Latency-Aware MIoT Service Strategy in UAV-Assisted Dynamic MMEC Environment

Marine Internet of Things (MIoT) has emerged as a prominent technology for the future development of marine applications, in which edge equipment provides a valuable method for information collection and processing on smart mobile devices (SMDs). However, the deployment of edge equipment may result in high latency due to inefficient computing offloading schemes. In this paper, we propose an optimal offloading scheme based on a dynamic unmanned air vehicle (UAV) assisted marine mobile edge computing (MMEC) environment in which latency-sensitive computing tasks can be partially offloaded autonomously. Specifically, we consider a time-varying scenario where the UAV hovers over multiple maritime mobile unmanned surface vessels (USVs) and provides MIoT services over communication periods. Our objective is to minimize the overall task execution time through joint optimization of user scheduling variables, UAV motion trajectory, and resource allocation while considering energy consumption and spatial constraints, thereby achieving enhanced quality of service. Considering the non-convexity of this optimization problem, we propose an advanced Twin Delayed Deep Deterministic policy gradient (ATD3) algorithm and examine the convergence and optimality of different parameter factors. Simulation results demonstrate that the proposed algorithm is superior to the baseline scheme regarding convergence speed, adaptability, and task execution time.