Intelligent Target Coverage in Wireless Sensor Networks with Adaptive Sensors

Day by day innovation in wireless communications and micro-technology has evolved in the development of wireless sensor networks. This technology has applications such as healthcare supervision, home security, battlefield surveillance and many more. However, due to the use of small batteries with low power this technology faces the issue of power and target monitoring. There is much research done to overcome these issues with the development of different architecture and algorithms. In this paper, a scheduling machine learning algorithm called adaptive learning automata algorithm(ALAA) is used. It provides an efficient scheduling technique. Such that each sensor node in the network has been equipped with learning automata, and with this, they can select their proper state at any given time. The state of the sensor is either active or sleep. For the experiment, different parameters are used to check the consistency of the algorithm to schedule the sensor node such that it can cover all the targets with the use of less power. The results obtained from the experiments show that the proposed algorithm is an efficient way to schedule the sensor nodes to monitor all the targets with use of less power. On the whole, this paper manages to achieve its goal by contributing to the related research on wireless sensor networks with a new design of a learning automata scheduling algorithm. The ability of this proposed algorithm to use the minimum number of sensors to be in active state verified to reduce the use of power in the network. Thus, achieving the goal by enhancing the lifetime of wireless sensor networks.