Performance Analysis and Optimization for IoT Mobile Edge Computing Networks With RF Energy Harvesting and UAV Relaying

• Published in: IEEE access Vol. 10; pp. 21526 - 21540
• Main Authors: Nguyen, Anh-Nhat, Ha, Dac-Binh, Vo, Van Nhan, Truong, Van-Truong, Do, Dinh-Thuan, So-In, Chakchai
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous aerial vehicles; Clusters; Computer simulation; Edge computing; Energy consumption; Energy harvesting; Internet of Things; Mathematical analysis; Mobile computing; mobile-edge computing; Monte Carlo simulation; NOMA; Nonorthogonal multiple access; Optimization; Particle swarm optimization; Performance evaluation; Radio frequency; Relaying; Relays; Servers; Switching; Task analysis; Unmanned aerial vehicles
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3150046

This paper studies unmanned aerial vehicle (UAV)-aided nonorthogonal multiple access (NOMA)-based mobile-edge computing (MEC) in Internet of Things (IoT) systems in which the uav acts as a relay (UR). Specifically, we consider a scenario with two clusters IoT devices (IDs) (i.e., a high-priority cluster IA and a low-priority cluster IB) with limited resources, so these IDs cannot compute their tasks and must offload them to a base station (BS) through a UR. We propose a protocol named time switching - radio frequency (RF) energy harvesting (EH) UR NOMA (TS-REUN), which is divided into 5 phases. By applying the TS-REUN protocol, the IDs in the two clusters and the UR harvest RF energy from the broadcast signal of the power beacons (PB). Then, the IDs offload their tasks to the MEC server located at the BS. After server processing, the IDs receive the calculation results from the BS via the UR. The effects of both imperfect channel state information (ICSI) and imperfect successive interference cancellation (ISIC) on the REUN-based mec (REUN-MEC) are taken into account. To evaluate the performance of the system, we derive closed-form expressions for the successful computation probability (SCP) and energy consumption probability (ECP) in the Nakagami-<inline-formula> <tex-math notation="LaTeX">\mathfrak {m} </tex-math></inline-formula> fading channel. Moreover, we propose an optimization problem formulation that maximizes the SCP by optimizing the position and the height of the UR and the time switching ratio (TSR). The problem was addressed by employing an algorithm based on particle swarm optimization (PSO). In addition, the Monte Carlo simulation results confirmed the accuracy of our analysis based on system performance simulations with various system parameters, such as the number of antennas at the BS, the number of IDs in each cluster, the TSR, and the position and the height of the UR.