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

TL;DR

This paper proposes a cooperative MEC framework utilizing UAVs and vessels to efficiently handle uncertain maritime IoT tasks through advanced optimization and reinforcement learning techniques.

Contribution

It introduces a novel cooperative MEC framework with Lyapunov optimization and a heterogeneous-agent soft actor-critic for uncertain maritime task offloading.

Findings

Effective reduction in total execution time.

Successful handling of uncertain task arrivals.

Improved resource allocation efficiency.

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.