Fuzzy Inference System Based Handover Scheme in UAV-Assisted MEC Network

Due to the rapid growth of smart devices and 5G technology increase the requirement of computational tasks, unmanned aerial vehicles (UAVs) assisted Mobile Edge Computing (MEC) networks are designed to improve the task processing by reducing latency. Considering the necessity to complete the task quickly, the UE must seamlessly handover (HO) to the optimal BS, which may lead to frequent HO. This paper introduces a Fuzzy Inference System (FIS) based HO decision-making scheme for UAV-assisted Mobile Edge Computing (MEC) networks, addressing the increasing demand for computational tasks. At first, the proposed method optimizes HO decisions using RSS, distance, and number of serving users. Then, it employs a two-layer FIS to select the target base station (BS), considering SINR, time of stay (ToS), distance, and user connectivity. Simulation results demonstrate the FIS-based method can achieve less HO frequency and task delay compared to RSS-based and TOPSIS-based schemes.