Optimum K-Coverage in Wireless Sensor Network with no Redundant Node by Cellular Learning Automata

Wireless Sensor Networks (WSNs) have been widely considered as one of the most important technologies for the twenty-first century. Thus, the coverage and energy consumption are the key issues of wireless sensor network research. In a distributed coverage algorithm, each node has the capability to decide its working mode with the help of neighboring nodes information. In k-coverage any point in the target area is covered by at least K sensor nodes. To achieve k-coverage, sensor node deployment must be carefully treated. Some protocols like DRKC has been provided recently to achieve k-coverage in dense sensor networks with an efficient distributed approximation algorithm. In this paper, we propose a new distributed location unaware algorithm to find an optimum selection of nodes to be activated in order to obtain full k-coverage while deactivating all of redundant nodes by Cellular Learning Automata (CLA). Simulation results show good improvements in coverage performance, increasing lifetime and decreasing number of active nodes and energy consumption in the wireless sensor network