Energy Efficiency Optimization in Algorithm for RIS-Assisted UAV-Enabled MEC-IoT Networks

The combination of drones and smart reconfigurable surfaces (RISs) is becoming increasingly important for improving energy efficiency and wireless communication performance in Internet of Things (IoT) networks. This research focuses on developing an iterative optimization algorithm based on the fmincon algorithm in MATLAB. Sensing and transmission parameters are updated simultaneously at each iteration to achieve maximum energy efficiency. The algorithm starts with initial values. To optimize the energy efficiency of a system integrating a drone that provides mobile edge computing (MEC) services to IoT devices, the proposed system takes into account several critical factors, including drone trajectory optimization, optimal bit allocation between local and drone processing, and phase shift optimization in smart reconfigurable surfaces. The goal is to maximize overall energy efficiency by jointly optimizing these elements through a novel algorithm that alternates between optimizing the smart reconfigurable surfaces' phase shifts, the drone trajectory, and bit allocation. Simulation results demonstrate that the proposed solution significantly outperforms other measurement approaches in terms of energy efficiency, while examining the impact of variables such as the number of users, the reflectance elements of reconfigurable smart surfaces, and base station antennas on system performance. In conclusion, this research presents a novel approach to enhancing energy efficiency in RIS-enabled and drone-enabled MEC systems for IoT networks, achieving significant improvements over existing methods.