Energy-minimized Partial Computation Offloading in Satellite-terrestrial Edge Computing Networks

Given the forthcoming emergence of 6G communication models, the integration of terrestrial and non-terrestrial infrastructures is receiving increasing attention due to its widespread reach and broadcasting/multicast functions. The utilization of edge computing in space-related applications is becoming increasingly appealing. However, the issue of positioning satellite edge servers and deploying services has yet to be resolved. Besides, existing studies mainly concentrate on energy consumption and latency problems, often neglecting issues about user mobility and potential privacy leakage in a mobile edge computing (MEC) environment. Thus, it is crucial to optimize computation offloading and resource allocation for satellite-terrestrial edge computing networks. This work designs an innovative architecture for collaborative computation among multiple mobile devices and MEC servers deployed in ground stations and satellites. Based on this architecture, we formulate a limited non-linear integer optimization problem to minimize the total system energy consumption. The model integrates several complex real-life non-linear constraints, including operator cost constraints, edge servers’ computing capacity constraints, storage capacity constraints, resource and latency constraints, and privacy constraints. To tackle the problem, this work proposes an advanced hybrid algorithm named a Slime Mould algorithm with Genetic operations and individual updates of Grey wolf optimizer (SMG2). SMG2 optimizes user mobility and privacy protection while optimizing server and service placement to minimize total energy consumption. Simulation experiments demonstrate that SMG2 reduces energy consumption by 81.10% on average compared to the other five state-of-the-art algorithms.