Maximum Degree of Centrality for Cluster Head Selection and Data Security in WSN

Wireless Sensor Networks (WSNs) present a new generation of real-time embedded systems for a wide variety of applications. However WSNs have limited computation, energy, and memory resources. One of the approaches to minimize the energy consumption is to allow only some nodes in a cluster of sensor nodes, called clusterheads, to communicate with the base station. Appropriate cluster-head election can drastically reduce the energy consumption and enhance the lifetime of the network. In this thesis, a fuzzy logic approach to cluster-head election is proposed based on three descriptors energy, concentration and centrality. After modeling the energy consumption for the WSN, we applied the algorithm to check for the quality of the network by measuring the time it takes for the first node to die in the cluster. We compare our algorithm with Low Energy Adaptive Clustering Hierarchy (LEACH), a previously proposed technique, by adjusting the cluster-head selection probabilities.