Reinforcement learning-based dynamic position control of mobile node for ocean sensor networks

Abstract

In this paper, a novel dynamic position control (PC) approach for mobile nodes (MNs) is proposed for ocean sensor networks (OSNs) which directly utilizes a neural network to represent a PC strategy. The calculation of position estimation no longer needs to be carried out in the proposed scheme, so the localization error is eliminated. In addition, reinforcement learning is used to train the PC strategy, so that the MN can learn a more highly accurate and fast response control strategy. Moreover, to verify its applicability to the real-world environment, we conducted field experiment deployment in OSNs consisting of a MN designed by us and some fixed nodes. The experimental results demonstrate the effectiveness of our proposed control scheme with impressive improvements on PC accuracy by more than 53% and response speed by more than 15%.