Learning Automata Based Heuristics for Target Q-Coverage

Coverage problem in wireless sensor networks (WSNs), deals with monitoring of targets in a certain given terrain. In dense network, targets are covered by redundant sensors at any span of time while achieving full coverage. It happens so because, sensor nodes are densely deployed. A network is called operational till all the targets are covered by at least a sensor in cover set. To extend network lifetime, one can schedule the sensor activity by alternating each sensor from active to sleep state and vice versa. Since network dynamics is changing with time, the proposed energy proficient scheduling algorithm follows learning automata which helps a sensing node to choose action state (active or sleep). The effectiveness of the suggested method is proved by carrying a set of simulations over various scenarios under different environmental conditions.