An Self Regulating Clustering in Wireless Sensor Network using Particle Swarm Optimization

In a wide variety of application areas including geophysical monitoring, precision agriculture, habitat monitoring, transportation, military systems and business processes, Wireless Sensor networks (WSNs) are envisioned to fulfill complex monitoring tasks. WSN properties and requirements are both unique in the networks world and extremely diversified between themselves. Most of these applications demands efficient organization of network topology for data collection, data aggregation and load balancing to increase the network lifetime and Scalability of the network. The network must be autonomous and self organizing. WSN constraints demand an efficient and optimal clustering protocol for its operations. In order to facilitate low power consumption, fault tolerance, scalability, WSNs should be clustered hierarchically and aggregated data should to be routed energy efficiently with minimum latency. In this paper, we introduce a new approach for clustering wireless sensor networks based on Particle Swarm Optimization (PSO). Using the optimal fitness function, which aims to extend network lifetime. The parameters used in these Techniques are the distance from the base station, intra-cluster distance from the cluser head and inter cluster distance.