Secure Multi-Layer MEC Systems With UAV-Enabled Reconfigurable Intelligent Surface Against Full-Duplex Eavesdropper

• Published in: IEEE transactions on communications Vol. 72; no. 3; pp. 1565 - 1577
• Main Authors: Zhou, Yi, Ma, Zheng, Liu, Gang, Zhang, Zhengquan, Yeoh, Phee Lep, Vucetic, Branka, Li, Yonghui
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-03-2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autonomous aerial vehicles; Communication system security; Computation offloading; Eavesdropping; Edge computing; full-duplex eavesdropper; Full-duplex system; Iterative algorithms; Iterative methods; Mobile computing; multi-layer MEC; Multilayers; Reconfigurable intelligent surface; Reconfigurable intelligent surfaces; Resource allocation; Robustness (mathematics); Servers; Task analysis; Task complexity; UAV communications; Unmanned aerial vehicles; Wireless communication
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2023.3337239

In this paper, we develop a secure multi-layer mobile edge computing (MEC) system where an unmanned aerial vehicle (UAV) equipped with a reconfigurable intelligent surface (RIS) acts as an aerial edge server and assists the offloading from multiple ground users to a base station (BS), in the presence of a full-duplex active eavesdropper (AE). To enhance the computing performance, we consider a partially offloading scheme where the computational task at each user can be executed at itself and offloaded to the UAV edge server and the BS via the UAV-enabled RIS, respectively. To maximize the total number of secure computing tasks among all users, we design a low complexity iterative algorithm by jointly optimizing the RIS phase shift, UAV deployment, power and computing resource allocation subject to certain power constraints. Numerical results show that compared to benchmark offloading schemes, our proposed UAV-RIS aided multi-layer MEC design improves the computing performance by at least 12.91%. Numerical results also demonstrate the impact of the full-duplex AE and validate the robustness of our proposed solution.