AUV-Aided Localization for Internet of Underwater Things: A Reinforcement-Learning-Based Method

Abstract

Localization is a critical issue for many location-based applications in the Internet of Underwater Things (IoUT). Nevertheless, the asynchronous time clock, stratification effect, and mobility properties of the underwater environment make it much more challenging to solve the localization issue. This article is concerned with an autonomous underwater vehicle (AUV)-aided localization issue for IoUT. We first provide a hybrid network architecture that includes surface buoys, AUVs, and active and passive sensor nodes. On the basis of this architecture, an asynchronous localization protocol is designed, through which the localization problem is provided to minimize the sum of all measurement errors. In order to make this problem tractable, a reinforcement-learning (RL)-based localization algorithm is developed to estimate the locations of AUVs, and active and passive sensor nodes, where an online value iteration procedure is performed to seek the optimization locations. It is worth mentioning that the proposed localization algorithm adopts two neural networks to approximate the increment policy and value function, and more importantly, it is much preferable for the nonsmooth and nonconvex underwater localization problem due to its insensitivity to the local optimal. Performance analyses for the RL-based localization algorithm are also provided. Finally, simulation and experimental results reveal that the localization performance in this article can be significantly improved as compared with the other works.