Optimizing Value of Information for Simultaneous Energy Replenishment and Data Collection in AUV-Assisted UWSNs

Energy constraints significantly limit the long-term operation of underwater wireless sensor networks (UWSNs) due to their battery-powered sensor nodes (SNs). In this paper, we investigate an autonomous underwater vehicle (AUV)assisted UWSN where the AUV simultaneously collects data and recharges multiple independently located SNs. We employ the value of information (VoI) to evaluate the importance of the sensing data. We propose a novel Lyapunov-guided deep reinforcement learning (LDRL) algorithm to maximize the long-term average VoI while guaranteeing the battery energy constraints of SNs. We first employ a Lyapunov optimization to decompose the multi-stage stochastic VoI maximization problem into a series of single-stage deterministic subproblems. The Lyapunov drift-pluspenalty function is designed to deal with the long-term energy queue equilibrium. Then, we employ a deep neural network (DNN) to optimize the AUV's path and integrate a sequential convex approximation (SCA) optimization module for optimal charging time allocation. Experimental results demonstrate that our algorithm significantly enhances the long-term average VoI while ensuring the SNs' battery energy constraints.