A learning automata-based solution to the target coverage problem in wireless sensor networks

In the last years, wireless sensor networks (WSNs) have been used in a wide range of applications like monitoring, tracking, classification, etc. One of the most crucial challenges in the WSNs is designing an efficient method to monitor a set of targets and, at the same time, extend the network lifetime. Because of high density of the deployed sensors, scheduling algorithms can be considered as a promising method. In this paper, a learning automata-based scheduling algorithm is designed for finding a near-optimal solution to the target coverage problem that can produce both disjoint and non-disjoint cover sets in the WSNS. In the proposed algorithm, one learning automaton is in charge of choosing the sensor nodes that should be activated at each stage to cover all the targets. Furthermore, two pruning rules are devised to help the learning automaton in selection of more suitable active sensors. We have conducted several simulation experiments to evaluate the performance of the proposed algorithm. The obtained results revealed that the proposed algorithm could successfully extend the network lifetime.