WSN wtih Artificial Intelligence Techniques based Optimization using PSO - A Survey

Abstract

The WSN is the network of sensor in which each node can collect  and  forward  data  to  his  neighbor  node  or  to sink node say base station. The genetic algorithm formally used for creating and re-initiating the cluster based WSN in which it optimizes the network while network establishment. Optimization done in such way that the networks itself find his cluster head and leaf node of network by using fitness function. The dormant parameters, connectivity parameters and energy parameters play precise role to form fitness function to validate WSN. The optimization stages of WSN are node placement, network coverage, clustering, data aggregation and routing can be optimized by using artificial intelligence techniques such as genetic algorithm and particle swarm optimization. In advance to the genetic algorithm for forming energy-efficient network particle swarm optimization computational method came into picture. Particle swarm optimization internally uses candidate solution to find optimize solution to get strengthen particle out  of all swarm particle say node to establish WSN. Particle swarm optimization is the most efficient way of optimizing the stages of WSN.