Joint Resource Optimization of Mobile Edge Computing in Disaster Areas Based on 5G Communication Network

In disaster-prone regions, cloud servers may face functionality impairments. Therefore, the emergence of the 5G era has led to the use of Unmanned Aerial Vehicles (UAVs) with edge servers as a promising alternative to centralized cloud servers for expedited restoration of computing capabilities in emergency situations. In this paper, we propose a multi-objective genetic particle swarm nested greedy algorithm (MOGAPSO-G) that optimizes energy usage and time delay within the system. By utilizing the Pareto optimal set obtained from MOGAPSO-G, one can comprehend the trade-offs between energy consumption and time delay, thus enabling the customization of UAV location deployment and task offloading strategies based on specific user requirements. Compared to the NSGA-II algorithm, MOPSO-G, the proposed MOGAPSO-G approach exhibits superior performance in terms of time delay and the Pareto frontier distribution. Notably, our method presents greater proximity to the true Pareto boundary, enhanced adaptability compared to the MOPSO algorithm, and increased robustness and practical applicability. This approach providing infrastructure support for AIoT computing that can significantly improve system efficiency and resilience during emergencies.