Joint Computation Offloading and Resource Allocation for Maritime MEC with Energy Harvesting

TL;DR

This paper proposes a joint computation offloading and resource allocation scheme for maritime MEC networks with energy harvesting, optimizing long-term throughput in dynamic environments using Lyapunov optimization.

Contribution

It introduces a novel JCORA algorithm for joint optimization in maritime MEC with energy harvesting, addressing dynamic conditions and large state spaces.

Findings

The proposed scheme outperforms existing methods in simulations.

Effective long-term throughput maximization under energy and queue constraints.

Demonstrates feasibility of real-time maritime traffic monitoring with energy harvesting.

Abstract

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.