Development of an Artificial Fish Swarm Algorithm Based Energy Efficient Target Tracking Scheme in Wireless Sensor Networks

Description

– Development of an Artificial Fish Swarm Algorithm Based Energy Efficient Target Tracking Scheme in Wireless Sensor Networks –

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Abstract

Optimal deployment of sensor nodes in other to ensure optimum network coverage is one of the challenging problems faced by Wireless Sensor Network (WSN) researchers due to the complexity and exhaustive nature of WSN. The target tracking problem is concerned with maximizing the lifetime of the network while continuously monitoring a set of targets.

This dissertation presents an optimal deployment of WSN and target tracking scheme using the intelligent swarming behaviors of the Artificial Fish Swarm Algorithm (AFSA). The preying, swarming, and chasing behaviors of AFSA were initially replicated using MATLAB R2013b simulation environment.

The position of network nodes was randomly deployed in a network coverage area of 60 square meters with a total of 60 sensor nodes of 4m radius and communication range of 15m using the replicated AFSA algorithm. Thereafter, the replicated AFSA was used to detect events based on the target discovery probability model.

Introduction

Wireless Sensor Networks (WSNs) are increasingly being used for collecting data, such as physical and environmental properties, from a geographical region of interest due to advancements in electronics and wireless communication technology.

WSNs are composed of a large number of tiny, low-power, low-cost sensor nodes which have the ability to sense physical phenomena, process data, and communicate with one another (Alikhani, 2010).

A sensor node is a tiny device that includes four basic components: a sensing subsystem for data acquisition from the physical surrounding environment, a processing subsystem for local data processing and storage, a wireless communication subsystem for data transmission, and a power supply subsystem, which are batteries.

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