DQN-based Coverage Maximization for Mobile Video Camera Networks

Abstract

Coverage maximization is an important issue of Mobile wireless sensor networks (M-WSN). Especially for visual sensors like video camera which have a specific sensing direction and range, obstacles and the position of the sensors should also be considered. In this paper, we propose an efficient coverage maximization method for mobile video camera networks by leveraging DQN, a deep reinforcement learning algorithm. Evaluation results show that the proposed method can cover up to 4.93% better than existing ones.