Energy-Efficient Multi-Access Edge Computing for Heterogeneous Satellite-Maritime Networks: A Hybrid Harvesting-and-Offloading Design

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Minghui Dai, Shan Chang, Yixuan Wang, Zhou Su

Published: 01 Jan 2025, Last Modified: 11 Nov 2025IEEE Trans. Mob. Comput. 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Low earth orbit (LEO) constellation integrated maritime networks have recently attracted much interest due to the rapid development of maritime applications and services. LEO satellites have the advantages of wide coverage to provide seamless connection for maritime wireless devices. However, due to the limited battery and computing capacity of uncrewed aerial vehicles (UAVs) for ocean information perception and processing, the computing-intensive and delay-sensitive oceanic data suffer from long latency and high energy consumption, which degrades the efficiency of maritime services. In this paper, to enhance the perception and offloading endurance of UAVs in maritime networks, we propose an energy efficient multi-access edge computing scheme for heterogeneous satellite-maritime networks, with the objective of minimizing the cumulative transmitted energy for UAVs. Specifically, we first present a heterogeneous satellite-maritime network framework in which LEO satellites and uncrewed surface vehicles (USVs) equipped with edge servers can process workloads simultaneously. Next, considering the limited battery supply of UAVs, we propose a hybrid harvesting-and-offloading scheme for resource allocation, where UAVs first harvest energy from solar power and radio frequency power from USV, and then UAVs determine the offloading strategy for task processing. Moreover, a joint optimization problem is formulated to optimize the offloading decision, the time scheduling, and the transmitting power. We also exploit a vertical architecture to solve the formulated problem. Regarding each decomposed sub-problem, we propose efficient algorithms to derive the corresponding solutions. Finally, we provide numerical results to validate the performance of our proposed algorithms in comparison with several benchmark algorithms.