A New Learning Automata-based Algorithm to Solve the Target k-coverage Problem in Wireless Sensor Networks

Recently, a number of algorithms have been proposed to solve the target coverage problem in wireless sensor networks (WSNs). Conventionally, it is assumed that only a single sensor is sufficient for covering a target; though, in real situations, more than one sensor may be required for this purpose. This problem is known as k-coverage problem that its NP-completeness has been already proved. To solve the problem, this paper proposes a learning-automata based algorithm equipped with a pruning rule. The aim of the proposed algorithm is to determine minimum number of sensors in such a way that each target can be monitored for at least k times. The proposed algorithm performance was evaluated through conducting a number of experiments. The experimental results were compared to those of a greedy-based algorithm. As shown by the final results, the learning-automata based algorithm was more successful than the greedy-based one regarding the construction of cover sets with minimum number of sensors.