Fuzzy association rule based Cluster head selection in wireless Sensor Network

Abstract

Wireless sensor networks can be deployed in a site where the traditional networking infrastructure is practically impossible. Energy, memory, computation resources and transmission range are the limitations of Sensor Network. In this network, the sensor nodes are grouped together to form clusters. Cluster performs data aggregation and limits data transmissions hence data are disseminated to the cluster head and further propagated to the base station. Storage constraint is one of the challenging factors in the sensor network. Hence, this paper focuses on reducing the rule set by incorporating an association rule along with fuzzy logic for predicting the cluster head. Support and confidence are evaluated for the rule set and reduced final rule sets are generated based on the calculated confidence level with a certain threshold. Simulation results showed that a minimum rule set bin can predict the Cluster head, which has high potential in the group. The Node occupies less memory space for the reduced rule set and the computational complexities are reduced as a result it also enhances the network lifetime.