Joint Optimization of Task Offloading and Resource Allocation for Multi-Layer USV-MEC System with Renewable Energy Supply

Abstract

Mobile edge computing (MEC) plays a vital role in supporting communication, computation, and resource management within the maritime industry. It has become an essential element in the advancement of maritime technology. This study adopts a space-air-sea integrated MEC network, in which high-altitude platforms (HAPs) and low-earth orbit (LEO) satellites powered by renewable energy assist unmanned surface vehicles (USVs) in task offloading. The goal of this study is to minimize the total energy consumption of the system subject to a controllable task backlog queue. The Lyapunov optimization method is adopted to transform the original stochastic optimization problem into a deterministic optimization problem, thereby ensuring the stability of the system. Furthermore, an algorithm based on convex optimization is proposed, which is specifically designed to optimize transmission power allocation and channel allocation. The simulation results verify that the proposed scheme can significantly reduce the total energy consumption of the system compared with other benchmark schemes.

Keywords

Lyapunov optimization / unmanned surface vehicle / convex optimization / mobile edge computing

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