Satellite-Assisted Task Offloading and Resource Allocation for Ocean of Things Edge Computing

Abstract

With the increasing number of terminal devices in the Ocean of Things (OoT), it is necessary to apply the OoT mobile edge computing (MEC) paradigm to low-Earth orbit (LEO) satellites. The aim is to support the operation of compute-intensive OoT services with LEO satellite assistance. To address the proliferation of computing services in OoT, this article proposes a satellite-assisted task offloading and resource allocation (STORA) approach for OoT edge computing, which includes a generalized framework for three-layer MEC systems in space, on the surface, and underwater. First, the MEC system energy minimization problem is described as mixed integer-nonlinear programming (MINLP) and divided into two subproblems: 1) task offloading and 2) resource allocation. Second, the task offloading subproblem is modeled as a Markov decision process (MDP). The proposed adaptive deep deterministic policy gradient (A-DDPG) algorithm jointly optimizes the offloading policy and offloading volume. In A-DDPG, a soft network update method with an adaptive updating coefficient ensures stable network updates while achieving fast convergence. Finally, the resource allocation is decomposed into a joint optimization problem involving buoy and satellite computational resources, which is shown to be convex. The Lagrange multiplier method is used to optimize the buoy-satellite resource allocation problem while also balancing edge computational load across servers. The experimental results show that STORA can reduce network energy consumption by 17.8%, increase network lifetime by 24.4%, and lower network latency by 11.5%.