Resource Allocation and Pricing for UAV-enabled Mobile Edge Computing Systems
Na Yu, Shensheng Zheng, Xuehe Wang · 2023
Mobile edge computing (MEC) on unmanned aerial vehicles (UAVs) has emerged as a promising method to enhance the computational capabilities of mobile devices (MDs) with limited resources. However, most of the current research on computational offloading algorithms focuses on optimizing the delay and energy of users and lacks attention to the economy of the MEC system. Therefore, in this paper, we propose a three-stage UAV-enabled MEC system model to optimize MDs' cost and the UAV's revenue. To minimize the cost of MDs, we propose a partial computing offloading optimization method based on monetary cost. We investigate a cost-minimization strategy for partial offload that jointly controls task allocation and local central processing unit (CPU) frequency in MDs. In addition, we consider a revenue maximization problem for UAV servers with limited computing resources and propose a heuristic algorithm to determine the optimal service price to solve this problem. Finally, we apply an optimal UAV deployment method to maximize total revenue across all regions. Simulation results demonstrate the effectiveness of our proposed scheme in cost-saving and pricing.