Optimization of Energy Consumption in Wireless Sensor Networks using Particle Swarm Optimization

Abstract

ABSTRACT The present work is to optimize energy consumption in wireless sensor networks for multi-layer medium access control (ML-MAC) scheme where the delay times, i.e., maximum response time delay, packet transmission delay, clock-drift delay, sleep delay and queuing delay are considered. Particle swarm optimization (PSO) is a popular multidimensional optimization procedure which is used in this paper to achieve less power consumption in wireless sensor networks. Keywords Energy consumption, Medium access control, Wireless sensor networks, Particle swarm optimization. 1. INTRODUCTION A wireless sensor network (WSN) is a collection of large number of sensor nodes that are deployed in an ad-hoc manner and communicate using a short-range radio channel. Sensor nodes are battery operated and normally they cannot be recharged due to its deployment in harsh and remote environment. Therefore, energy efficiency is a very critical issue to enhance the network lifetime [1,2,3]. ML-MAC is a distributed contention-based MAC algorithm where nodes discover their neighbors based on their radio signal level Step 2:[4-8]. Also, ML-MAC is a self-organizing MAC algorithm that does not require a central node to control the operation of the nodes. ML-MAC saves energy to re-send the falsified packets. ML-MAC is a contention based algorithms, which use an active/sleep routine in frame to save energy consumption. There are four major sources of energy waste [1]: collision, overhearing, control packet overhead and idle listening. The frame length T