Energy-Minimized Partial Computation Offloading in Satellite-Terrestrial Edge Computing Networks

Given the forthcoming emergence of 6G communication models, the integration of terrestrial and nonterrestrial infrastructures is receiving increasing attention due to its widespread reach and broadcasting/multicast functions. The utilization of edge computing in space-related applications is 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 the user mobility and potential privacy leakage issues in a mobile edge computing (MEC) environment. Yet 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 nonlinear integer optimization problem to minimize the total system energy consumption. The model integrates several complex real-life nonlinear constraints, including operator cost, edge servers' computing capacity, storage capacity, resource and latency, and privacy ones. To tackle the problem, this work proposes an advanced hybrid algorithm named a slime mold 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 drastically over the state of the art.