Mobile Edge Deployment and Resource Management for Maritime Wireless Networks

Abstract

Mobile Edge Computing (MEC) has been envisioned as one of the key technologies for supplying computation and storage resources in Internet of Vessels (IoV) networks. Due to its flexible deployment, low cost and agile maneuverability, Unmanned Surface Vehicle (USV) has emerged as a promising solution, to provide communication and computation services for maritime users. In this paper, we study mobile edge deployment and resource management for MEC-assisted maritime wireless networks where USVs with diverse computation resources are deployed to provide edge computing services that complement the cloud-based services. To this end, we formulate an optimization problem to minimize the expected response time by jointly optimizing the deployment of mobile USVs and computation offloading decisions. To solve the mixed-integer nonlinear program problem, we propose a Dual-Layer Reinforcement Learning (DLRL) framework to attain a near-optimal solution. Specifically, a Deep Deterministic Policy Gradient (DDPG) algorithm is designed to obtain the best USV deployment in the outer layer learning, and a Q-learning algorithm is designed to determine the best computation offloading decisions in the inner layer learning. Numerical results demonstrate that the proposed solution outperforms some literature algorithms by effectively handling both continuous and discrete variables.