An Effective Technique for PSO Based Clustering and Polynomial Regression in Wireless Sensor Network

Wireless sensor networks (WSNs) consist of sensor nodes. These networks have huge application in habitat monitoring, disaster management, security and military, etc. Wireless sensor nodes are very small in size and have limited processing capability very low battery power. This restriction of low battery power makes the sensor network prone to failure. Data aggregation is very crucial technique in wireless sensor networks. With the help of data aggregation we reduce the energy consumption by eliminating redundancy. In this paper we discuss about data aggregation and its various energy-efficient technique used for data aggregation in WSN. Data aggregation is a process of aggregating the sensor data using aggregation approaches. Particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. The cluster head choice is predicated on the space, residual energy, position, velocity parameters. The optimized cluster head selected by exploitation PSO algorithm. Particle swarm optimization (PSO) could be a population-based random search method, shapely once the social behavior of a bird flock. The algorithmic rule maintains a population of particles, wh ere every particle represents a possible answer to an optimisation drawback.