Deep-Learning-Assisted Complete Targets Coverage in Energy-Harvesting IoT Networks

Complete targets coverage is required by many Internet of Things (IoT) applications. In this respect, an important goal is to maximize the number of time slots with complete targets coverage. Achieving such coverage is challenging when devices experience spatio-temporal energy arrivals. To this end, this article outlines a deep learning assisted approach that has an offline stage whereby it determines and stores an exhaustive collection of optimal activation schedules based the energy levels and arrivals of devices. In addition, it presents a network partitioning and training strategy, and outlines an algorithm to mend coverage holes in its online stage. We have compared the proposed approach with the optimal solution, and also a state-of-the-art heuristic algorithm. The results show that our solution achieves 94% of the optimal coverage lifetime. Moreover, the proposed approach has a 35% smaller optimality gap as compared with the said heuristic algorithm.