Coordinated Sensing Coverage with Distributed Deep Reinforcement Learning

Abstract

The Internet of Things (IoT) extends the electronic connectivity into millions of IoT nodes in our city, which collect, share and fuse information to comprehend the status of the city. In order to achieve the autonomy to make control decisions based on the collected and analyzed information, a promising artificial intelligence method, reinforcement learning (RL), is for smart entities to leverage. In this paper, we propose a distributed learning approach using the deep RL method and consensus theories to solve the coordinated sensing coverage problem in wireless sensor and actuator networks. Also, evaluation works show the proposed algorithm emerges powerful capability, and this approach provides important operational advantages over traditional centralized and distributed approaches.