In this paper, we establish a multi-access edge computing (MEC)-enabled sea lane monitoring network (MSLMN) architecture with energy harvesting (EH) to support dynamic ship tracking, accident forensics, and anti-fouling through real-time maritime traffic scene monitoring. Under this architecture, the computation offloading and resource allocation are jointly optimized to maximize the long-term average throughput of MSLMN. Due to the dynamic environment and unavailable future network information, we employ the Lyapunov optimization technique to tackle the optimization problem with large state and action spaces and formulate a stochastic optimization program subject to queue stability and energy consumption constraints. We transform the formulated problem into a deterministic one and decouple the temporal and spatial variables to obtain asymptotically optimal solutions. Under the premise of queue stability, we develop a joint computation offloading and resource allocation (JCORA) algorithm to maximize the long-term average throughput by optimizing task offloading, subchannel allocation, computing resource allocation, and task migration decisions. Simulation results demonstrate the effectiveness of the proposed scheme over existing approaches.

翻译：本文提出了一种支持能量收集（EH）的多接入边缘计算（MEC）海上航道监测网络（MSLMN）架构，通过实时海事交通场景监测，为动态船舶跟踪、事故取证及防污处理提供支撑。在该架构下，联合优化计算卸载与资源分配，以最大化MSLMN的长期平均吞吐量。鉴于动态环境及未来网络信息的不可获取性，我们采用李雅普诺夫优化技术处理具有大规模状态与动作空间的优化问题，并构建一个受队列稳定性与能耗约束的随机优化模型。通过将原问题转化为确定性优化问题，并解耦时空变量，我们获得了渐近最优解。在保证队列稳定的前提下，提出了一种联合计算卸载与资源分配（JCORA）算法，通过优化任务卸载、子信道分配、计算资源分配及任务迁移决策，实现长期平均吞吐量的最大化。仿真结果表明，所提方案较现有方法具有显著优越性。