Efficient computation for task offloading in 6G mobile computing systems

This research focuses on achieving efficient computation for complex tasks within the overlapping coverage of 6G network base stations. A multi-access edge computing network model that involves multiple base stations and IoT devices is constructed by addressing task offloading challenges while considering task latency, energy consumption, societal impacts, and economic incentives. Joint optimization of base station pricing, IoT device base station selection, and task offloading strategies aim to maximize base station profits and IoT device utilities. A many-to-one matching game model tackles IoT device base station selection, and a Stackelberg game theory-based two-stage model handles pricing and task offloading interactions. The proposed game theory-based optimal pricing and best response algorithm (OBGT) achieves equilibrium strategies, demonstrating rapid convergence in simulations and enhancing base station profits and IoT device utility. This study incorporates Data-driven Mobile Computing Systems Assurance for advancing efficient task offloading and optimization.