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

arXiv: https://arxiv.org/abs/2506.15225 · HTML: https://mlsanity.com/paper/2506.15225/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Heterogeneous-Agent Reinforcement Learning](https://mlsanity.com/paper/2304.09870/) (2023)
- [Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial](https://mlsanity.com/paper/2011.03615/) (2020)
- [Settling the Variance of Multi-Agent Policy Gradients](https://mlsanity.com/paper/2108.08612/) (2021)
- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks](https://mlsanity.com/paper/2010.01370/) (2020)
- [Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey](https://mlsanity.com/paper/2012.06712/) (2020)
- [Hierarchical Aerial Computing for Internet of Things via Cooperation of HAPs and UAVs](https://mlsanity.com/paper/2202.06046/) (2022)
- [Computation Rate Maximization in UAV-Enabled Wireless Powered Mobile-Edge Computing Systems](https://mlsanity.com/paper/1806.04589/) (2018)
- [Wireless Backhaul in 5G and Beyond: Issues, Challenges and Opportunities](https://mlsanity.com/paper/2103.08234/) (2021)
- [Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework](https://mlsanity.com/paper/2405.11281/) (2024)
- [UAVs as a Service: Boosting Edge Intelligence for Air-Ground Integrated Networks](https://mlsanity.com/paper/2003.10737/) (2020)
- [Energy Consumption Minimization in Secure Multi-antenna UAV-assisted MEC Networks with Channel Uncertainty](https://mlsanity.com/paper/2302.09547/) (2023)
- [Computation Offloading for Uncertain Marine Tasks by Cooperation of UAVs and Vessels](https://mlsanity.com/paper/2302.06055/) (2023)

## Built on by (most cited first, 3 of 3)

- [Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARL](https://mlsanity.com/paper/2512.11862/) (2025)
- [Prompt-Driven Low-Altitude Edge Intelligence: Modular Agents and Generative Reasoning](https://mlsanity.com/paper/2602.14003/) (2026)
- [Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks](https://mlsanity.com/paper/2602.23667/) (2026)

## Abstract (arXiv)

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.