Using the combination of particle swarm algorithms and fuzzy approach to provide a clustering method for network nodes with coverage maintenance in wireless sensor networks

Abstract

Wireless sensor network (WSN) is an inexpensive newfound technology with many applications in various fields (such as biology Environment, war and natural disasters). A network consisting of a large number of sensor nodes and collecting information from the environment in a distributed environment. The main limitations include limited energy, low communication capacity, low storage volume, and low bandwidth. This study provides a clustering method for network nodes by optimized particle swarm and fuzzy approach. This method determines the cluster head according to node's distances from each other and the left amount of energy and the distance from the sink and the density of each node, and determines the rest of the nodes in the network as sub clusters. Selecting the number and the place of cluster heads in order to have the most energy efficiency in the network is a Np-Hard problem, and on the other hand PSO algorithms is very flexible in solving dynamic problems. In this method we maintain the coverage and reduce the energy consumption by combining the particle swarm algorithms and fuzzy approach. This method is faster than the other methods.