%0 Journal Article
%T Sensor Node Placement in Wireless Sensor Network Based on Territorial Predator Scent Marking Algorithm
%A Husna Zainol Abidin
%A Norashidah Md. Din
%J ISRN Sensor Networks
%D 2013
%R 10.1155/2013/170809
%X Optimum sensor node placement in a monitored area is needed for cost-effective deployment. The positions of sensor nodes must be able to provide maximum coverage with longer lifetimes. This paper proposed a sensor node placement technique that utilizes a new biologically inspired optimization technique that imitates the behaviour of territorial predators in marking their territories with their odours, known as territorial predator scent marking algorithm (TPSMA). The TPSMA deployed in this paper uses the maximum coverage objective function. A performance study has been carried out by comparing the performance of the proposed technique with the minimax and lexicographic minimax (lexmin) sensor node placement schemes in terms of coverage ratio and uniformity. Uniformity is a performance metric that can be used to estimate a WSN lifetime. Simulation results show that the WSN deployed with the proposed sensor node placement scheme outperforms the other two schemes with larger coverage ratio and is expected to provide as long lifetime as possible. 1. Introduction One of the ways in provisioning maximum coverage with longer lifetime is through the use of some sort of wireless sensor network (WSN) deployment mechanism. This can be done by utilizing an effective planning mechanism in arranging the limited number of sensor nodes. WSN for target monitoring applications such as landslide monitoring, forest fire detection, and precision agriculture can be implemented with a fixed number of sensor nodes that are deployed to monitor one or more locations within a monitored area. For cost-effective deployment, it is critically important to determine optimal locations for these sensor nodes. The locations of the sensor nodes strongly affect the energy consumption, operational lifetime, and sensing coverage [1]. Thus, careful sensor node placement is needed. Romoozi et al. [2] stated that there is a tradeoff between energy consumption of sensor nodes and network coverage. Closer sensor nodes will reduce the energy consumption but the network coverage will become smaller. This scenario has attracted numerous research works on WSN sensor node deployment. Ingle and Bawane in [3] utilized a Voronoi diagram [4] as shown in Figure 1 in their technique known as Node Network Voronoi (NNV) and Edge Network Voronoi (ENV) as a solution for WSN coverage determination and optimization of energy. In Voronoi diagram, a partition of sensor nodes that are points inside a polygon are closer to the sensor node inside the polygon than any other sensor nodes. Thus, one of the polygon
%U http://www.hindawi.com/journals/isrn.sensor.networks/2013/170809/