Aerial-Aerial-Ground Computation Offloading Using High Altitude Aerial Vehicle and Mini-drones

Esmail Almosharea, Mingchu Li, Runfa Zhang, Mohammed Albishari, Ikhlas Al-Hammadi, Gehad Abdullah Amran, Ebraheem Farea

Abstract: Unmanned Aerial Vehicles (UAV) supported by 5G networks can play an important role in providing aerial-aerial/aerial-ground computing services to remote and isolated areas at a low cost. In this paper, we present an aerial-aerial-ground network (AAGN) computing architecture using High Altitude Unmanned Aerial Vehicle (HAU) and Mini-Drones (MDs) based on Mobile Edge Computing (MEC) services where HAU provides computation offloading services for MDs, while MDs can serve as edge computing servers that can be equipped with appropriate capabilities to provide computing services for User Equipments (UEs) on demand. This study focuses on the computation offloading services provided by HAU to MDs, where the MD offloads all or a part of the task to the HAU, and the remaining of the task can be executed by MD. The proposed AAGN framework aims to reduce the MDs’ energy consumption and minimize the processing delay by optimizing HAU mobility, MDs scheduling, flight speed, flight angle, and tasks offloading, equipping HAU with the required computing resources. We investigate the computation offloading problem using Deep Deterministic Policy Gradient (DDPG) as a computing offloading approach to learn the optimal offloading policy from a dynamic AAGN environment, considering this problem as a non-convex problem. The simulation results show the feasibility and effectiveness of the proposed AAGN environment where DDPG algorithm can achieve an optimal decision offloading policy and obtains a critical optimization in delay and task offloading ratio compared with Deep Q Network (DQN) and Actor-Critic (AC) algorithms.