a novel approach to increase overall

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2012 Inteational Conference on Computing, Electronics and Electrical Technologies [ICCEET]

A Novel Approach to Increase OverallEfciency in Wireless Sensor Networks

RaghunandanGHDepartment of Electronics and Communication

lobal Academy of Techology,(Afl o Vsvsvy colocl Uvs)Bangalore, India

Ab-Wireless Sensor Networks (WSNs) consist of

small nodes with sensing, computation and wireless

communications capailities. Evolution in wireless

sensor network has roadened its pervasive and

uiquitous applications in numerous elds. These

applications often require accurate information

collecting as well as uninterrupted, prolonged activeservice. Routing protocols have signicant impact on

the overall energy consumption of sensor networks.

Suitale Energyecient routing algorithms are

required to the inherent characteristics of these types of

networks are needed. Due to resource limitations in

wireless sensor networks, prolonging the network

lifetime has een of a great interest. Most of the energy

of sensor nodes is utilized for transmission of data to the

ase station. Thus, it makes them to deplete their

energy much faster. In this paper, Centrality ased

Cluster approach is used along with a movale ase

station to reduce the energy consumption of cluster

heads. ccording to the simulation results, the proposed

scheme has proved its eciency in the network lifetime,

residual energy of network. The proposed scheme also

shows improvement in performance of WSN compared

to other routing scheme.

Kw w wk, h, p

I. NTRODUCTONCurrent developments in the world convey us thespeed at which the enancements in technologies aremoving. Wireless sensor networks ae a bridge to thephysical world. It is a fast growing and existingresearch area which has attracted considerable

research attention in the recent past; this is backed bythe recent tremendous technological advancement inthe development of low-cost sensor devices equippedwith wireless network interfaces which aetechnically and economically feasible. The sensingelectronics measure ambient conditions related to theenvironment surrounding the sensor and transformthem into an electric signals which when processedreveal some properties about objects located orevents happening in the vicinity of the sensor. A

978-1-4673-0210-4/12/$31.00 2012 IEEE 699

Saga MetDepartment of Electronics and Communication

Atria Institute of Tehnology(Afl o Vsvsvy colocl Uvs)Bangalore, India

Wireless Sensor Network (WSN) contains hundredsor thousands of these sensor nodes which can benetworked in many applications that requireunattended operations, these have the ability tocommunicate either among each other or directly toan exteal base-station and also allows for sensingover larger geographical regions with greateraccuracy. Each sensor node bases its decisions on itsmission, the information it currently has, knowledgeof its computing, communication, and energyresources and have capability to collect and routedata either to other sensors or back to an extealbase station or stations which may be a xed or amobile node capable of connecting the sensornetwork to an existing communication inastructureor to the Inteet where users have access to thereported data. Hierarchical or cluster-based routing,are well-known techniques with special advantagesrelated to scalability and ecient communication. As

such, the concept of hierarchical routing is alsoutilized to perform energy efcient routing in WSNs.In a hierachical architecture, higher energy nodescan be used to process and send the informationwhile low energy nodes ca be used to perform thesensing in the proximity of the target. Therefore, itmakes them to deplete their energy much faster. Themain problem statement is more energy consumptionto transfer data om source to base station.aticulaly, if the base station is far away omsource. Recently, some ideas have been proposedbased on Movable base station. In these algorithms,the base station moves around

the network in order

to reduce the distance of communication and thusdecrease the energy consumptions. Utilizingintelligent techniques improves the efciency ofwireless sensor network. In applications that requirereal time decision making, zzy system is a powerltool that can make decision even if there isinsucient data. In this paper, centrality basedclustering approach along with movable base stationis used which is managed by a zzy logic forperformance improvement in WSN. In this paper,


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Section 2 discusses cluster based routing tecniques.Section 3 describes the centrality based clusteringapproach nd Section 4 provides simulation resultsand discusses the eciency of this proposed scheme.Finally, Section 5 gives the conclusion of the paper.

II. USTER ASED ROUTNG ECHNQUESRouting in sensor networks has attracted a lot ofattention in the recent years and introduced uniquechallenges compared to traditional data routing inwired networks. LEACH, PEGASIS, TEENAPTEEN are some of hierarchical routing protocols.In this cluster based routing techniques, it is theresponsibility of cluster heads to send data to the BS.Clustering can be used as an energy ecientcommunication protocol. The main aim of clusteringis to minimize the total transmission power dissipatedover the nodes in the selected path, and to balance theload among the nodes and to increase the networklifetime. Clustering is grouping of sensor nodes.

Here, each cluster is managed by a special node orleader, called cluster head (CH), which is responsiblefor coordinating the data trnsmission activities of allsensors in its group.CH is decided with a differentprobability [7]. All sensors in a cluster communicatewith a cluster head that acts as a local coordinator orsink for performing intra transmission arrangementand data aggregation. Cluster heads in t transmitsthe sensed data to the global sink.The operation isdivided into rounds. Each round consists of setupphase and the steady state phase. In the setup phase,the clusters are organized and Cluster Heads (CHs)are selected. Each sensor in generates a random

number between 0 and 1. If this number is less than T(n) deed by equation (1), then sensor n would beselected as a cluster head.j P nEGT n = - P * r mod )

therwe(1)

In this equation, P is the desired percentage of CHs, rspecies the current round and G is the set of nodeswhich are le aer cluster head election, the CHs

broadcast an advertisement message and other nodesselect the closest CHs based on the received signal

strength. In this paper, we consider more eectiveparameters for managing the movements of the basestation.

III. ENTRATY ASED USTER PROACHWireless sensor networks is a fast growing andexisting research area which has attractedconsiderable research attention in the recent past. Inhierarchical routing algorithms, CHs are responsible

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for gathering, compressing and forwarding the data tothe base station. Thus, they are responsible forconveying the entire information of the members ofthat cluster. If member of a cluster is Far om theCH then energy of such node will be wasted in orderto transmit the data to the CH. In order to overcomesuch problems we have centrality based clusterapproach, where node which is at equidistance omall the members of the cluster is elected as ClusterHead. In the proposed algorithm, movement of the

base station is managed in each round so that it canapproach to a specic CH and reduce the CH's loadin the sense of energy consumption. We haveevaluated the effect of energy and number nodes(members) w.r.t. critical degree .which gave us thevariations w.r.t energy and number of nodes asshown in gure-2. We formed the clusters based onLEACH algorithm proposed [3]. Aer organizingnodes into clusters and at the beginning of eachround, the CHs report their status (such as location,

number of nodes ad energy) to the BS via a singlepacket called Status Packet. At this point, BS mustdecide to which CH it should approach. In otherwords, BS must determine the most critical-status CHand then approach to it. Based on the receivedinformation om CHs, the BS is able to determinethe distance to the CHs. Decision making isperformed by a zzy system at the BS. Therefore,we dene a zzy system with following parametersas inputs:1 CH's residual energy: this gives the status ofcluster head. If it has less energy than morepreference is given.

Number of nodes in the cluster: The moremembers in the cluster, the more data processing isrequired and thus the more preference is given tosuch CH.3) Distance om base station to CH: The moredistant the CH is om the base station, the moreenergy is required for data communications. Hencemore preference is given to that CH.We used distance of the CH om base station and

number of nodes in a cluster for determining Criticaldegree because these parameters play imporant roleto consume energy of CHs. The membershipnctions of these parameters are shown in Figures 3

to 5 The output of the zzy system is the CriticalDegree specied to each CH whose membershipnction is depicted in Figure 5 As a result, the morecritical-status CH would be assigned more CriticalDegree.


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, J -_:: __ _M j r" '"

! - ' " r -J , t ' Figur Evalation of ct of nrgy

igure2 valaton of eet o nmber o nodesAer assigning a Critical Degree to each CH, the BS

determines the most critical-status CH and moves

towards it. If two or more CH have the same Critical

Degree, BS randomly select one of them. The

movements of the base station are limited to a

predened step size. Aer cluster formation in each

round and receiving all status packets, the BS

determines its new location and move towards it.

Situating in the position, the BS broadcasts the

location across the network, so that CHs would know

it. Now, CHs can adjust their power control

according to their proximity to the BS and send their

data to it. This is as shown in the gure 6.The zzy

system consists of a fuzzier, zzy rules, fuzzy

inference engine and a dezzier. We have used If

then rules to nd the critical status of the CH. The

membership nctions of CH's remaining

Figure 3 Membership nction of Energy

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Figure 4 Membership function of distance to base station

)2I[-

i: I '

IIFigure 5 membership function of number of members

-" '1LFigure 6membership function of critical degree

,I

energy, distance to base station om CH and numberof nodes in the cluster depend on initial energy, theposition of BS. The maximum range of membershipnctions are determined by maximum values ofinput parameters. Maximum value of Proximity toBS parameter can be derived as below,

MPBS=.xs yJs (2)Where (XBS, YBS) is the pOtOn of BS. Maximumvalues of energy and cluster members are initialenergy and total nodes minus one node as clusterhead respectively. The movement of the base stationis as shown in the gure 7.aer collecting the status

information of the CH's base station will movetowards new location which is the step 2.aer that itis going to broadcast its location to all the CH's.now,the CH's starts processing of the data packets whichis shown as step 4.


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2012 Inteational Conference on Computing, lectronics and lectrical Technologies [ICCT]

Figure 7 Movement of the base station

IV. MULATON ESULTS

In this section, we evaluate the performance ofproposed algorithm in MA TLAB simulator. 100

nodes are randomly distributed in a 100 x 100 mnetwork. Simulation was performed for 7000 rounds.We compare our proposed algorithm with LACH,in networks Lifetime, remaining energy of the

0 \\\00 * !.l\ .

: : I -: :; ;"- ; . L

i Figure 8. Network lifetime

1_.-": \. ; ........................................ . . + . . ....... h ;;........ ,

..............-

Figure 9Energy of the network

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network and variance of energy. Lifetime isconsidered as the time when the rst node dies.Figure 7 shows the number of alive nodes withrespect to the operation of the network in rounds.Approximately the sensors start dying aer 1000thround. Therefore, it improves the overall networklifetime. Residual energy of the network in eachround gives the rate of total energy consumption,which can be a good metric to measure the energyeciency of the algorits. Figure 8 shows thecomparison of network's energ in four algorithms.In the proposed algorithm the residual energy ofnetwork is much more than others. The faiess ofenergy consumption can be well observed bymeasuring the variance of the residual energy of allnodes in each round. The less variant residual energyin each consequent round is the reason of the fairerenergy consumption. Contrary, more variance inenergy consumption shows that the network's load ison some sets of nodes. The more straight line

demonstrates the less variant energy consumption,which is due to the better energy balancing. nergyvarance as shown n the gure 10.

i f . ; ; F'+ ....+ ....rO \ ; ! ; , =

Figure I OVarice of energy

V. ONCLUSONWireless sensors can communicate betweenthemselves by transferring data through them anddecides the head of the cluster group based oncentrality approach. This is the major advantage ofthis technique in the energy conservation. Datatransfer to the base station consumes more energy ofsensors. Mobility of base station can achieve the

conservation of energy. Also prolonging the networklifetime has been of a great interest due to resourcelimitations in wireless sensor networks. Networkswith movable base station have attracted the interestsof researches as an applicable approach to prolongthe lifetime. In this paper, a movable base stationalong with centrality based clustering approach isused. According to the simulation results, it hasshown efciency in the network lifetime and residualenergy of network. The proposed schemes also have


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proved considerable efciency in different scenarios.It has also shown Improvement in performance ofWSN compared to other routing scheme.

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