Multi-sink Optimal Repositioning for Energy and Power Optimization in Wireless Sensor Networks

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Multi-sink Optimal Repositioning for Energy and PowerOptimization in Wireless Sensor Networks

S. Yasotha 1 • V. Gopalakrishnan 2 •

M. Mohankumar 1

Published online: 26 May 2015 Springer Science+Business Media New York 2015

Abstract A wireless sensor network (WSN) plays a major role in many recent appli-cations now such as surveillance and security, target tracking, agriculture, health andmilitary purposes. The main problem with WSN is the energy resource for long lastinglifetime. Therefore an efcient methodology is to be implemented for improving theenergy level of WSN. Also some efforts have focused on the mobility of a single ormultiple sink nodes. The mobility of the sink node introduces a tradeoff between the need

for frequent re-routing to optimize the performance and the minimization of the overheadresulting from this topology management. In this we propose a novel approach to increasethe lifetime of a sensor network based on the mobility, static sink repositioning andmultiplicity of sinks. Optimal sink position also identied using optimal search conceptsand multipath routing in large scale sensor networks with multiple sink nodes for energy isimplemented for entire WSN. Based on the evolution of network in terms of energydissipation and distribution this approach reaches to nd the optimal position for all thesinks in order to optimize the lifetime of the network and move according with intelligentsink positioning. The simulation result shows the efciency of our approach in terms of energy gain.

Keywords Wireless sensor networks Multiple sinks mobility Energy efciency Sink relocation Optimal repositioning

& S. [email protected]

1 Department of Information and Communication Engineering, Anna University of Technology,Coimbatore, Coimbatore, India

2 Department of Electrical and Electronics Engineering, Government College of Technology,Coimbatore, Coimbatore, India

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Wireless Pers Commun (2016) 87:335–348DOI 10.1007/s11277-015-2642-5

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1 Introduction

A large number of electro mechanical devices such as relays with sensing unit embeddedwith computing and communication capabilities of included in wireless sensor networks

(WSN). A sensing unit is an electro mechanic device which generally collects three mainparameters: collecting the data relative to the environment surrounded by it, ability tomeasure the data and to exchange it with the neighboring devices. The neighboring devicesmay be sensor nodes or sink nodes. A sink is a particular node that collects the informationor data resulting from the sensing nodes present in the network, which has ability toprocess them and/or send them to data concentration center. Generally sensor nodes delivertheir information to the nearest sink node. If the amount of data which needs to betransmitted is reduced then the energy consumption of the network also is reduced [ 1]. It isnecessary to consider in WSN architecture, the network topology, power consumption,data rate and fault tolerance for avoiding the energy consumption and for improving thebandwidth utilization [ 2]. The sink repositioning includes a moving node which has theability to move around to collect data from sensor nodes. Sink repositioning can be madewith the following methods.

1.1 Multiple Sink Deployment

Always the collected information by the sensing node will be shared with the nearest sink,deploying multiple sinks may decrease the average number of hops a message has to passthrough.

1.2 Sink Mobility

The sink moves fast enough to deliver data with a tolerable delay with the mechanicalmovements, the mobile sink picks up data from nodes and transports the data. Thereforefor the reduction of energy consumption of nodes, this approach trades data deliverylatency.

1.3 Deploying Multiple Mobile Sinks

The multiple sinks are deployed for collecting sensor data without delay and withoutcausing buffer overow. Our aim is to propose a multiple sinks relocation solution fornetwork lifetime optimization and power optimization by moving the sink nodes towardstheir optimal positions with an intelligent manner.

All sensor networks data’s are routed towards by a single sink, the gateway in ourmodel, hops close to that sink become heavily involved in packet forwarding and thus theirenergy resource gets depleted rather quickly. The hops that are further away from thegateway have to be used as substitutes. It increases the total transmission power and itgradually limits sensor coverage in the WSN and often it makes the network useless. If the

sink node has limited motion capability it will be necessary to relocate the sink close to anarea of heavy trafc or near loaded hops in order to decrease total transmission power andextend the life of nodes on the path of heavy packet trafc and also it will reduce theaverage delay per packet.

The challenging scenario in WSN is repositioning the sink node during regular network operation. By making the sink node to be relocated when it would make sense, where the

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sink node should go and how it will be moved. The relocation of the gateway rst has to bemotivated by odd pattern of energy depletion or data route setup, even if it is the mostefcient network operation given the trafc distribution and network state at that time. Thesink node detects the optimal location to place the sink which has the odd pattern energy

depletion in order to improve the network performance. While moving the sink nodeensures that there is no data loss during that time.

The sink node achieves a trade-off analysis between the gain achieved by going to anew place and the overhead in terms of additional energy consumption that the relocationimposes on sensors. If the relocation is justied, the sink node moves towards that location.

2 Related Works

The purpose is to determine the optimal multiple sink relocation to enhance the energy of the WSN by minimizing the energy utilization in multiple sink nodes. In many applicationslike inside the buildings the nodes are deployed in WSN to collect the information in thatarea, they used integer linear program (ILP) for multiple sinks which increases the WSNlifetime instead of reducing the energy consumption [ 3]. In that mobile sinks are movedrandomly and sinks are moved separately in different clusters. WSN consist of collectionof nodes which collects the information like pressure, humidity and temperature etc … tocollect these information the nodes are used in the network area, collected information willbe send to the destination through sink node/base station. Sink node energy drains fastercompare to other nodes present in the network because sink node will act as a gateway to

other nodes. In paper [ 4] they have implemented a hybrid sink repositioning techniquewhich indirectly increases the network lifetime, in that the considered sink node as a staticnode which collects urgent data in that network area and mobile sink nodes are used tocollect the non-urgent data which moves randomly and periodically collects the data’s inthe network. Repositioning the sink node gives the greatest achievement in the network lifetime for a longer period by reducing the energy consumed for sending data. Thisreplaces the deployment of multiple sink and multi hop transmission. While placing thesink node in the network the energy balanced is a major problem. Here the node whichdeployed nearest to a sink node which drains the energy faster compare to other nodes innetwork [ 5, 6]. Akkaya [ 7] has scrutinized the potential of sink relocating for improvementof network performance in case of energy, throughput and delay. They faced a problemthat how to handle the data trafc without any loss of data while moving the sink nodefrom one location to another location. They identied a solution that using relay node foreach sink node for optimized energy and suitability. In this paper [ 8], they have consideredreposition of the sink/base station by checking the trafc ow of the networks node that isone hop away from the sink and the distance from the sink. Once the total transmissionpower for node is guaranteed then it is compared with the threshold value and the overheadof moving sink is decided, then sink starts move to the new position. Using routing strategythe sink is moved so the data will not be affected. In [ 9] Seino, proposed sink node travel in

xed route in sensing area and collect the data from sensors and stored on the base station.Communication trafc can be reduced by delivering the predicated sensor data. In thismethod the mobile sink node broadcast the predicated value to the sensor and only thatsensor can send the data which exceeds the admissible error margin.

Comparison of sink node The static sink node, mobile sink node and multiple sink nodesare specied [ 10]. Static sink node was used for data collection in WSN by using multi hop

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transmission so sink node consumes more energy around by node base station and passesthe data from other one. Mobile sink node was used to collect data from sensors node andstored it to the base station. If only one sink node is used for the entire network in case thatparticular node gets failure then it affects the entire network. Multiple sink nodes were

used in data collection process from the sensors and stored collected data on the basestation in the database. If multiple sink nodes is used in the network then it will reduces thenetwork failure.

In this paper [ 11 ] technique the adaptive data approximation algorithm should be self-adaptive to the changes of the sensor readings timely as sensor readings change slowlyaccording to the change of physical phenomena. This algorithm consists of two parts; dataapproximation learning algorithm and data approximation monitoring algorithm. In [ 12]proposed schemes block partition is used and then each block selects a sensor node as localsink for the data collection of the local area. In [ 13] proposed Mobicluster protocol forsensor node under the assumption of SNs are location aware. The protocol consist of vephases (1) clustering approach is used for WSN, (2) cluster head (CHs) attachment to RNs,(3) rendezvous nodes (RNs), (4) data aggregation and forwarding to the RNs, (5) com-munication between RNs and mobile sinks.

2.1 Movement of the Sink

The movement of the sink node will be decided based on the trafc ow in network area. Itchecks for the nearest hop to take a move to new location in order to reduce the energyconsumption in sink to achieve the overall performance of the WSN.

The following notations are used for sink relocation:G: Gateway in a clusterG1 : Set of sensors less than distance D from GGR : Set of sensors that are one hop away on the active routeGR1 : Set of sensors in G R which are also in G 1 , i.e. G R1 = G1 \ GRG1

new : Set of sensors less than distance D away from the gateway at the new locationGR

new : Set of sensors those are one hop away on new route at the new locationGR2 : Set of sensors in G R new which are also in G1new, i.e. G R2 = G1

new \ GRnew

Pi: Packet trafc measured as the number of packets per frame, going through a

nodePT: Set consisting of packet trafc of each sensor in G R1 in an ascending orderPT new : Set consisting of packet trafc of each sensor in G R2 in an ascending orderE(Tri):

Energy consumed by a node i in transmission of a packet to the next hop

Note that G1new , G R

new , G R2 and PT new are calculated by locating the sink node at new

location. The basic idea is for the sink node to check the changes in data route in con-secutive routing cycles using routing algorithm. Typically re-routing is performed in re-sponse to a high packet loss caused by the energy depletion or failure of a relay node or is

triggered by a change in data sources that requires setting a new topology [ 14]. Whencomparing routes in two consecutive cycles, if the data sources are the same and the nodesin previous G R1 differs from that the current G R1 , the gateway perform further analysis.The gateway checks the nodes that used to be in the previous G R1 and are excluded fromthe new G R1 . If these nodes were among the bottom 70 % of the set PT , relocation wouldnot be necessary. On the other hand if these nodes were forwarding high trafc (among top30 % in PT ), the gateway perform a heuristic search for a better position.


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To qualify the impact of locating the sink node at a new position, some static network performance parameter has to be used. Most popular performance parameter for sensornetworks, such as average delay per packet, data transmission rate and throughput, arebased on the network operation over time period and are typically network-wide in

scope. The parameter which is considered here is the total transmission energy of therelay node that is one-hop away from the sink node, basically those in G R and G R

new . Apositive effect on the total transmission energy for these nodes would be a good indi-cation for the sink node move from one place to another. The reduction in the totaltransmission energy has to exceed an application threshold to justify the overhead. Aconstant d can be derived based on the overhead for handling the movement of both atthe gateway and at the network level. The condition for relocation can be mathematicallydened as:

X E ðTRiÞ8i2 GR

Pi X E ðTR jÞ8 j2 G Rnew

Pj [ d ð1Þ

Determining an optimal new position for the sink node is a hard issue for the relo-cation decision. Optimal positioning of the sink node is an NP-hard problem. Thereforewe purse a heuristic search and settle for a quasi-optimal location to overcome suchcomplexity.

2.2 Intelligent Positioning for a Sink Node

Once the gateway movement is identied, then a difcult problem is to nd the direction of

move for G (sink) could be based on the network trafc. The node G is moved towards thesensors that produces the maximum number of packets. However, it may be infeasible forG to move far from its current position. There is a risk for wasting substantial resources forreaching a far position, which turns shortly after to be not optimal due to changes in theenvironment when the network topology dynamically changes. To achieve the largestnumber of packets G is placed close to the relay nodes in G R .

In our method, G is to move towards the most dominating node in PT and it shouldgive a greater value of d . In case of multiple relay sensors in network, a weightedaverage based on the distance between the sink node and these relay sensors and theirtrafc load is taken into account. The idea is to balance the sink node while directing theposition for the sink.

A position ‘‘g’’ central, in terms of distance 9 trafc density, different routes is de-termined from the relay sensors in high trafc area. While performing this repositioningwill be ideal for high trafc way, it can worsen the total power consumed on other routewith lower trafc load. Therefore, the sink node will be located on some point on thestraight line between its current position and the newly determined position ‘‘g’’ that isintermediate from picked relay nodes. How to choose the direction of the sink node motionto optimize total transmission energy for the top two relay nodes in PT . Wang et al. [ 15]and Dandekar and Deshmukh [ 16] refers the optimal search with multiple hop transmis-

sion. Balancing the interest of the relay node A and B changes the slope of the sink nodetravel path. Placing the sink node at position g on the line between A and B increases theefciency of packet transmissions from A and B which indirectly proposition to the energyefcient of entire network area, to the sink node at the expense of node C . Therefore alocation on the route to g that decreases the total transmission energy for the entire G R setwill be looked for using a bisection search.


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2.3 Handling Gateway Motion

One the position for a new location for a sink node is identied, surely this will improvesthe overall performance of WSN, and then next vital role is moving a sink node to a new

position without any data loss. In order to face this problem, during routing phase the sink node to be moved to a new position. Routing phase has two parts (1) data transfer phase,(2) routing phase

Data transfer phase In this phase using routing table concept the data packets are sendsto sink node from sensing node.

Routing phase The sensors send their information to their sink node and based on suchinformation multi-hop routes to the sink node are created and routing table information issent back to the sensor nodes. In this phase there is no data transmission takes place so therelocation can be done easily by modifying the location of the sink node followed by themodication of network topology at the new location without any packet loss or data loss.

3 Proposed Work

Multi-sink repositioning consists in nding the best sinks positions within the network during the network lifetime. The sink movement facility is possible which allows us tomove the sinks in an efcient way towards the positions which enhance the network lifetime.

3.1 Routing

Before realizing the routing identity, the rst phase consists in providing each link in thenetwork a specic weight. This weight depends on the energy of the destination node, inorder to relay the information by the nodes having the higher residual energy, and thedistance between nodes, in order to prefer short distance transmissions in Fig. 1.

Once this weight is calculated, we obtain a graph on which Dijkstra algorithm can beapplied to obtain the shortest path between a sink and each node in the network. Sincemore than one sink node is present in the network then the aim is to nd the shortest pathtowards the nearest sink.

Fig. 1 Routing in a sensor network


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The algorithm complexity is acceptable since we achieve p Dijkstra in O(n). The nextproblem is to nd the weight of the links.

3.2 Calculation of Links Weight

The energy consumed for the communication is dened by the distance between the nodesi and j. Since the required energy is proportional to distance squared, the wait will show thesame behavior. The lowest is the destination node energy and the highest is the link weight.

The link weight is dened as follows:

W i; jð Þ ¼ CF 0 dist i ; jð ÞexpCF 0 þ CF 1 1

energy ð jÞ ð2Þ

where,

• W (i,j ) is the weight of a link between nodes i and j• dist (i,j ) is the energy consumed by the communication between nodes i and j• energy ( j) is the remaining energy in node j• CF 0 , CF 1 , and expCF 0 are coefcients for equation balancing

The energy model is updated by decreasing the energy of the whole nodes which con-tributed to the packet relaying. When a packet arrives to a sink by implementing, only thetransmission energy consumption has been considered. The routing protocol is divided inseveral periods. The major periods are data transferring phases and routing phases.Regularly, a routing phase is activated in order to detect if a better conguration of the

sinks is possible and take an intelligent decision for sinks arrangement.

3.3 Optimum Search

The optimal multi-sinks positioning problem in a network is NP-complete [ 17]. Here theproblem faced is an innite space of solutions since a sink position is dened by a coupleof real numbers. This approach can be implemented using integer numbers space. Even if we admit such an approach in an integer numbers space, in a 100 9 100 points space, wewill have 10 12 solutions for a 3 sinks placement.

Fig. 2 a , b Initial network and associated routing; c, d one sink movement and associated routing; e, f twosinks movement and associated routing


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A solution neighborhood and relocations The aim of the local search approach is asfollows: from an initial solution x0 , a nite series of solutions xi is generated with a sys-tematic change of neighborhood. xi ? 1 is derived from xi such that for all i, f ( xi ) [ f ( xi ? 1 ). f is the evaluation function of the solution.

To choose a neighborhood and relocation 3 levels are implemented:• One sink movement: this movement consists in one sink relocation in respect to the

initial position. This movement is performed in eight directions: North (N), South(S),East (E), West (W), N–E, N–W, SE, S–W.

• Two sinks movement: this level consists in a two sinks simultaneous transform towiden the neighborhood of a solution and avoid certain deadlock situations. We limitedthe transformations to the fourth cardinal points.

• Three sinks movement: three sinks are relocated simultaneously.

To illustrate the relevance of the two sinks movement, Fig. 2 shows a theoretical caseproved by simulation where a single sink movement does not lead to a better solution. Thesimultaneous movement of two sinks allows getting a better solution.

1. Constrained local search The aim of the constrained local search is to limit the sinksmotion while maintaining their path way to the optimal positions. Based on both thecurrent and the optimal locations of the sink the liberty is dened. The next move willtake place in this constrained space.

Let’s d be the liberty distance [ 18]. We also dene G, the point located at a distanced from the current position of the sink on the line formed by the current position Cp and theoptimal position of the sink. The liberty space is then dened as the set of points located ata distance smaller than d from the point G and the current position (see Fig. 3).Graphically, this point is the intersection between two discs with G and Cp as centers andd as radius.

In Fig. 4, the intelligent movement is shown based on the constrained space to achievethe optimal solution.

3.4 MRMS Algorithm Overview

Multipath routing in large scale sensor networks with multiple sink nodes (MRMS) consist

of three phases: topology discovery, cluster maintenance and path switching.MRMS topology discovery is derived from the three-color algorithm, but with a number

of signicant differences. In paper [ 19] initially, MRMS must save the routes from dif-ferent sinks, so that when the primary path is not reachable another path is selected based

Fig. 3 Liberty spaces delimitation


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on the residuals energy. Secondly, based on the cost metric it can construct an optimal orsub-optimal path to any sink node (i.e.) construct construction. Finally each cluster can beconsidered as a single node.

There are two major processes within cluster maintenance: energy monitoring andcluster reconstruction. The residual energy of the each node is monitored and the levelreaches below threshold, cluster reconstruction is started. In cluster reconstruction, if theCH residual energy is below some threshold, it will select a new child which has themaximum residual energy. On the other hand, if the delivery node’s residual energy isbelow the threshold, the CH will choose the new destination node based on the path cost.Next, the main phase, path switching, is to switch path to another sink when the primary.After a primary path has been in use for an extended period of time, the energy level of these sensors along with this path will dissipate quicker than other sensor nodes, and few

nodes may run out of energy in total leaving the path unusable. By switching paths, energyconsumption is distributed more evenhandedly. In [ 20] the size and structure of eachcluster follow certain rules so that each CH can transmit data to sink over a period of timewhich does not overlap with each other and these time periods are continuous.

4 Simulation Results

4.1 Simulation Model

The implementation of the sensor network we proposed was realized by simulation withriverbed software (OPNET). A rst series of simulations was dedicated to validate themodel. Our aim was to make sure that the network behaves according to the theoreticalmodel and operates on some easily veriable congurations. 100 9 100 square meter areanodes and sinks are randomly placed for sensor networks. Except for testing speciccapabilities of our approach, the sinks and node positions are determined randomly within

Fig. 4 a Intelligent movement in the constraint space; b the sink in the right side is blocked


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the area boundaries. Each node is assumed to have an initial energy of 5 joules and isconsidered non-functional if its energy level reaches 0. The experiment was carried out forvarious numbers of sinks to evaluate the performance for intelligent movement of sink node.

4.2 Performances Metrics

We used the following metrics to evaluate the performance of our multi-sink repositioningapproach and to compare it with the motionless approach (Table 1):

• Time for rst node to die• Number of delivered packets and lost packets• Average delay per packet• Average energy consumed per packet

4.3 Environment Validation

The results of an experiment, shown in Fig. 5, illustrate this principle and example, whenthe energy of the node C is equal to 5 Joules, the routing algorithm chooses C to route theinformation from A to the sink. The node B is chosen as relay when the energy of C is 1.5Joules.

Finally, we checked that the optimal location of each sink is selected so as to minimizethe sum of the distances crossed by the sinks and thus, reduce the delay necessary to reach

these positions. This was done by testing each possible permutation of the sinks.

Table 1 Simulation parametersNetwork size 100 9 100 m

Physical channel 802.15.4–2.4 GHz

Coverage area 30 m

packet size 97 bytes

Number of nodes 46

Data rate 250 kbps

Bandwidth 2.4 GHz

Fig. 5 Energy, routing cost and energy-aware routing


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4.4 Performance Results

The main performance results derived from simulation model. Figure 6 shows the energydissipation with static sink and mobile is shown, comparisons was made between both the

mobile node and static node but that is not shown here. The mobility rate is dened bywhen the sinks move towards the data generating zones, and since these data generatingnodes are relatively stable, we observe a decrease in the consumed energy. Finally, theefciency of the approach a vary with more than the data production of zones are stable,the efciency here is an important criteria however, in such case the performance isobtained by choosing high value for the coefcient which is taken into account the ow inthe network (Figs. 7, 8, 9).

1. Straight line movement versus smart movement: a major part of our work wasdedicated to the smart location of the sinks by dening a restricted area of freedom to

move the nodes.

Fig. 6 Energy dissipation with and without mobility

Fig. 7 Nodes lifetime and packet transfer results [ Y axis represents Time (s)/number]


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5 Conclusions and Future Works

In this paper, we proposed a new approach for static sinks repositioning and nding theoptimal multi sink location in a sensor networks. The static nodes for collecting urgentdata’s and mobile sink for colleting un-urgent data’s in the network area. This approach isbased on previous works related to two elds: multiple sinks positioning and the uniquesink relocation. Our approach has the advantage when relocating the sinks to their optimalpositions for the entire network. In the high trafc are the optimal solution will be used in

effective way. Moving the sinks towards the heavy trafc (in terms of information pro-duction), allows to obtain a power saving provided that a stability of these areas exists.Repositioning of the static sink will be implemented in routing phase to avoid the packetloss. Finding the optimal solution will enhance the energy efciency of the nodes and itindirectly increases the network lifetime. Our result shows the average energy efciency

Fig. 8 The average energy consumed per packet

Fig. 9 Average energy consumed per packet for both approaches


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with mobility and without mobility using riverbed (OPNET) simulator. In future themetrics can be focused on minimizing delay and area.

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http://www.iariajournals.org/intelligent_systemshttp://www.iariajournals.org/intelligent_systemshttp://dx.doi.org/10.1109/IAdCC.2013.6514260http://dx.doi.org/10.1109/IAdCC.2013.6514260http://www.iariajournals.org/intelligent_systemshttp://www.iariajournals.org/intelligent_systems


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Mrs. S. Yasotha pursing the Ph.D. degree in Information and Com-munication Engineering from Anna University of Technology,Coimbatore, Tamil Nadu, India, Received the M.E. degree in Infor-mation and Communication Engineering Anna University of Tech-nology, Coimbatore, India, in 2010; currently I am working as an

Assistant Professor, Sri Eshwar College of Engineering, Department of Computer Science and Engineering, Coimbatore. I had published 2papers in National Journals and she presented more than 6 papers inNational and International Conferences. My research interests includewireless sensor networks, networks security, VLSI design, mobilenetworks, data mining, adhoc sensor networks and android.

Dr. V. Gopalakrishnan received the Ph.D. degree in Information andCommunication Engineering from Anna University, Chennai, TamilNadu, India, Received the M.E. degree in Computer Science andEngineering from Government College of Technology, Coimbatore,India; He presented more than 20 papers in National and InternationalConferences and also he published more than 10 papers in Nationaland International Journals. His research interests include power systemprotection, high voltage engineering, computer network, wirelesssensor networks, renewable energy resources.

Mr. M. Mohankumar pursing the Ph.D. degree in Information andCommunication Engineering from Anna University of Technology,Coimbatore, Tamil Nadu, India, Received the M.E. degree in Infor-mation and Communication Engineering from Anna University of Technology, Coimbatore, India, in 2010; currently he is working as anAssistant Professor for Sri Eshwar College of Engineering, Departmentof Electronics and Communication Engineering, Coimbatore. He hadpresented more than 8 papers in National and International Confer-ences and also published 3 papers in National Journals. His researchinterests include wireless sensor networks, VLSI design, image pro-cessing, control systems, power electronics drives and circuits.


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Multi-sink Optimal Repositioning for Energy and Power Optimization in Wireless Sensor Networks

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