Security Energy Efficiency Optimization and Analysis of Aerial-IRS-Assisted UAV-MEC System

Abstract

Mobile edge computing (MEC) supported by unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS) can provide ubiquitous communication and computing services for internet of things (IoT) devices, particularly in remote areas and emergency situations caused by natural disasters. Additionally, integrating IRS and UAV can enhance the network’s secure service performance to address the significant challenges posed by colluding eavesdroppers in the system. To address the contradiction between high-security communication rate and low energy consumption, this paper introduces security energy efficiency as a performance metric for the system. To improve the security energy efficiency of the system, we propose an IRS-assisted UAV-MEC system for the joint optimization of computing resources, IRS phase shift, and UAV trajectory to maximize system’s performance. The optimization problem is a non-convex fraction problem, and complex coupling relations exist among the physical variables. We propose an iterative algorithm with a double-loop structure to solve the problem, in which the outer loop is based on the Dinkelbach method, and the inner loop is based on the block coordinate descent (BCD) algorithm to decompose the total problem. Finally, numerical simulations show that our proposed algorithm improves the system’s security energy efficiency compared with benchmark schemes, obtains a good trade-off between the security communication rate and energy consumption of the system, and improves the security communication performance of the system in the presence of colluding eavesdroppers.

Keywords

• Colluding eavesdroppers
• intelligent reflecting surface (IRS)
• mobile edge computing (MEC)
• security energy efficiency
• unmanned aerial vehicle (UAV)