Computation Offloading Analysis of IoT Networks with UAV-enabled MEC with NOMA and WPT

In this paper, thept performance of an Internet of Thing (IoT) system using an unmanned aerial vehicle (UAV) in an urban setting is investigated using mobile-edge computing (MEC) based on non-orthogonal multiple access (NOMA). We analyze two clusters of IoT devices (IDs) with limited resources that can harvest energy from a power beacon (PB) operating as a wireless power transfer (WPT) station and offload their tasks to a UAV. We propose a four-phase technique for IDs offloading and energy harvesting (EH). To evaluate offloading performance, we define the expression of offloading outage probability (OOP) and successful computation probability (SCP) for the entire system. Additionally, we offer a formulation of the optimization problem that optimizes the OOP and SCP by optimizing the EH time and height of the UAV. Using particle swarm optimization (PSO), these problem were fixed. To confirm the correctness of our analysis, a variety of system parameters are assessed based on the Monte Carlo simulation, such as the height of the UAV, the number of IDs in each cluster and the EH time.