node and there by any malfunctioning ofbeacon nodes can be detected easily. To accomplish this,CDS nodes, when they hear a beacon signal from any otherCDS node verify that its functioning normally as desired.Beacon nodes can be configured to report any malfunctionto the base station in which case the CDS can serve as therouting backbone to transmit the report to the base station.

Since the beacon nodes form the CDS, they cover theentire network such that every sensor node is within the



transmission range of atleast one beacon node. For improv-ing the accuracy of localization, we can impose the k-CDSconstraint such that each sensor node lies within the trans-mission range of at least k-beacon nodes. This ensures thatthe accuracy metric of localization is addressed to an extentdeemed necessary based on the application domain andcriticality of node location.

SecLoc is a secure model for sensor node localization.In all beacon node based localization models, the mostcritical security threat arises from malicious behavior ofbeacon nodes. The malicious node behavior can be a resultof either hardware/software malfunction or due to captureand compromise of the node by an adversary. In this paper,we address only the malicious behavior resulting from cap-ture and compromise. Once captured, the adversary hasaccess to all secure and confidential information on boardthe captured node. This could include cryptographic keys,cryptographic seeds for key refreshing, etc. The adversarycan now compromise the captured node to function as hedesires. This can be potentially dangerous if the behavior isskewed subtly and difficult to notice. A reputation and trust-based model have been proposed to capture such maliciousbehavior arising due to capture and compromise in Ref. [9],which isolates the compromised node.

However, in the proposed model, we are assuming thatthe beacon nodes are specialty nodes with a large resourcebase and are hard to compromise. We also assume that anadversary can only capture but cannot compromise a beaconnode. Practically this can be achieved by implementing apassword protection for the beacon nodes. All secure andconfidential information are placed on a ROM. On threeincorrect login attempts, a wipe process will be initiatedto protect the information onboard the sensor. The wipeprocess will erase all contents of the ROM. This ensuresthe security metric of localization.

4. SIMULATION AND RESULTS

In this section, we first discuss the simulation environmentfollowed by the results.

4.1. Environment

All simulations have been carried out on a custom built,stand-alone C++ simulator. In our simulations, a sensorfield of area 10 × 100 m2 has been considered. The fol-lowing parameters have been considered tunable in oursimulations: number of sensor nodes Nsn, number of bea-con nodes Nbn, and transmission range Rbn of beacon nodesexpressed in meters.

4.2. Result

The results presented in this section have been averagedover 500 iterations for statistical stability. We have studied

the impact of different parameters on the size of the CDS. Asa first step, we have studied the impact of diameter on CDSsize independent of density. We have plotted two curves ineach of the three graphs shown in Figure 4a--c. One curvedenotes the change in CDS size with respect to networksize in which the CDS has been constructed using Vertex-ID as the node priority value. The second curve denotes thechange in CDS size with respect to network size in whichthe CDS has been constructed using Vertex-Degree as thenode priority value. We see that with higher diameter, thetwo priority criteria produce a CDS of nearly the same size.Also, with increasing diameter, we can see that the size ofthe CDS shrinks.

With d = 10, transmission range of 15 m, and Vertex-IDas the node priority value, the CDS is on average about 22%the size of the network. With Vertex-Degree as the priority,it is about 16% the size of the network. These results areshown in Figure 4a. For d = 20 with transmission range of15 m, the CDS is about 15% the size of the network withVertex-ID as node priority value and 14% the size of thenetwork with Vertex-Degree as node priority value. Thesize of the CDS, with d = 10, transmission range of 15 m,and Vertex-ID as priority value, is on average about 6%larger than that with Vertex-Degree.

In comparison, with d = 20, transmission range of 15 m,the CDS size is around 1% larger with Vertex-ID as nodepriority value compared to Vertex-Degree as node priorityvalue. Similarly we have studied the impact of diameterd = 30 with transmission range of 15 m on the CDS size.These results are shown in Figure 4c. In the rest of oursimulations, unless otherwise specified, vertex-ID is usedas the node priority value in constructing the CDS.

We have also studied the impact network diameter d onthe CDS size for varying transmission ranges Rbn. We havefixed d = 10 and varied the transmission range Rbn of beaconnodes. Varying the transmission range of beacon nodes Theresults for d = 10 are presented in Figure 4d.

We have also studied the impact of density on CDS sizeby fixing the transmission range. We have simulated threedifferent scenarios with range fixed at 15, 30, and 45 m,respectively. The results are presented in Figure 4c. Fromthe graph it is evident that the size of the CDS shrinks withincreasing transmission range. This is because, more nodeslie in the range of a dominant node with higher transmis-sion range there by reducing the overall size of the CDS.Nonetheless, the size of the CDS increases with increase inthe network size as expected but at much slower rate withhigher transmission range. With range fixed at 15 m withd = 20, the size of the CDS, on average, is about 42% of thesize of the network. Similarly, with range fixed at 30 and45 m, the CDS size is 22 and 15% the size of the network,respectively.

In Figure 5a,b, we have presented results showing the per-centage of successful CDS constructions averaged over 100trials. Specifically, in Figure 5a, we have presented resultsplotting the percentage of successful CDS constructionsagainst the number of nodes in the network for differenttransmission ranges 15, 30, and 45 m, respectively. In all



Figure 4. (a) Comparison of CDS size for different transmission ranges with diameter fixed at d = 10, (b) Comparison of CDS size fordifferent transmission ranges with diameter fixed at d = 20.

Figure 5. (a) Comparison of percentage of successful CDS construction for different transmission ranges with d = 10, (b) comparison ofpercentage of successful CDS construction for different transmission ranges with d = 20, (c) comparison of percentage of successfulCDS construction for different d values with transmission range fixed at 15 m, (d) comparison of percentage of successful CDS

construction for different d values with transmission range fixed at 30 m.

these simulation runs, the diameter fixed at d = 10. We cansee from the results that on average for 100 trials, a CDSis constructed successfully 87% of the times for a trans-mission range of 15 m with fixed d = 10. Similarly, withtransmission ranges of 30 and 45 m, a CDS is constructedsuccessfully 89.5 and 94% of the times, respectively, fora fixed d = 10. This clearly indicates that with increasingtransmission range, the success rate of CDS constructionsalso increases. Similarly, in Figure 5b, we have presented

results plotting the percentage of successful CDS con-structions against the number of nodes in the network fordifferent transmission ranges 15, 30, and 45 m, respectively,with diameter fixed at d = 20. Similar observations can bemade as discussed above. However, notice that the suc-cessful CDS construction percentage drops with increasein diameter of the network.

In Figure 5c,d, we have plotted the results highlightingthe percentage of successful CDS construction for a fixed



Figure 6. (a) SN/BN ratio varied with fixed n = 1000. (b) n varied from 500--2000 in steps of 500 with SN:BN ratio fixed at 80:20.

transmission range of 15 m and varied the d value d = 10and 20. We can see from the results presented in Figure5c that the performance is better with a smaller d-value.With d = 10 and transmission range fixed at 15 m, a CDSis constructed successfully 87% of the times where as ford = 20 it drops to 83%. Similar obervations can be madefrom results presented in Figure 5d where the transmissionrange is fixed at 30 m. Notice that the performance dropswith increase in transmission range.

From the above results, we are confirming that a CDS willbe constructed with a very high percentage of success forany given network scenarios assuring that the localizationprocess will not be denied due to non availability of a CDS.Even if a case were to arise wherein a CDS was not success-fully constructed, the algorithm will be executed repeatedlyuntil one is obtained. We have used results from Figure 4to determine the optimum senor to beacon node ratio in anetwork to ensure the existence of a CDS.

In Figure 6a, we examine the effect of varying the ratioof BNs to SNs on the robustness of our model assuming theadversary is able to bypass the password protection of bea-con nodes after capturing them. The network was deployedwith 1000 SNs, and the number of BNs was varied to getthe appropriate ratios. The transmission range for both SNsand BNs was fixed at 20 m. SN to BN ratios of 95:5, 80:20,66:33, and 50:50 were tested, and the system performedthe best with 50:50 ratio. However, with a 80:20 ratio, 50%of SNs can withstand a collusion of five malicious node intheir neighborhood where as with a 66:33 ratio they canwithstand up to 8. It is evident from the results that higherthe number of BNs the more robust our model gets but itoffsets the benefits economically.

Similarly, we have studied the impact of the total numberof nodes on the robustness of our model. The results arepresented in Figure 6b.

5. RELATED WORK

Localization in WSNs has draw significant attention fromthe research community. Several methods have been

proposed for accomplishing secure, reliable, and accuratelocalization in WSNs over the last decade. However, incor-porating all desired properties into one localization methodis extremely difficult if not imposable since meeting onerequirement inevitably opens up vulnerabilities in anotherdesired property. Secure localization has been a key researcharea in recent years. In this section, we will review someimportant research works related to node localization inWSNs. For more details, please refer to Ref. [3].

Over the years, many protocols have been devisedto enable the location discovery process in WSN to beautonomous and function independent of GPS and othermanual configuration techniques[1,2,11--14].

In Ref. [13], Savvides et al. present a novel approachfor sensor localization in an ad hoc network called AHLoS(Ad Hoc Localization System) that enables sensor nodes todiscover their locations using set distributed iterative algo-rithms. In Ref. [15], Sastry et al. introduced the concept ofsecure location verification, and show how it can be usedfor location-based access control. In Ref. [16], Lazos andPoovendran have addressed the problem of enabling sensorsof WSNs to determine their location in an untrusted environ-ment. They have proposed a range independent localizationalgorithm called SeRLoc. SeRLoc is a distributed algo-rithm and does not require any communication amongsensors.

In Ref. [17], Liu et al. have presented a suite of techniquesthat detect malicious beacon signals, identify malicious bea-con nodes, revoke malicious beacon nodes, detect replayedbeacon signals, and avoid false detection. Their revocationscheme works on the basis of two counters maintained foreach beacon node -- attack counter and report counter.

Finally, in Ref. [9], Srinivasan et al. have proposedimprovisations to the model proposed in Ref. [17] with areputation framework for detecting and isolating maliciousbeacon nodes. The method proposed in Ref. [9] is the firstwork of its kind to employ a reputation-based frameworkfor ensuring secure sensor node localization. This modelpresents methods for countering attacks due to informationasymmetry arising in a beacon-based sensor node localiza-tion model.



For interested readers, Srinivasan et al. have provided asurvey on secure localization in WSNs in Ref. [3]. In Ref.[18], Srinivasan and Wu have presented some groundworkfor CDS-based localization in sensor networks.

6. CONCLUSION AND FUTUREWORK

In this paper, we have proposed SecLoc, a novel wirelesssensor node localization method based on the CDS propertyof a network graph. This is the first proposal of its kind toexploit the CDS property to ensure energy efficient, secure,and accurate node localization in WSNs. Through simula-tion we have confirmed the effectiveness of our model. Tothe best of our knowledge, the proposed model is the firstto employ password protection and autowipe to secure theinformation stored onboard a sensor node upon capture. Thefollowing are on the agenda of our future work

(i) We would like to apply the CDS property of a net-work graph to solve more problems in WSNs as wellas MANETs.

(ii) Assume beacons nodes can be compromised andstudy its impact on localization security.

(iii) Consider dynamic sensor networks to study theimpact of reconstruction of CDS on networkresources and consequently on network longevity.

ACKNOWLEDGEMENTS

This work was supported in part by Bloomsburg UniversityResearch and Disciplinary Grant 2008--2009.

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SecLoc – secure localization in WSNs using CDS