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Hindawi Publishing CorporationJournal of Computer Networks and CommunicationsVolume 2012, Article ID 971685, 8 pagesdoi:10.1155/2012/971685

Research Article

Efficient and Secure Routing Protocol for Wireless SensorNetworks through Optimal Power Control and OptimalHandoff-Based Recovery Mechanism

S. Ganesh1 and R. Amutha2

1 Electronics and Communication Engineering, Sathyabama University, Tamil Nadu, Chennai 600075, India2 ECE Department, SSN College of Engineering, Tamil Nadu, Chennai 603110, India

Correspondence should be addressed to S. Ganesh, [email protected]

Received 14 January 2012; Accepted 10 June 2012

Academic Editor: Youyun Xu

Copyright © 2012 S. Ganesh and R. Amutha. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Advances in wireless sensor network (WSN) technology have provided the availability of small and low-cost sensor with capabilityof sensing various types of physical and environmental conditions, data processing, and wireless communication. In WSN, thesensor nodes have a limited transmission range, and their processing and storage capabilities as well as their energy resources arealso limited. Modified triple umpiring system (MTUS) has already proved its better performance in Wireless Sensor Networks.In this paper, we extended the MTUS by incorporating optimal signal to noise ratio (SNR)-based power control mechanism andoptimal handoff-based self-recovery features to form an efficient and secure routing for WSN. Extensive investigation studies usingGlomosim-2.03 Simulator show that efficient and secure routing protocol (ESRP) with optimal power control mechanism, andhandoff-based self-recovery can significantly reduce the power usage.

1. Introduction

Wireless Sensor Network is widely considered as one of themost important technologies for the twenty-first century.The sensing electronics measure ambient conditions relatedto the environment surrounding the sensor and transformthem into an electrical signal. In many WSN applications [1],the deployment of sensor nodes is performed in an ad hocfashion without careful planning and engineering. In the pastfew years, an intensive research that addresses the potentialof collaboration among sensors in data gathering and proc-essing and in the coordination and management of thesensing activities was conducted. However sensor nodes areconstrained in energy supply and bandwidth. Such con-straints combine with a typical deployment of large numberof sensor nodes that pose many challenges to the design andmanagement of WSNs and necessitate energy awareness at alllayers of the networking protocol stack.

At the network layer, it is highly desirable to find methodsfor energy-efficient route discovery and relaying of data fromthe sensor nodes to the base stations, so that the lifetime of

the network is maximized. Routing in WSN is very chal-lenging due to the inherent characteristics that distinguishthese networks from other wireless networks like mobile adhoc networks or cellular networks. First, due to the relativelylarge number of sensor nodes, it is not possible to build aglobal addressing scheme for the deployment of largenumber of sensor nodes as the overhead of ID maintenance ishigh. Thus, traditional IP-based protocols may not be ap-plied to WSN. Second, in contrast to typical communicationnetworks, almost all applications of sensor nodes requirethe flow of sensed data from multiple sources to a particularbase station. Third, sensor nodes are tightly constrained interms of energy, processing and storage capacities. Thus theyrequire careful resource management. Further, in most appli-cation scenarios, nodes in WSNs are generally stationaryafter deployment except for, maybe, a few mobile nodes.Due to such differences, many algorithms like low-energyadaptive cluster hierarchy (LEACH), power-efficient gather-ing in sensor information systems (PEGASIS), and virtualgrid architecture (VGA) have been proposed for the routingproblems in WSNs.

2 Journal of Computer Networks and Communications

Energy conservation is critical in Wireless Sensor Net-works. Replacing or recharging batteries are not an option forsensors deployed in hostile environments. Generally com-munication electronics in the sensor utilizes most energy.Stability is one of the major concerns in advancement ofWireless Sensor Networks (WSNs). A number of applicationsof WSN require guaranteed sensing, coverage, and connec-tivity throughout its operational period. Death of the firstnode might cause instability in the network. Therefore, all ofthe sensor nodes in the network must be alive to achieve thegoal during that period. One of the major obstacles to ensurethese phenomena is unbalanced energy consumption rate.Different techniques have already been proposed to improveenergy consumption rate such as clustering, efficient routing,and data aggregation.

In [2], Li et al. have investigated the joint powerallocation (PA) issue in a class of MIMO relay systems. Byusing the capacity and the mean-square error (MSE) as opti-mization criterion, two joint PA optimization problems havebeen formulated. As the cost functions derived directly fromthe capacity and the MSE would lead to nonconvex optimiza-tion, two modified cost functions corresponding to a convexproblem of the source and the relay power weighting coeffi-cients have been developed. The key contribution of the pro-posed method lies in the discovery of a tight bound for thecapacity and the MSE that simplifies the joint source andrelay power allocation into a convex problem. A distinctfeature of the new method is that the power allocation withinthe source and that within the relay are jointly optimal forany given power ratio of the two units.

Li et al. and Yang et al. [3, 4] have studied the joint powerallocation problem for multicast systems with physical-layernetwork coding based on the maximization of the achievablerate. To deal with the nonconvex optimization problem, ahigh-SNR approximation is employed to modify the originalcost function in order to obtain a convex minimizationproblem, where the approximation is shown to be asymptot-ically optimal at the high-SNR regime. As an alternative, aniterative algorithm has been developed by utilizing theconvexity property of the cost function with respect to a partof the whole power coefficients. Considering the low com-plexity of the physical layer network coding in the multicastsystem, the lattice-based network coding that uses the pro-posed joint power allocation schemes has been suggested.

In this paper, we investigate the performance of ESRP inWSN to attain extra routing information through optimalSNR-based power control mechanism and optimal handoff-based self-recovery features to reduce the power usage anddecrease the latency of packet delivery. The rest of this paperis organized as follows. In Section 2, the related work is brief-ly reviewed and discussed. Then we describe our networkmodel, adversary model, and notations used throughout inthis paper in Sections 3, 4, and 5. Simulation results arepresented in Section 6. We conclude this paper in Section 7.

2. Related Work

The task of finding and maintaining routes in WSNs is non-trivial since energy restrictions and sudden changes in node

status cause frequent and unpredictable topological changes.Routing protocols can be classified into three categories,namely, proactive (table driven), reactive (on demand), andHybrid protocols depending on how the source finds a routeto the destination. In proactive protocols sensors advertisetheir routing state to the entire network to maintain acommon (partially) complete topology of the network.Examples of such schemes are the conventional routingschemes, Destination-Sequenced Distance Vector (DSDV).On the other hand, reactive protocols establish paths onlyupon request, for example, in response to a query, or anevent; meanwhile, sensors remain idle in terms of routingbehavior. Sensors forward each routing request to peers untilit arrives at a sink; the latter will respond over the reversecommunication path. Examples of reactive routing schemesare Ad hoc On-demand Distance Vector (AODV), DynamicSource Routing (DSR). Hybrid protocols use a combinationof these two ideas. Ayyaswamy Kathirvel and Rengaramanu-jam Srinivasan proposed a new protocol that modifies AODVto improve its performance.

In [5], Wang et al. has proposed a cross-layer jointrouting and MAC-PHY design to achieve energy balance andenergy efficiency simultaneously in WSN. The energy-bal-anced routing distributes the levels of residue energy evenlythroughout the network, while the optimal transmissionpower control achieves further energy savings by adjustingthe transmission power to meet the communication qualityrequirements at the receiver.

In [6], Chen and Terzis have presented a work for mobilesensor nodes based on Log-normal path loss model. Theyhave started with the hypothesis that if the lognormal modelholds perfectly, then the signal strengths at nearby locationsare independent. In turn, this implies that finding the loca-tion whose packet reception ratio is above a certain thresholdcan be modeled as a sequence of Bernoulli trials. Therefore,the number of attempts required to find a location with highPRR is geometrically distributed. Furthermore, they arguedthat the geometric distribution is desirable because it doesnot exhibit long tails, and therefore the number of attemptsto find a good location should usually be small, provided thata reasonable percentage of location with high PRR exists inthe search vicinity. Rather than looking at signal strength var-iations in the time domain, they focused on variations in thespace domain. Specifically, they studied the implications ofthe lognormal path loss model when deploying or movingsensor motes.

2.1. Introduction to AODV. Ad hoc On-Demand DistanceVector (AODV) Routing is a routing protocol for mobile adhoc networks (MANETs) and other wireless ad hoc networks.It is jointly developed in Nokia Research Center, University ofCalifornia, Santa Barbara and University of Cincinnati byC. Perkins, E. Belding-Royer, and S. Das. It is a reactiverouting protocol, meaning that it establishes a route to adestination only on demand. It employs destinationsequence numbers to identify the most recent path. When anode needs to determine a route to a destination node, itfloods the network with a Route Request (RREQ) message.

Journal of Computer Networks and Communications 3

Ni−1 Ni Ni + 1 Nn−1 Nn

Ui−1 Ui Ui + 1 Ui + 2 Un−1 Un

Figure 1: Triple umpiring system.

The originating node broadcasts a RREQ message to itsneighboring nodes, which broadcast the message to theirneighbors and so on. To prevent cycles, each node remembersrecently forwarded route requests in a route request buffer.As these requests spread through the network, intermediatenodes store reverse routes back to the originating node. Sincean intermediate node could have many reverse routes, italways picks the route with the smallest hop count. When anode receiving the request either knows of a “fresh enough”route to the destination or is itself the destination, the nodegenerates a Route Reply (RREP) message and sends thismessage along the reverse path back towards the originatingnode. As the RREP message passes through intermediatenodes, these nodes update their routing tables, so that, inthe future, messages can be routed though these nodes to thedestination.

2.2. Introduction to Triple Umpiring System. In the umpiringsystem [7] as shown in Figure 1, each node is issued with atoken at the inception. The token consists of two fields: NodeID and status. Node ID is assumed to be unique and deemedto be beyond manipulation; status is a single bit flag. Initiallythe status bit is preset to zero indicating a green flag. Thetoken with green flag is a permit issued to each node, whichconfers it the freedom to participate in all network activities.Each node in order to participate in any network activity,say Route Request RREQ, has to announce its token. Ifstatus bit is “1” indicating “red flag,” then the protocoldoes not allow the node to participate in any networkactivities. Gwalani et al. in [8] proposed a new protocol thatmodifies AODV to improve its performance. The protocol,AODV-PA, incorporates path accumulation during the routediscovery process in AODV to attain extra routing infor-mation. They have shown from the results that AODV-PAimproves the performance of AODV under conditions ofhigh load and moderate-to-high mobility. Krco and Dup-cinov in [9] observed a problem that affects the neighbordetection algorithm of the AODV-routing protocol and has adeteriorating impact on performance of ad hoc networksthat use this protocol. An improvement of the neighbordetection algorithm [10] based on the differentiation of goodand bad neighbors using signal to noise ratio (SNR) value isproposed, described, and experimentally verified.

3. MTUS Model

TUS [11, 12] can be modified to enable path accumulationduring the route discovery cycle. When the Route Request

(RREQ) and Route Reply (RREP) messages are generated orforwarded by the nodes in the network, each node appendsits own address on these route discovery messages. Eachnode also updates its routing table with all the informationcontained in the control messages. As the RREQ messages arebroadcast, each intermediate node that does not have a routeto the destination forwards the RREQ packet after appendingits address in the packet. Hence, at any point the RREQpacket contains a list of all the nodes traversed. Whenever anode receives a RREQ packet, it updates the route to thesource node. It then checks for intermediate nodes accu-mulated in the path. Before making an entry, we proposedifferentiation between the so-called “good” neighbors and“bad” neighbors. Classification is done dynamically based onthe signal to noise ratio (SNR) value that is measuredwhenever a packet that contains a TUS message is received.Neighbors are typically classified as “bad” if the qualityof the interconnecting channel is poor; that is, it is notgood enough to carry broadcast and unicast messages withsufficient quality regardless of transmission rate or codingtechnique.

4. MTUS with Optimal SNR-BasedPower Control

In wireless signal transmission, one of the major sourcesof loss is attenuation. Basically the communication rangedecreases as the transmission data rate increases. One of theimportant parameter of interest is bit error rate (BER). Thedesirable BER value can be mapped into a desirable SNRvalue for a given modulation scheme. The desirable SNRvalue required by a given data rate increases with the datarate. That is, if data rate increases, the probability of erroralso increases, and a higher SNR value is required at thetransmitter to achieve the same BER at the receiver. Hencepower supply increases with the SNR value.

The relation between transmit power PS and the SNRvalue at the receiver (SNRRx) is given by

SNRRx = PSN· A, (1)

where “A” is the channel attenuation factor includingantenna gain in transmission.

The noise power “N” can be expressed as

N = N0 · RS, (2)

where N0 is the noise power density. The transmissionsymbol rate is given by

RS = R

b, (3)

where “R” is the transmission rate, and “b” is the modulationconstellation size. Consider

SNRRx = EbN0· b, (4)

where “Eb” is energy per bit

Eb = PS ∗ Tb. (5)


The transmission time of each bit is as follows:

Tb = 1R. (6)

Hence the optimal transmission in terms of specific desirableBER at the receiver end can be expressed as

PS = SNRRx ·N · 1A

,

PS = SNRRx · RS · N0

A,

(7)

PS = RS · bN0

A

EbN0

, (8)

where the factor “A” is the product of antenna gain andchannel loss. We have

A = K ∗ L−1. (9)

The relationship between BER and SNR for quadrature phaseshift keying (QPSK) is given by

BER = 12

erfc

√EbN0

. (10)

Hence the ratio Eb/N0 can be calculated from BER as follows:

Eb

N0=[

erfc−1(2 · BER)]2. (11)

From (8)

PS = RS · b ·(N0

A

)[erfc−1(2 · BER)

]2. (12)

The signal strength at the receiver can be calculated asfollows.

In a receiver, this concept involves the following parame-ters.

Minimum Detectable Signal Power (MDS). It is dependenton the modulation type as well as the noise specs of theantenna and receiver.

Maximum Allowable Signal Power (MAS). It is limited by thecompression or third-order intercept points.

Minimum Detectable Signal (MDS). For a given receivernoise power, MDS determines the minimum signal-to-noiseratio at the output of the receiver (SNRo). Typical minimumSNR for QPSK with Pe = 10−5 is 10 dB. We have

Si min = KT0FB(S0

N0

)min

. (13)

In dB

Si min(dBm) = −174 + B(dBHz) + F(dB) +(S0

N0

)min dB

,

(14)

where “K” is the Boltzmann constant, “F” is the receivernoise Figure, B is the receiver bandwidth, and T0 = 290 K.

The receiver dynamic range (DRr) can be calculated as

DRr = MASMDS

. (15)

The measured receiver signal strength (aggregated value) canbe fed back in the beacon message to let the transmitter toknow the received signal strength. Based on the receiverfeedback, the transmitter either increases or decreases thetransmit power PS there by achieving optimal power reduc-tion.

5. MTUS with Optimal Handoff-BasedSelf-Recovery Feature

We also did a modification in MTUS link breakage recovery[13] mechanism. In MTUS, the source node broadcastsRREQ message to find a new route to the destination whenthe link break is occurred. As an improvement of MTUS, self-recovery [10, 11] MTUS takes the intermediate node, whichdetects the link break, to repair the break route.

Once the intermediate node cannot repair the route intime, the backward prehop node tends to find a new routeinstead. MTUS tends to repair break route if the broken nodeis near to the destination node. Otherwise, if the break nodeis far away from destination node, a Route Error (RRER)message is sent back to source node, and the source noderebroadcasts RREQ to find a new route. Self-recovery MTUScan repair break route without considering the distancebetween the broken node and the destination node. Becausethe intermediate nodes are usually nearer than the sourcenode to the destination, the intermediate nodes on the dataflow are more suitable than the source to broadcast RREQ torepair or find a route to destination. In the optimal handoff-based self-recovery feature, the route maintenance is per-formed by detecting the link break before the complete fail-ure of the link. For this purpose each node maintains a neigh-bors power list (NPL). The decision to predict a link break[14] is made on the basis of received power with which anode is received from its neighbors with whom it forms partof an active route. When a link break on a path of data deliv-ery occurs, the intermediate node upstream of that breakmay choose to repair the link locally by itself [15] if its powerlevel is greater than certain threshold. Otherwise the sourcewill reinitiate a route discovery instead. The flow chart foroptimal handoff-based self-recovery feature is shown inFigure 2. The proposed method will intimate the problem tothe upper layers if the packet has not been received by thereceiver after fifteen trials.

6. Experimental Results and Analysis

We use a simulation model based on Glomosim-2.03 [16] inour evaluation.

Our performance evaluations are based on the simula-tions of 500 wireless sensor nodes that form a wireless sensornetwork over a rectangular (1000 × 1000 m) flat space.


Node send RREQ

Is route available?No

Start

End

No

No

No

2

1

1

Yes

Yes

2

Yes

Yes

Check NPL

Neighbor power threshold?

Forward RREQ to next hop

Is destination reached?

Break count ≤hop count /3?

Intermediate node initiates the recovery

Find the strongest node from NPL

Replace the next hop by the strongest node

Handoff the route recovery to the strongest node

Source-initiated recovery

Figure 2: Flow chart for optimal hand off based self recovery.

Table 1: Simulation parameters.

Area of sensing field 1000 ∗ 1000 m

Number of sensor nodes 500

Simulation time 600 s

Frequency 2.4 GHz

Bandwidth 2 Mbps

Traffic type Constant bit rate (CBR)

Payload size 30 to 70 Bytes

Number of loads 200 Packets

Number of nodes 500 nodes

Propagation limit (dbm) −111.0

Path loss model Two ray model

The MAC layer protocol used in the simulations was thedistributed coordination function (DCF) of IEEE 802.11.The performance setting parameters are given in Table 1. We

fix the distance between the source and sink to be 350 meters.The other 498 nodes are deployed between the source-sinkpair. We assume that all nodes are able to adjust transmissionpower arbitrarily. Thus all nodes can send packets directly toany other node and allocate optimal transmission power forall possible link state information. We assume that channelconditions remain fixed during the time period of the end-to-end transmission of every packet. Each flow did notchange its source and destination for the lifetime of asimulation run. We also evaluate the performance of allrouting strategies under two different transmission powersettings:

(1) Tx power: 15 dBm (Transmission range is376.782 m).

(2) Tx power: 10 dBm (Transmission range is282.547 m).

6.1. Setting Up the Transmission Range. The radio range isthe average maximum distance in usual operating conditionsbetween two nodes. There is no standard and commonoperating procedure to measure a range (except in free space,which is useless), so we cannot really compare differentproducts from the ranges as indicated in the mobile devicesdata sheets. If we want to compare mobile nodes in terms ofrange performance, we must look closely at the transmittedpower and sensitivity values. These are some measurable char-acteristics of the hardware which indicate the performanceof the products in that respect. The transmitted power is thestrength of the emissions measured in Watts (or milliWatts).Government regulations limit this power, but also having ahigh transmit power will also be likely to drain the batteriesfaster. Nevertheless, having a high transmit power will help toemit signals stronger than the interferers in the band. Thesensitivity is the measure of the weakest signal that may bereliably heard on the channel by the receiver (it is able toread the bits from the antenna with a low error probability).This indicates the performance of the receiver, and the lowerthe value the better the hardware. Usual values are around−80 dBm (the lowest, the better, e.g., −90 dBm is better). Apossible methodology to determine the transmission radiorange in GloMoSim would be the following.

(1) Set the propagation pathloss model (PROPAGA-TION-PATHLOSS parameter).

(2) Fix the received power of the destination antenna(RADIO-RX-THRESHOLD parameter).

(3) Fix the distance and calculate the transmittedpower according to the selected propagation pathlossmodel.

(4) Set this value to the RADIO-TX-POWER parameter.

GloMoSim has the following propagation models: free spaceand the ground reflection (or two-ray) models. The-freespace propagation model is used to predict received signalstrength when the transmitter and receiver have a clear,unobstructed line-of-sight between them. This model pre-dicts that transmission power is attenuated in proportion to


the square of the distance. The ground reflection (two-ray)model considers both the direct path and a ground-reflectedpropagation path between transmitter and receiver. Thismodel predicts that received power falls of distance raised tothe fourth power, or at a rate of 40 dB/decade. This is a muchmore rapid path loss than is experienced in free space. Wehave

Pr = Pth2t h

2r

d4GtGr , (16)

where ht and hr for the height of the transmitter and receiverantennas, and in GloMoSim this value is hard coded to 1.5meters. The antenna gain (represented in GloMoSim by theRADIO-ANTENNA-GAIN parameter) is a measure of thedirectionality of an antenna. Antenna gain is defined asthe power output, in a particular direction, compared tothat produced in any direction by a perfect omnidirectionalantenna (isotropic antenna). In both models, we have tomeasure the loss or attenuation of signal strength. The decibelis a measure of the ratio between two signal levels. Thedecibel gain is given by the following equation:

GdB = 10 log10Pout

Pin. (17)

It is convenient to be able to refer to an absolute level ofpower in decibels so that gains and losses with reference toan initial signal level may be calculated easily. The dBW(decibel Watt) is used extensively in microwave applications.The value of 1 W is selected as a reference and defined to be0 dBW. Another common unit is the dBm (decibel milliWatt),which uses 1 mW as the reference. Thus 0 dbM = 1 mW. Thisis represented in the following formulae:

PowerdbW = 10 logPowerW

1W, PowerW = 10PowerdbW/10,

Powerdbm = 10 logPowermW

1 mW, PowermW = 10PowerdbW/10,

(18)

PROPAGATION-PATHLOSS = TWO-RAY, PROPAGA-TION-LIMIT = −111 (dBm), RADIO-FREQUENCY = 2.4 e9 (hertz), RADIO-TX-POWER = 15 (dBm), RADIO-RX-THRESHOLD = −81 (dBm), RADIO-ANTENNA-GAIN =0.0 (dBm).

The propagation pathloss model indicates us to use thetwo-ray model formula as follows:

Gt = Gr = 0 dBm, (19)

Pr = Pth2t h

2r

d4GtGr ,

d = 4

√Pth

2t h

2r

Pr= 4

√(31.6227 mW)(1.5)2(1.5)2

7× 10−9 = 388 m.

(20)

We compared our ESRP with two different transmissionpower levels based on the following output parameters.

0

20

40

60

Th

rou

ghpu

t

80

100

0 100 200 300 400 500 600

Number of nodes

Through in bits (s)

TX power 15 dBmTX power 10 dBm

Figure 3: Number of nodes versus throughput.

0

1

2

3

4

5

6

0 100 200 300 400 500 600

Del

ay

Number of nodes

End-to-end delay in seconds


Figure 4: Number of nodes versus end to end delay.

(1) Packet delivery ratio. It is the ratio of the number of datapackets successfully delivered to the destinations to thosegenerated by the sources. Consider

PDR = Nr

Nt, (21)

where Nr is the number of data packets successfully received,and Nt is the number of data packets transmitted.

(2) End to end delay (Seconds). It indicates the time taken forthe message to reach from source to destination.

(3) Energy consumption in mWH. Figure 3 shows that thereduction in the TX power helps to improve the throughputas the number of nodes increases. Figure 4 shows thatthe end-to-end delay considerably decreases, with lowertransmission power, even though it increases initially. Theaverage energy consumption is reduced from 40 mWH to30 mWH when TX power is reduced from 15 dBm to 10 dBmas shown in Figure 5.


0

10

20

30

40

50

0 100 200 300 400 500 600

En

ergy

con

sum

ptio

n

Number of nodes

Energy consumption in mWH


Figure 5: Number of nodes versus energy consumption.

7. Conclusion

We are attempting to develop a comprehensive approachto understand the fundamental performance of informationrouting in energy-limited wireless sensor networks throughoptimal SNR- [17, 18] based power control mechanism andoptimal handoff-based self-recovery features. We presentedsome results for a few different small-scale WSN experimentsto study the solutions obtained for these problems as wevary the fairness constraints [19, 20]. We found that higherfairness constraints can result in significant decrease ininformation extraction and higher energy usage. Anotherobservation about the results is that the flow and energycurves show qualitatively abrupt changes as the fairness con-straints are varied. Based on the simulation results, we canconclude that efficient and secure routing protocol (ESRP)with optimal power control mechanism and handoff-basedself-recovery can significantly reduce the power usage. Wenote that this is a very much work in progress. We are cur-rently trying to make the models richer and more useful foranalyzing different kinds of wireless sensor networks. Onesignificant extension [21] would be to incorporate in- net-work aggregation to capture the data-centric nature of thesesystems. Other extensions we are looking into include in-depth analysis of the impact of other parameters [22] such assensor deployment/placement, different modulation andpath loss methods, and energy and information extractionconstraints. In the longer term, we also hope to enhance theoptimization-based formulations with closed-form analyti-cal expressions [23].

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[23] K. Lee and H. Lee, “A self-organized and smart-adaptiveclustering and routing approach for wireless sensor networks,”International Journal of Distributed Sensor Networks, vol. 2012,Article ID 156268, 13 pages, 2012.

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