Reinforcement Learning-Based Coverage Maximization Under Full Connectivity Constraints in Mobile Wireless Sensor Network

Coverage maximization under full connection constraints involves many factors and poses huge challenge in mobile wireless sensor networks. Most of research works on this issue are based on the disk model, which have high complexity and long iterations, and therefore cannot be applied to dynamic complex networks. In this paper, the problem of confident information coverage maximization under full connectivity constraints (CIC-CC) is defined based on the confident information coverage model (CIC). To address this problem, a 3-stage connectivity constrained coverage maximization algorithm (3-CCC) is proposed with the time complexity of O(T*n2). 3-CCC contains three stages: maximizing coverage (MC), full connectivity (FC), and maximizing connectivity constrained coverage (MCCC). These three stages can be used in whole or in part to achieve coverage maximization with connectivity constraints depending on the network state. The stable matching mechanism, greedy algorithm, and Q-learning are utilized to improve the algorithm’s efficiency. Experiments show that the proposed algorithm has good performance in terms of iteration number, running time, and coverage rate.