Certain investigations on optimized self healing and self configuring wireless networks

Abstract

newline In Wireless Sensor Networks the efficient use of resources is the basic challenge. One such significant challenge is the utilization of bandwidth. The bandwidth is the fundamental resource for transmitting data in a network. The bandwidth must be maximum while gathering and forwarding data from the sensor nodes to the Base Station. In this research work, Particle Swarm Optimization algorithm is employed for the formation of successful clustering in wireless sensor network and in choosing the cluster head. Clusters are formed by considering the fitness value of every particle. To reduce the workload of every cluster head, a corresponding cluster assistant node, with second maximum fitness value, is selected. Also Probabilistic Neural Network approach is used here for allocating maximum bandwidth for the nodes and for dynamic channel assignment of the nodes. The cluster head which has the global best fitness value will transmit the data to the base station. Experimental results show that the proposed method is better than the conventional methods.