Modified VIKOR based Distributed ClusteringScheme for Wireless Sensor Networks

Tauseef Shah, Mansoor Mustafa, Syed Hassan Ahmed*, S.H.Bouk, Dongkyun Kim*Dept. of Electrical Engineering, COSMATS Institute of Information Technology, Islamabad, Pakistan

*School of Computer Science and Engineering, Kyungpook National University, Daegu, KoreaEmail:{tauseefshah01, mansoor.mustafa22}@gmail.com, bouk@comsats.edu.pk, hassan@monet.knu.ac.kr, dongkyun@knu.ac.kr

Abstract—Stability and lifetime of Wireless Sensor Networks(WSNs) mainly depend on energy of each node in the network.Hence, it is necessary for a WSN to be energy efferent. There aredifferent methods to preserve energy in WSNs and clustering isone of those methods. Clustering techniques divide whole networkinto small blocks, each having a managing node, called clusterhead (CH) and rest of the nodes within that block act as members.In this paper, we propose a distributed clustering scheme, calledModified VIKOR model based Clustering (MVC) protocol. Thistechnique uses multiple criteria i.e. residual energy, node degree,distance to the base station and average distance between a nodeand its neighbors, to select a cluster head. Each node shares thoseparameters within its transmission range and decides which CHis suitable within that region. Modified VIKOR method is used tooutrank the potential nodes as CHs by considering the conflictingcriteria. The realistic multi-hoping communication model is usedin both, inter-cluster and intra-cluster communication, insteadof single hop as in previous schemes. Simulation results showthat our purposed technique performs much better than thoseprevious methods in terms of energy efficiency, network life time,less CH deformation and control overhead.

Index Terms—WSNs, Clustering, Cluster Head, VIKOR, Dis-tributed, Multi-criteria

I. INTRODUCTION

In recent years WSNs have got interest due to developmentin micro-electronics. Applications of WSNs include atmo-spheric sensing, like temperature, pressure, humidity etc, nat-ural disasters, like earthquake monitoring and military appli-cations like battlefield monitoring [1][2][2][4]. WSNs consistsof tiny battery operated electronic devises called sensor nodes.Besides sensing, these nodes have memory, processor andposses communication capabilities. Sensor nodes are deployedin the field either strategically or randomly. In case of randomdeployment these sensor nodes have to be self organized in ad-hoc fashion. Replacement or recharge of battery is not possibleonce sensor nodes are dispersed in field [5][6].

Network lifetime and stability of WSNs depend on energyconsumption of sensor nodes. Lesser the energy consumption,longer will be the network lifetime. Sensor nodes consumeenergy in processing and communication with BS or othersensor nodes. For prolonging network lifetime and stability,WSNs should be energy efficient. Energy efficiency can beachieved by different means, like intelligently designing ofMAC and routing protocols. Routing protocols can be flat,Minimum Transmission Energy (MTE) or hierarchical proto-

cols. In flat routing protocols sensor nodes directly send data toBS. In MTE each sensor node sends data to its neighbor node,therefore load at sensor nodes near to base station is muchgreater than other sensor nodes, resulting in shorter lifetime[4]. In hierarchical protocols, whole network is divided insmall number of blocks, called clusters. Each cluster consistsof multiple number of sensor nodes. Inside each cluster a CHis selected to perform management and routing tasks for thatcluster.

Flat and MTE transmission protocols perform better insmall networks. If network size is large then these typeof protocols are not efficient. To overcome this deficiency,hierarchical or clustering protocols are designed for largenetworks [8]. In recent years researchers are focusing ondesign of energy efficient hierarchical protocols. CH selectionis a major constituent of hierarchal protocols. CH performsa major role in network stability and lifetime. Many routingprotocols are designed in this regard. Most of the previousdesigned clustering protocols consider single criterion for CHselection [9] or CH is selected randomly based on probabilisticmodel [8]. Single criterion include residual energy, distancefrom BS or density of sensor node etc. Considering onlyone criterion may not be sufficient in many specific cases,for example in a clustering protocol which considers residualenergy as CH selection parameter, worst case arises when asensor node with high residual energy located far away fromBS, selected as CH. In this case this selected CH requirelarge amount of energy to forward data to BS, resulting inshorter network lifetime. Similar kind of situation arises whenconsidering distance form BS as a CH selection parameterand a sensor node near to BS selected as CH having verysmall amount of residual energy. Hence by analyzing thesebottlenecks it is observed that single criteria is not sufficientenough to prolong network stability and lifetime.

In this paper we design a distributed multi-hop energyefficient clustering protocol, called Modified VIKOR modelbased Clustering (MVC) protocol based on four criteria,including residual energy, number of neighbor nodes, dis-tance form BS and average distance between neighbor nodes.VIKOR is an abbreviation in bosnian language for a techniquecalled VIeKriterijumska Optimizacija I Kompromisno Resenje,whose English meaning is Multicriteria optimization and com-promise solution. The main aim of this method is to focus on

2013 11th International Conference on Frontiers of Information Technology

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DOI 10.1109/FIT.2013.53

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ranking and selecting one alternative from a set of alternatives,if there is some contradiction between different alternatives.It introduces the multi-criteria ranking index based on thespecific measure of closeness to the ideal solution. A majorbottleneck of VIKOR method is that it fails to solve theproblems when one or more criteria have same values forall alternatives. To overcome this problem, we use ModifiedVIKOR method in our proposed protocol.

Rest of the paper is organized as follows: In section II wediscuss the related work in the area of clustering in WSNs.Section III describes the details of our proposed scheme.Section IV shows the performance of our proposed schemeby simulations and its comparison with previously proposedschemes. Finally section V concludes the manuscript.

II. RELATED WORK

In recent years WSNs have evaluated an important area forresearch. Applications of WSNs are in the areas of day-to-day activities, military and recently in healthcare. Day by dayincreasing applications has focused the attention of researchersto design specialized sensor devices and networks. As dis-cussed earlier that it is under very rare circumstances that thebattery is replaced or recharged due to nature of deployment.Hence energy is most important factor in WSNs. As discussedearlier, clustering is a famous technique to prolong energyefficiency in WSNs. Over the years many researchers haveproposed energy efficient clustering techniques in this field.Some popular clustering protocols are discussed here.

Low-Energy Adaptive Hierarchy (LEACH) [8] is verypopular clustering protocol designed in year 2000. It usesdeterministic CH selection technique, by using randomizedrotation of high energy CH positions among nodes in network.Hence load of energy consumption is distributed among allnodes. Each round in LEACH has two phases; setup phase andsteady state phase. In setup phase nodes organize themselvesinto clusters where as in steady state phase normal nodessend data to their respective CHs. Each node elects itself asCH in the start by assigning itself a probabilistic value. Thisvalue is based on energy of sensor node which has recentlyperformed CH operation or not served as CH in a givenround. Sensor nodes which have higher energy have a higherprobabilistic value and nodes with lower energy have lowerprobabilistic value. If two or more nodes have same energy,then probabilistic value goes high for nodes which are notselected as CHs in any previous rounds to fairly share nodeamong all nodes.

Hybrid Energy Distributed Protocol (HEED) [10] dividessensor nodes in different clusters using distributed algorithm.This protocol uses average energy (energy required to a sensornode to transmit a unit of data to CH) as parameter to selectCH. For a given sensor node which can be potential CH,Average Minimum Reach-ability Power (AMPR) is calculated.AMRP is the measure of expected intra-communication energyconsumption for a node if it becomes a CH.

In [11] authors have proposed centralized version ofLEACH (C-LEACH). In this protocol sensor nodes send their

location information to BS, then BS computes average nodeenergy. The only nodes above average value can be eligiblefor CH for a particular round.

All above discussed protocols are based on single criteria.In [12] authors have proposed CH selection criteria based onfuzzy-TOPSIS method, in their purposed model base stationis performing CH selection process (centralized). Due tocentralized scheme, nodes periodically send their data to basestation, then base station decides which nodes have to be CH.After selection of CH, base station broadcasts that informationto all nodes. CHs broadcast advertisement message and normalnodes send join request to CHs then CHs send acknowledg-ment packets. This increase control overhead. In their proposedscheme CH is changing in every round, which also increasescontrol overhead packets.They have considered three criteria.

In next section we present our proposed scheme basedon distributed multi-criteria, to overcome the bottlenecks ofpreviously proposed schemes.

III. PROPOSED SCHEME

In this section, we describe the details of our proposescheme for CH selection using distributed algorithm. In WSNsCHs are selected using one or more criteria. These criteriainclude residual energy, distance of sensor node from BS andnode density etc. However, considering all of these criteriais very difficult job because a sensor node might have highresidual energy but its sensor node density will be low, anothersensor node might have low residual energy but might havehigh node density. Therefore we adopt modified VIKOR [13]method, which is multi-criteria technique to select a suitableCH. According to this method each sensor node evaluate itselfwith respect to its neighbors and decides to become CH ornot. Four criteria to be consider by a sensor node for CHselection are residual energy, node density, distance from BSand average distance from its neighbor sensor nodes. Ourproposed scheme consists of three phases, which are describedbelow in detail:

A. Neighbor discovery

Here we explain neighborhood discover technique of a sen-sor node. Initially when sensor nodes are randomly deployedthey don’t have any information about their surroundings andtherefore cannot take part in CH selection process. In orderto discover neighbors, every sensor node broadcast its ID in aHELLO message in its intra-communication range and listensto medium for short period of time for similar messages. Eachtime when a sensor node receives this HELLO message, itcreates and updates its neighbor table by storing neighborIDs from this HELLO message. The sensor nodes in ourproposed scheme are static, therefore no need of neighbordiscovery in every communication cycle. After completingneighbor discovery, sensor nodes calculate average distancefrom its neighbor and also calculate its distance from the BS.The sensor node then broadcast an information packet whichcontains information about sensor nodes energy, number ofneighbors, average distance from its neighbor and distance

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from BS. In our proposed scheme neighbor discovery processis performed after few communication cycles in order to checkfor dead neighbors and to update routing table accordingly.Performing neighbor discovery process after few communica-tion cycles decreases HELLO over head, resulting in lowerenergy consumption. In the next section, we present detaileddescription of CH selection in our proposed scheme.

B. Cluster Head Selection

Comprehensive explanation of CH selection process ofour proposed scheme is given in this section. Based oninformation packet received, sensor node calculates its CHvalue (𝐶𝐻 𝑉 𝑎𝑙) and shares this (𝐶𝐻 𝑉 𝑎𝑙) with all of itsneighbors. Following are the steps involved in calculating(𝐶𝐻 𝑉 𝑎𝑙) of a sensor node.

Step 1: Every sensor node compares its each criterionvalue ′𝑉 ′ with every neighbor and determines the node withmaximum value and the node with minimum value for thatcriteria. We represent maximum value with Positive IdealCandidate (PIC), and minimum value with Negative IdealCandidate (NIC).

𝑃𝐼𝐶𝑘𝑗(𝑘 = 1, 2, ..., 𝑁) = 𝑚𝑎𝑥[𝑉𝑖𝑗 ∣(𝑗 = 1, 2, ..., 𝑐)

𝑖 = 1, 2, ...(𝑛+ 1)] (1)

𝑁𝐼𝐶𝑘𝑗(𝑘 = 1, 2, ..., 𝑁) = 𝑚𝑖𝑛[𝑉𝑖𝑗 ∣(𝑗 = 1, 2, ..., 𝑐)

𝑖 = 1, 2, ...(𝑛+ 1)] (2)

where 𝑘 is the number of sensor node, 𝑁 is the total numberof sensor nodes, 𝑖 is number of neighbor (it includes the sensornode it self), 𝑗 is the number of criteria, 𝑛 is the total numberof neighbor, 𝑐 is the total number of criteria and 𝑚𝑎𝑥 𝑣𝑎𝑙𝑘and 𝑚𝑖𝑛 𝑣𝑎𝑙𝑘 are the maximum and minimum values of 𝑘𝑡ℎ

sensor node’s 𝑗𝑡ℎ neighbor.Step 2: We assign weight ′𝑤′ to all four criteria according

to their importance. Since residual energy is major criterionfor selection of CH, so we assign 0.4 weight to it and 0.2 toeach other three criteria; i.e. number of neighbor sensor nodes,distance from BS and average distance between sensor nodeand its neighbors.

Step 3: In this step each sensor node calculates the distanceof each criterion (j) to the ideal solution (MAX/MIN) and thencomputes sum of all the distances to obtain the final value.For some criteria like, residual energy, greater values providesbetter chances for a sensor node to become CH therefore itsdistance is calculated to the positive ideal solution by equation3 while criterion like distance to BS low value provides betterchance for a sensor node to become CH, so its distance iscalculated from negative ideal solution by equation 4. Equation5 is used to calculate regret measure.

𝑆∗𝑘 =𝑐∑

𝑗=1

(𝑃𝐼𝐶𝑗 − 𝑉𝑘𝑗)/(𝑃𝐼𝐶𝑗 −𝑁𝐼𝐶𝑗) (3)

𝑆−𝑘 =𝑐∑

𝑗=1

(𝑉𝑘𝑗 −𝑁𝐼𝐶𝑗)/(𝑃𝐼𝐶𝑗 −𝑁𝐼𝐶𝑗) (4)

𝑅𝑘 = 𝑚𝑎𝑥𝑗 [𝑤𝑗 ((𝑃𝐼𝐶𝑗)/

(𝑃𝐼𝐶𝑗 −𝑁𝐼𝐶𝑗))∣𝑗 = 1, 2, .., 𝑐] (5)

Where 𝑆𝑘 represents the distance of sensor node 𝑘 tothe ideal solution and 𝑅𝑘 shows the regret measure. Aftercomputing these 𝑆𝑘 and 𝑅𝑘 values, sensor node broadcast thisinformation to its neighbors and listens to channel for similarinformation. The ranking of a sensor node with its neighbordepends on the above information.

Step 4: Each node calculate its CH value (𝐶𝐻 𝑣𝑎𝑙) andits neighbor 𝐶𝐻 𝑣𝑎𝑙 from the broadcast information usingfollowing equations.

𝐴 = (𝑆𝑖 − 𝑆𝑚𝑖𝑛)

𝐵 = (𝑆𝑚𝑎𝑥 − 𝑆𝑚𝑖𝑛)

𝐶 = (𝑅𝑖 −𝑅𝑚𝑖𝑛)

𝐷 = (𝑅𝑚𝑎𝑥 −𝑅𝑚𝑖𝑛)

𝐶𝐻 𝑣𝑎𝑙𝑘 =

⎧⎨⎩

𝑣(𝐴)/(𝐵)+

(1− 𝑣)(𝐶)/(𝐷) 𝑆𝑚𝑎𝑥 ∕= 𝑆𝑚𝑖𝑛 ∩𝑅𝑚𝑎𝑥 ∕= 𝑅𝑚𝑖𝑛

(𝐶)/(𝐷) 𝑆𝑚𝑎𝑥 = 𝑆𝑚𝑖𝑛 ∩𝑅𝑚𝑎𝑥 ∕= 𝑅𝑚𝑖𝑛

(𝐴)/(𝐵) 𝑅𝑚𝑎𝑥 = 𝑅𝑚𝑖𝑛 ∩ 𝑆𝑚𝑎𝑥 ∕= 𝑆𝑚𝑖𝑛

𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡 𝑅𝑚𝑎𝑥 = 𝑅𝑚𝑖𝑛 ∩ 𝑆𝑚𝑎𝑥 = 𝑆𝑚𝑖𝑛

𝑆𝑚𝑖𝑛 = 𝑚𝑖𝑛[(𝑆𝑖)] (6)

𝑆𝑚𝑎𝑥 = 𝑚𝑎𝑥[(𝑆𝑖)] (7)

𝑅𝑚𝑖𝑛 = 𝑚𝑖𝑛[(𝑅𝑖)] (8)

𝑅𝑚𝑎𝑥 = 𝑚𝑎𝑥[(𝑅𝑖)] (9)

where 𝑖 = 1, 2, 3.......(𝑛+ 1) and 𝑣 is the weight for strategyof maximum group utility and 1−𝑣 is the weight of individualregret. Value of 𝑣 is set to 0.5.

Step 5: After calculating 𝐶𝐻 𝑣𝑎𝑙, sensor node will thencompare its 𝐶𝐻 𝑣𝑎𝑙 with its neighbors. If 𝐶𝐻 𝑣𝑎𝑙 of thesensor nodes is less then its any neighbor, it elect itself as CHand broadcast advertisement packet to all of its neighbors.If the sensor node 𝐶𝐻 𝑣𝑎𝑙 is greater then at least one ofits neighbors, it will wait for advertisement packet from itsneighbor node which has lowest 𝐶𝐻 𝑣𝑎𝑙.

Every sensor node checks that whether its is the roundimmediately after deployment or not. If its not the roundimmediately after deployment, sensor node predicting CH’s𝐶𝐻 𝑣𝑎𝑙 by calculates the remaining energy of neighbor’s andCH’s by calculating their energy consumed on transmission.The remaining energy of neighbor’s and CH’s is calculatedfrom their last know energy and distance from BS. All othercriteria remain same as all the sensor nodes are static. Sensor

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node compare its own 𝐶𝐻 𝑣𝑎𝑙 with the CH’s 𝐶𝐻 𝑣𝑎𝑙.If difference between sensor node’s and CH’s 𝐶𝐻 𝑣𝑎𝑙 isgreater than threshold, the sensor node broadcast a notificationmessage with all criteria information and and waits for itsneighbor’s information. After receiving neighbor’s informa-tion, sensor node perform all of the above steps. If differencebetween sensor node’s and CH’s 𝐶𝐻 𝑣𝑎𝑙 is not greater thanthe threshold, sensor node wait for a specific period of timefor notification from other sensor nodes. The sensor nodebroadcast its criteria information if receive notification fromother sensor node and get associated with new CH otherwiseit remain associated with the current CH as there no new CHelected. When CHs are elected they send an advertisementpacket to their entire neighbors. Nodes which receive thisadvertisement packet will send a join request message to theCH in order to join that respective cluster. CH, in responseassign TDMA slots for sensor node communication, and sendsan acknowledgment packet to sensor node.

C. Sensor Nodes Communication

After CH selection and sensor nodes association with theirCHs, sensor nodes communication starts. Sensor nodes sendtheir sensed data to their associated CHs in assigned TDMAslots. The data received from the sensor nodes are collected,aggregated and amplified by the CH. CH then forward thiscollected data to the BS as shown in Fig. 1. In our proposedscheme there are two types of communication operations.These are inter-cluster communication and intra-cluster com-munication.

In Multi-hop inter-cluster communication, when whole net-work is divided into multiple clusters each cluster has one CH.This CH is responsible for communication for all sensor nodesin the cluster. CH receive data from all sensor nodes at single-hop and aggregate and transmit directly to BS or throughintermediate CH. In Multi-hop inter-cluster communicationwhen distance between CH and BS is larger than 10 meters,then CH uses intermediate cluster-head to communicate toBS. Through multi-hop communication, energy consumptionis minimized which in turn increases overall network lifetimeand stability. Packets to BS and other simulation results arediscussed in next section.

IV. SIMULATION ANALYSIS

The performance of proposed scheme is compared andanalyzed in this section. We compare and analyze our proposedscheme with fuzzy-TOPSIS based [12]and LEACH [8] routingprotocols. We consider five scenarios in our proposed protocol.Table I shows values of criteria for each scenario. In thistable, C1 represents residual energy, C2 represents number ofnodes, C1 is distance form BS, whereas C4 is the averagedistance of a node from its neighbors. The simulation ofpreviously proposed schemes and our proposed protocol isdone in MATLAB.

𝑁 number of sensor nodes are scattered in the area of 100mX 100. BS is settled at the top of the field i.e at (50,100)location. In our simulation we assume that sensors node are

Fig. 1. Communication Model

TABLE ICRITERIA VALUES FOR EACH SCENARIO

Scenario C1 C2 C3 C41 0.25 0.25 0.25 0.252 0.4 0.2 0.2 0.23 0.2 0.4 0.2 0.24 0.2 0.2 0.4 0.25 0.2 0.2 0.2 0.4

sensing continuously the environment for information and theyhave always data to send. We also assume that the the channelused for communication is noise free and collision free. Theparameters for simulation are summarized in table II

TABLE IISIMULATION PARAMETERS

Parameter ValueNetwork Area 100m x 100mNumber of Nodes 𝑛 100Base Station Position (50,100)Initial Energy 0.5 JData Aggregation Energy 50pj/bit/reportData Packet Size 4000 bitsHello Packet Size 200 bitsTransmitter Electronics (EelectTx) 50 nJ/bitReceiver Electronics (EelecRx) 50 nJ/bitTransmit Amplifier (Eamp) 100 pJ/bit/m2

We evaluate the stability of the network by examining thenumbers of rounds until first node dies. Following graphsshow simulation results of our purposed scheme comparedwith previous schemes.

In Fig. 2, network stability and lifetime are shown whichdepicts that our proposed schemes outstrip the previous pro-tocols. This is because of minimizing hello overhead byrestricting the CH selection to some threshold furthermore,the multi-hope communication model, that we used in ourprotocol, also play a vital role in increasing the network life

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MVC Scenario 1MVC Scenario 2

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Fig. 2. Number of Dead Sensor Nodes

time. Figure 2 shows that stability of stability of LEACH is200 rounds and fuzzy based protocol is around 700 roundswhere nodes starts to die. Network lifetime for both LEACHfuzzy based protocol is around 1100 rounds. For our proposedprotocol, stability and lifetime for scenario 1 are 1200 and2400 rounds respectively. For scenario 2 they are 1300 and2400 rounds respectively. Here greater value for stability isdue to assigning highest weight to residual energy. Stabilityand lifetime for scenario 3 are 1100 and 2100 rounds. In thisscenario , highest weight is assigned to number of neighbors,hence it CH may be selected with lower residual energy. Thisis the reason for shorter network lifetime in this scenario.In scenario 4, stability is around 900 rounds. In this case,nodes near to BS are selected as CHs, so the nodes awayfrom BS start dying earlier, but nodes near to BS remain alivefor longer time, hence the lifetime for this scenario is around2400 rounds. Similarly in scenario 5, stability is 1000 roundsand network lifetime is 2400 rounds. Hence form all results,we observe that network performance is better in scenario 2as compared to all other cases. Hence the CHs selected withhigher weight to residual energy. perform better than all otherscenarios, and due to high residual energy they are capableenough to forward large amount of data to BS for longer time.

Figure 3 depicts the energy consume per round of everyprotocol. It can be analyzed from Fig. 3 that energy consumedper round in our protocol is less then the previous protocols.LEACH consumes 0.2J of energy, whereas fuzzy based modelconsumes 0.35J energy per round. When we observe ourproposed protocol, we can see that all scenarios consume 0.25Jof energy except scenario 2, which consumes 0.2J. Hence theCHs selected with higher weight of residual energy, consumelesser energy than all other protocols. In our proposed protocolcontinuity can be observe in energy consumption, however incompared protocols specially LEACH there is much ups anddowns in energy consumption per round. This is because ofproper distribution of CH responsibilities on the sensor nodesand minimum changing in CHs. The CHs changing is shownin Fig. 4. Results of Fig. 4 clearly show that there is verymuch less changes in CHs in our proposed because of theprotocol however in previous, almost all the CHs changingrapidly in every round. Due to less changes in CHs the hellopackets overhead decreases tremendously as shown in Fig. 5.

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Fig. 3. Energy Consumption per Round

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Fig. 4. Cluster Head Change per Round

This decrease in hello over head increases the overall life timeof the network.

Figure 6 shows the number of CHs per round. It can beanalyzed from the Fig. 6 that in LEACH, the number CHsare varying in every round. In fuzzy-TOPSIS based protocol,number of CHs are almost 12 percent of the total number ofsensor nodes. This is because of centralized algorithm used inthis protocol. In our protocol, however distributed algorithm isused but still the number of CHs remain constant because ofthe proper CH selection technique. It is clear from the graphthat in scenario 1, 3 and 4, there is no CH change till 1400rounds. CHs start changing after 1400 rounds, but they arenot changing as frequently as in LEACH. In scenario 5, CHsare constant till 1500 rounds and in scenario 2 they start tochange after 1600 rounds. Greater stability can be observed inscenario 2, due to long network lifetime and stability.

Overall throughput of the network is show in Fig. 7. LEACHsends 0.5𝑥104 packets to BS. The total number packets sentto the BS in our protocol is much higher than other protocolsas depicted in fig.7. This is because of extended lifetime andstability of network.

V. CONCLUSION

We propose a new routing protocol in this paper, whichovercome the deficiencies in the existing routing protocols.Our protocol is based on multiple criteria including energy ofsensor, total number of neighbor of a sensor nodes, distancefrom BS of a sensor node, and average distance of sensornodes from its neighbor. We consider three seniors in ourproposed protocol, one with higher weight assigned to residualenergy, other with higher weight assigned to number of

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Fig. 5. Number of Hello Packets

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Fig. 6. Total Number of Cluster Heads per Round

neighbor nodes and last with equal weights to all criteria. Weapply modified VIKOR method for selection of cluster head,which overcomes the bottlenecks in simple VIKOR method.Our proposed protocol is based on distributed algorithm. Inour proposed protocol CHs do not change frequently. If thedifference between CH val of any sensor node and CH valof CH, to which the sensor node is associated, is greaterthen some threshold, then the CH will change, and sensornodes will execute the election process within that cluster.Due to this check the CHs change process is controlled andnumber of hello overhead packets in proposed protocol isminimized. To improve communication model, we introducedtwo level hierarchical communication. Simulation, analysisand comparison is performed in MATLAB. The results ofsimulations shows that our proposed protocol better the fuzzy-TOPSIS based protocol and LEACH. When comparing threeseniors of our protocol, it is clear that the scenario with higherweight to residual energy, performs better than with assigningequal weights to all criteria, because residual energy is mainconstituent for a sensor node to be selected as CH.

ACKNOWLEDGMENT

This research was supported by the MSIP (Ministry ofScience, ICT & Future Planning), Korea, under the C-ITRC (Convergence Information Technology Research Center)support program (NIPA-2013-H0401-13-1005) supervised bythe NIPA (National IT Industry Promotion Agency). Thiswork was supported by the IT RD program of MSIP/KEIT.[10041145, Self-Organizing Software platform (SoSp) forWelfare Devices].

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Fig. 7. Number of Packets to Base Station

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