Energy Efficient Cluster Formation Algorithm Based on GA-optimized Fuzzy Logic for Wireless Sensor Networks

Prolonging lifetime has always been a crucial issue in Wireless Sensor Networks (WSNs). Many researchers have contributed lots of routing techniques to maximize the network lifetime. Clustering is one of the most popular routing methods due to its high-energy efficiency and scalability. Numerous clustering algorithms have been proposed in recent years. Most of them focused on cluster head selection and neglected the importance of cluster head formation. This paper demonstrates the importance of the cluster formation issue and contributes a novel cluster formation algorithm for WSNs: an energy efficient Cluster formation algorithm based on GA-optimized Fuzzy Logic (CGAFL). In CGAFL, a Fuzzy Inference System (FIS) is applied in cluster formation phase. The fuzzy inference system takes residual energy of the CH, distance between the CH and BS, and distance between the CH and the node as parameters. Each non-CH applies the FIS for each CH and joins the CH that has the maximum chance value to form the cluster. Unlike other fuzzy logic protocols, we use Genetic Algorithm (GA) to optimize the fuzzy inference rule in the FIS. Simulation results show that CGAFL can find the optimal fuzzy inference rules and prolong the lifetime of WSNs, compared with LEACH, CFFL and FLCFP.