Cellular Learning Automata based Scheduling Method for Wireless Sensor Networks

In wireless sensor network often micro-battery with very limited power provides the energy of sensor nodes. Since sensors are usually utilized in remote or hostile environments, recharging or replacing the battery of the sensors is something quite undesirable or even impossible. Thus long system lifetime is a must. Sleep scheduling is a mechanism in wireless sensor network to save energy. In this paper, we propose an energyefficient distributed scheduling ethod considering mobile target tracking also called dynamic target coverage. The algorithm is based on cellular learning automata. In this algorithm, each node is equipped with a learning automaton which will learn (schedule) the proper on and off times of that node based on the movement nature of a single moving target. To evaluate the proposed method it is tested under straight with constant velocity movement model of target. The results of experimentations have shown that the proposed scheduling algorithm outperforms two existing dynamic target coverage scheduling methods.