IEEE Transactions on Vehicular Technology · Journal article · 2025
Authors: Jiahao You, Ziye Jia, Chao Dong, Qihui Wu, Zhu Han
Published in: IEEE Transactions on Vehicular Technology, vol. 74, no. 11, pp. 18081–18095, Nov. 2025.
Authorship: First author / 第一作者
Research overview
This paper studies how autonomous aerial vehicles and vessels can jointly provide computing services for uncertain maritime IoT tasks.
The problem
Unpredictable task arrivals and varying resource availability complicate maritime offloading. Aerial vehicles and vessels also have different computing capacities and action spaces, so their decisions require coordinated resource allocation.
The approach
Lyapunov optimization converts long-term constraints into per-slot problems that minimize execution time. These problems are formulated as a Markov game and addressed with heterogeneous-agent soft actor-critic, using sequential network updates to coordinate the different agents.
Evaluation
Numerical simulations evaluate offloading and resource allocation under the considered maritime conditions. Detailed assumptions, baselines, and results are available in the published article.
面向不确定海洋边缘计算的自主飞行器与船舶协同卸载和资源分配
这项工作研究自主飞行器与船舶如何协同，为具有不确定性的海洋物联网任务提供计算服务。
研究问题
任务到达和可用资源存在变化，增加了海洋计算卸载的难度。飞行器与船舶的计算能力和动作空间不同，也需要协调资源分配。
研究方法
论文利用 Lyapunov 优化将长期约束转为逐时隙问题，以减小任务执行时间；随后构建马尔可夫博弈，并通过异构智能体软演员–评论家算法及顺序网络更新协调不同节点。
研究评估
论文在所设海洋场景下通过数值仿真评估卸载和资源分配效果。完整假设、基线和结果见发表原文。
中文题名为便于阅读所作的译名，正式引用请使用英文原题。
Citation
[1] J. You, Z. Jia, C. Dong, Q. Wu, and Z. Han, “Joint computation offloading and resource allocation for uncertain maritime MEC via cooperation of AAVs and vessels,” IEEE Trans. Veh. Technol., vol. 74, no. 11, pp. 18081–18095, Nov. 2025, doi: 10.1109/TVT.2025.3581970.
