CALAFL : Clustering Algorithm based on Learning Automata and Fuzzy Logic for wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of many low energy devices called Sensor Nodes (SNs) which sense and transfer the data to remote controller which is called Base Stations (BSs). They are typically deployed in abandoned environment and are limited in communication and computing power; it is not easy or economical to replace or charge the batteries. In order to operate independently, the sensor nodes should be capable of healing and equipping to a built-in intelligence.An area which has been studied greatly in this field is Energy efficient clustering which is a wellknownoptimization problem to extend the lifetime of wireless sensor networks (WSNs). In this paper, we propose a new Clustering Algorithm based on Learning Automata and Fuzzy Logic (CALAFL) scheme for WSNs which uses fuzzy logic to select appropriate cluster heads and learning automata tomaintain appropriate cluster head. The performance of the proposed scheme is validated using the extensive simulation. The results indicate that the proposed scheme operates better than the existing schemes.

Keywords:

Wireless Sensor Network , Clustering , Learning automata , Fuzzy logic