Computation Offloading for Uncertain Marine Tasks by Cooperation of UAVs and Vessels

TL;DR

This paper proposes a cooperative framework using UAVs and vessels for marine task offloading, formulating it as a Markov decision process and applying Q-learning to optimize execution time and energy consumption in uncertain maritime environments.

Contribution

It introduces a novel cooperative offloading framework and a Q-learning based solution for marine tasks, addressing resource limitations and uncertainty.

Findings

The proposed algorithm effectively reduces execution time.

The approach minimizes energy consumption while maintaining task completion.

Simulation results validate the method's efficiency and correctness.

Abstract

With the continuous increment of maritime applications, the development of marine networks for data offloading becomes necessary. However, the limited maritime network resources are very difficult to satisfy real-time demands. Besides, how to effectively handle multiple compute-intensive tasks becomes another intractable issue. Hence, in this paper, we focus on the decision of maritime task offloading by the cooperation of unmanned aerial vehicles (UAVs) and vessels. Specifically, we first propose a cooperative offloading framework, including the demands from marine Internet of Things (MIoTs) devices and resource providers from UAVs and vessels. Due to the limited energy and computation ability of UAVs, it is necessary to help better apply the vessels to computation offloading. Then, we formulate the studied problem into a Markov decision process, aiming to minimize the total execution time and energy cost. Then, we leverage Lyapunov optimization to convert the long-term constraints of the total execution time and energy cost into their short-term constraints, further yielding a set of per-time-slot optimization problems. Furthermore, we propose a Q-learning based approach to solve the short-term problem efficiently. Finally, simulation results are conducted to verify the correctness and effectiveness of the proposed algorithm.