MADRL-Based Edge Computing: Joint Energy-Latency Optimization for Marine Internet of Things

Mobile edge computing technology has facilitated the deployment of high computational algorithms on Marine Internet of things (MIoT) devices that equipped with limited computing resources. However, the dynamic changes in the network environment, the strict requirements of system latency and energy consumption of mobile devices, restrict the task execution in MIoT. This paper proposes an offloading framework in a marine mobile edge computing scenario, which is assisted by sea buoys and unmanned aerial vehicles (UAVs). Aiming to achieve long-term system optimization goals under resource-constrained conditions, with task partitioning, user scheduling, and resource allocation joint optimization under the constraints of UAV residual battery life, system latency, and energy consumption. This paper specifically introduces a Network Partition Point Reservation Algorithm (NPPR) that reduces the solution space of the problem through preprocessing. Subsequently, the paper presents a multiagent deep deterministic policy gradient (MADDPG) algorithm, enhanced with standardization and adaptive learning rate decay (SA-MADDPG), to address task heterogeneity and environmental dynamics. Simulation results demonstrate that, compared to existing algorithms, the SA-MADDPG algorithm proposed in this paper reduces system latency and energy consumption by 34.7% and 61.2%, respectively.