Energy-Efficient Resource Management for Multi-UAV-Enabled Mobile Edge Computing

Unmanned aerial vehicles (UAVs) have been widely utilized to expand wireless network coverage and provide computation service for Internet-of-Things (IoT) devices in signal-blocked or shadowed environments. In this paper, we propose a novel multi-UAV-enabled mobile edge computing (MEC) architecture in which multiple UAVs provide both communication and computation services for IoT devices that cannot directly access the ground edge clouds. To achieve min-max fairness of energy consumption among UAVs, we minimize the maximal energy consumption among UAVs by jointly optimizing computation offloading decisions, communication and computation resource allocation, UAV positions, and task splitting decisions, while meeting the delay requirement of all tasks. The required optimization is a large-scale mixed-integer non-linear program that is generally intractable. To solve this problem, we propose an efficient iterative algorithm based on the successive convex approximation (SCA). The simulation results show that the proposed scheme outperforms various baseline schemes in processing computation-intensive and latency-critical tasks.