Satellite-Assisted Task Offloading and Resource Allocation for Ocean of Things Edge Computing
Shuai Liu, Wenfeng Li, Hongyan Chen, Jingjing Wang, Kanglian Zhao · IEEE Internet of Things Journal · 2025
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%.