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JOURNAL OF COMPUTING, VOLUME 3, ISSUE 5, MAY 2011, ISSN 2151-9617HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING/ WWW.JOURNALOFCOMPUTING.ORG 145

Deployment of sensor nodes and Optimization of Energy in Wireless Sensor Network using Voronoi

Diagram.Ms.Mitali R. Ingle, Ms.Sonali Nimbhorkar

Abstract —Wireless Sensor Network is a group of low-cost, low-power, multifunctional and small size wireless sensor nodes that work together to sense the environment, perform simple data processing and communicate wirelessly over a short distance. Some of these sensornodes are able to move on their own. With the ability to move independently, these mobile sensors are able to self deploy and self repair, thusadding more to their value. The energy constraint sensor nodes in sensors networks operate on limited batteries, so it is a very importantissue to use energy efficiently and reduce power consumption. The aim is to develop a system that deals with the Deployment of sensorsnodes efficiently so that energy consumption is minimum with maximum data transfer in minimum hopping using the concept of VoronoiDiagram

Index Terms — sensor networks, node deployment, voronoi diagram, energy efficiency.

—————————— ——————————

1 I NTRODUCTION

wireless sensor network consists of light-weight,low power, small size of sensor nodes distributedover an area to collect information. The areas of ap-

plications of sensor networks vary from military, civil,healthcare, and environmental to commercial.

Examples of application include forest fire detec-tion, inventory control, energy management, surveillanceand reconnaissance, and so on. Due to the low-cost ofthese nodes, the deployment can be in order of magni-tude of thousands to million nodes. The nodes can be de-ployed either in random fashion or a pre-engineered way.The sensor nodes perform desired measurements, processthe measured data and transmit it to a base station, com-monly referred to as the sink node, over a wireless chan-nel. The base station Collects data from all the nodes, andanalyses this data to draw conclusions about the activityin the area of interest. Sinks can act as gateways to othernetworks, as a powerful data processor or as access pointsfor human interface. They are often used to disseminatecontrol information or to extract data from the network.Nodes in sensor networks have restricted storage, compu-tational and energy resources; these restrictions place a

limit on the types of deployable routing mechanisms.

A sensor’s prime function is to sense the environment forany occurrence of the event of interest. Therefore cover-age is one of the major concerns in WSN. In fact it is a key

for evaluating the quality of service (QoS) in WSN.Coverage can be classified into three classes; area cover-age, point coverage and barrier coverage. Area coverage,as the name suggest is on how to cover an area with thesensors, while point coverage deals with coverage for aset of points of interest. Decreasing the probability of un-detected penetration is the main issue in barrier coverage.Most of the works discuss in this paper deal with areacoverage where the objective is to maximize the coveragepercentage; ratio of area covered by at least one sensor tothe total area of the region of interest (ROI).Coverage problem in WSN basically is caused by threemain reasons; not enough sensors to cover the whole ROI,limited sensing range and random deployment.

Different strategies have been proposed in theliterature for the WSN coverage optimization. grid cover-age strategy for effective surveillance and target position-ing using integer linear programming (ILP). The sensorfield is represented as grid. With the sensors placed at thegrid points, a target can be located easily at any time.However, grid based deployment strategies require thesensors to be placed exactly and accurately at the grid

points. Therefore, this method is subject to errors such asmisalignment and random misplacement. Other ap-proaches for optimizing the coverage in WSN are virtualfield; It is assumed that the sensor nodes and obstacleshave potential fields which exert virtual forces. The nodesrepel each other until either their sensing fields no longeroverlap or they cannot detect each other. This systemaims to present a use of Voronoi diagrams based solu-tions for sensor network coverage determination and op-timization of energy.

———————————————— Ms.Mitali R. Ingle is student of 4 th Sem M.E (Wireless Communication &

Computing) at G.H.Raisoni College of Engineering, Nagpur, India.

Ms.Sonali Nimbhorkar is Assistant Professor at CSE Department,G.H.Raisoni College of Engineering, Nagpur, India.

A




2 V ORONOI D IAGRAM Voronoi diagram is a partition of sites in such a way thata points inside a polygon are closer to the site inside thepolygon than any other sites, thus one of the vertices ofthe polygon is the farthest point of the polygon to the siteinside it. Voronoi diagram can be used as a samplingmethod in determining coverage of WSN; with the sen-

sors act as the sites. If all Voronoi polygons vertices arecovered, then the ROI is fully covered. Let S = {p1, p2. . .pi, . . . , pn} be a set of points in a two-dimensional Eu-clidean plane. These points are called sites. A Voronoidiagram decomposes the space into regions around eachsite, such that all points in the region around pi are closerto pi than any other point in S.The Voronoi region V (pi) for each pi consists of all

points that are closer to pi than any other site. The set ofall sites form the Voronoi Diagram V (S).

2.1 Properties of Voronoi Diagram A Voronoi edge between two Voronoi regions R

(pi) and R (pj) is a portion of the perpendicularbisector of the line segment connecting the twogenerators pi and pj.

A Voronoi vertex is the centre of the circle thatpasses through the three generators, whose re-gions are incident to the vertex, i.e., it is the cir-cumcenter of the triangle with those generatorsas the vertices.

A Voronoi region R (pi) is a convex (possiblyunbounded) polygon containing the correspond-ing generator pi.

3. IMPLEMENTATIONThe system for Deployment of sensor nodes and

Optimization of Energy include: Maximum Coverage Area in Minimum Hopping. To increase the Life Time of sensor nodes. To Optimize Energy Requirement.

The first phase of the system is dealing with the deploy-ment of sensor nodes using voronoi diagram. For this thesystem considers only 20 sensor nodes at initial stage toview the behaviour of the system. The number of nodescan be increased but a particular threshold values is to bedecided.

Figure 1 Area to be monitored

In the figure 1 the area which is to be monitored is shown.

The simulation result shows the area consisting of 20 sen ‐

sor nodes deployed using the voronoi diagram; here the

voronoi diagram is partially implemented as there are

some sensor nodes which are in sleep mode. The basic of

voronoi diagram implementation is to find the area each

node is responsible for, so after implementing the voronoi

diagram some of the sensor nodes are in sleep mode,this

sensor nodes which are in sleep mode are activated if any

of the sensor node is dead or Whose energy is totally

consumed.

Figure 2 Diagram showing the nodes which are in sleep

mode.

In the figure 2 the sensor nodes which are in sleep mode

is shown the nodes number 9,19,21,6,15,18,16,14,4,0 are in

sleep mode,this nodes will be activated if any node in the

network is dead or whose energy is totally consumed.

Figure 3 Diagram showing the nodes which are dead.

In further simulation time, some nodes in the

monitored area become dead nodes as there energy is

totally consumed in the data transmission. The nodes

numbers 6,15,19,9,21,16 are dead nodes. Now in next

phase of development the dead nodes are to be removed

and the voronoi diagram is needed to be updated. The

energy comparison is to be done for the implementation

till now, for this graph of Energy vs. simulation time is

traced.




Figure 4 Diagram showing the Energy consumption.

The above graph is traced from the trace file

which shows that there are still energy losses in the sys ‐

tem as the voronoi diagram is not totally implemented.

As there are dead nodes in the system so now the most

important aim to update the voronoi diagram. After the

voronoi diagram is updated the energy losses will be re ‐

moved and energy efficient deployment will be achived.

After the voronoi diagram is updated the energy

loss which was there is removed and we get two different

graph. One graph is traced with some nodes in sleep

modes and another graph is obtained by activating the

nodes in the sleep mode.The energy loss are removed to

much extent in the graph with nodes activated in sleep

modes.

Figure 5 Diagram showing Throughput of generatingpackets vs. simulation time

Figure 6 Diagram showing Throughput of generating

packets vs. simulation time.

After this energy comparision the various parameters are

also considered.The parameters which are considered are

as follows Throughput of generating packets

vs.simulation time, Throughput of sending packets vs.

simulation time, Throughput of sending bits vs. End to

End delay,Number of backup nodes vs. density.

Figure 7 Diagram showing Throughput of generat-ing packets vs. simulation time.




Figure 8 Diagram showing Throughput of sending

packets vs. simulation time

Figure

9

Diagram

showing

Throughput

of

sending

bits

vs. End to End Delay.

Figure 10 Diagram showing Number of backup nodes vs.

density

The above result shows that with the implementation ofvoronoi diagram that is with scheduling gives much im-proved result as compared to the result without schedu-leing.The performance of system is improved with theimplementation of the voronoi diagram.

4 C ONCLUSION This system is implemented using Voronoi dia ‐

gram.The Voronoi Diagram based solutions for sensor

network coverage determination and optimization of en ‐

ergy and analyses the potential. The Voronoi Diagram

based solution are obtained by scheduling of sensor

nodes which are improved as compared to the solutions

obtained by without scheduling of sensor nodes.

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Ms.Mitali R. Ingle is student of 4 th Sem M.E (Wireless Communication &Computing) at G.H.Raisoni College of Engineering, Nagpur, India.

Ms.Sonali Nimbhorkar is Assistant Professor at CSE Deaprtment,G.H.Raisoni College of Engineering, Nagpur, India.

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