Joint Computation Offloading and Resource Allocation for Uncertain Maritime MEC via Cooperation of UAVs and Vessels

• URL: http://arxiv.org/abs/2506.15225v1
• Date: Wed, 18 Jun 2025 08:10:50 GMT
• Title: Joint Computation Offloading and Resource Allocation for Uncertain Maritime MEC via Cooperation of UAVs and Vessels
• Authors: Jiahao You, Ziye Jia, Chao Dong, Qihui Wu, Zhu Han,
• Abstract summary: In this paper, we focus on the maritime offloading and resource allocation through the cooperation of UAVs and vessels.<n>Specifically, we propose a cooperative MEC framework for computation offloading and resource allocation, including MIoT devices, UAVs and vessels.<n>As for the uncertain MIoT tasks, we leverage Lyapunov optimization to tackle the unpredictable task arrivals and varying computational resource availability.
• Score: 36.3157805511305
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The computation demands from the maritime Internet of Things (MIoT) increase rapidly in recent years, and the unmanned aerial vehicles (UAVs) and vessels based multi-access edge computing (MEC) can fulfill these MIoT requirements. However, the uncertain maritime tasks present significant challenges of inefficient computation offloading and resource allocation. In this paper, we focus on the maritime computation offloading and resource allocation through the cooperation of UAVs and vessels, with consideration of uncertain tasks. Specifically, we propose a cooperative MEC framework for computation offloading and resource allocation, including MIoT devices, UAVs and vessels. Then, we formulate the optimization problem to minimize the total execution time. As for the uncertain MIoT tasks, we leverage Lyapunov optimization to tackle the unpredictable task arrivals and varying computational resource availability. By converting the long-term constraints into short-term constraints, we obtain a set of small-scale optimization problems. Further, considering the heterogeneity of actions and resources of UAVs and vessels, we reformulate the small-scale optimization problem into a Markov game (MG). Moreover, a heterogeneous-agent soft actor-critic is proposed to sequentially update various neural networks and effectively solve the MG problem. Finally, simulations are conducted to verify the effectiveness in addressing computational offloading and resource allocation.

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