Fairness-Aware Offloading and Trajectory Optimization for Multi-UAV Enabled Multi-Access Edge Computing

• Published in: IEEE access Vol. 8; pp. 124359 - 124370
• Main Authors: Diao, Xianbang, Wang, Meng, Zheng, Jianchao, Cai, Yueming
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Convexity; Delays; Edge computing; Fairness; Iterative methods; Mobile computing; multi-access edge computing; multiple UAVs; offloading optimization; Optimization; Servers; Task analysis; Trajectory optimization; Unmanned aerial vehicles
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3006112

Multiple unmanned aerial vehicles (UAVs) can compensate for the performance deficiencies of a single UAV in multi-access edge computing (MEC) systems, thus providing improved offloading services to user equipments (UEs). In multi-UAV enabled MEC systems, the offloading strategy and UAVs' trajectories affect the fairness of both UEs and UAVs, which affects the UE experience and UAVs' existence durations. Therefore, we investigate fairness-aware offloading and trajectory optimization in the system. To ensure fairness of energy consumptions (ECs) for both UEs and UAVs, we minimize the weighted sum of the maximum EC among UEs and the maximum EC among UAVs subject to the task delay, the offloading strategy and UAVs' trajectories constraints. Despite the non-convexity of the original formulated joint optimization problem, we transform the problem into two sub-problems and solve them one by one. Finally, an iterative optimization algorithm is proposed to alternately optimize the offloading strategies and the UAVs' trajectories. Simulation results show that the proposed algorithm can effectively reduce both the maximum EC among UEs and the maximum EC among UAVs and ensure the fairness of both the UEs and UAVs.