Particle swarm optimization technique & optimization for reducing energy consumption in WSN by PSO

Abstract

A number of basic variations have been developed due to improve speed of convergence and quality of
solution found by the PSO. On the other hand, basic PSO is more appropriate to process static, simple optimization
problem. Modification PSO is developed for solving the basic PSO problem. The observation and review focusing on
function of PSO, advantages and disadvantages of PSO, the basic variant of PSO, Modification of PSO and applications
that have implemented using PSO. The application can show which one the modified or variant PSO that haven’t been
made and which one the modified or variant PSO that will be developed. Wireless sensor networks (WSN) is composed
of a large number of small nodes with limited functionality. The most important issue in this type of networks is energy
constraints. In this area several researches have been done from which clustering is one of the most effective solutions.
The goal of clustering is to divide network into sections each of which has a cluster head (CH). The task of cluster heads
collection, data aggregation and transmission to the base station is undertaken. In this paper, we introduce a new
approach for clustering sensor networks based on Particle Swarm Optimization (PSO) algorithm using the optimal fitness
function, which aims to extend network lifetime. The parameters used in this algorithm are residual energy density, the
distance from the base station, intra-cluster distance from the cluster head. Simulation results show that the proposed
method is more effective compared to protocols such as (LEACH, CHEF, PSO-MV) in terms of network lifetime and
energy consumption.