Energy Efficient Target Coverage in Wireless Sensor Networks Using Adaptive Learning

Over the past few years, innovation in the development of Wireless Sensor Networks (WSNs) has evolved rapidly. WSNs are being used in many application fields such as target coverage, battlefield surveillance, home security, health care supervision, and many more. However, power usage in WSNs remains a challenging issue due to the low capacity of batteries and the difficulty of replacing or charging them, especially in harsh environments. Therefore, this has led to the development of various architectures and algorithms to deal with optimizing the energy usage of WSNs. In particular, extending the lifetime of the WSN in the context of target coverage problems by resorting to intelligent scheduling has received a lot of research attention. In this paper, we propose a scheduling technique for WSN based on a novel concept within the theory of Learning Automata (LA) called pursuit LA. Each sensor node in the WSN is equipped with an LA so that it can autonomously select its proper state, i.e., either sleep or active with the aim to cover all targets with the lowest energy cost. Through comprehensive experimental testing, we verify the efficiency of our algorithm and its ability to yield a near-optimal solution. The results are promising, given the low computational footprint of the algorithm.