AI; beyond 5G and 6G; DRL; multiaccess edge computing (MEC); reconfigurable intelligent surfaces (RISs); uncrewed aerial vehicles (UAVs); Aerial vehicle; Beyond 5g and 6g; Computing system; Edge computing; Multi-access edge computing; Multiaccess; Reconfigurable; Reconfigurable intelligent surface; Unmanned aerial vehicle; Signal Processing; Information Systems; Hardware and Architecture; Computer Science Applications; Computer Networks and Communications; Surveys; Autonomous aerial vehicles; Security; Reconfigurable intelligent surfaces; Reviews; Internet of Things; Electronic mail; Wireless communication; Focusing; Energy efficiency
Abstract :
[en] Uncrewed aerial vehicles (UAVs) have become essential in advancing multiaccess edge computing (MEC), providing flexible platforms that enhance network capacity, coverage, and efficiency while reducing latency and improving communication quality. Integrating reconfigurable intelligent surfaces (RISs) with UAV-based MEC systems further elevates these capabilities, delivering significant gains in computational power, energy efficiency (EE), and physical-layer security (PLS). However, managing the complexity of RIS within UAV networks requires sophisticated optimization strategies. This survey offers a comprehensive analysis of the fundamentals of RIS, UAVs, and MEC, followed by an in-depth examination of RIS configurations in UAV-based MEC systems, including static, dynamic, and hybrid models. We evaluate the benefits and challenges of RIS integration, such as improved communication, enhanced computational efficiency, optimized energy use, better task management, and strengthened security. In addition, the survey explores the latest advancements in RIS-assisted UAVs for MEC, focusing on boosting computational capacity, minimizing delay, maximizing EE, and enhancing security. To provide a thorough exploration of these topics, detailed summary tables are included, offering a comparative analysis of methodologies, performance metrics, and scenarios from recent studies. Furthermore, the survey presents key lessons learned from current research and identifies future research directions crucial for fully realizing the potential of RIS-enhanced UAV-based MEC systems in next-generation networks.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Ahmed, Manzoor ; School of Computer and Information Science, Xiaogan, China ; The Institute for AI Industrial Technology Research, Hubei Engineering University, Xiaogan, China
Soofi, Aized Amin ; National University of Modern Languages, Department of Computer Science, Faisalabad, Pakistan
Raza, Salman ; National Textile University, Department of Computer Science, Faisalabad, Pakistan
Khan, Feroz ; Beijing University of Posts and Telecommunications, School of Electronic Engineering, Beijing, China
Ahmad, Shabeer ; Beijing University of Posts and Telecommunications, School of Electronic Engineering, Beijing, China
KHAN, Wali Ullah ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Asif, Muhammad ; Jiangsu University, School of Computer Science and Communication Engineering, Zhenjiang, China
Xu, Fang ; School of Computer and Information Science, Xiaogan, China ; The Institute for AI Industrial Technology Research, Hubei Engineering University, Xiaogan, China
Han, Zhu ; University of Houston, Department of Electrical and Computer Engineering, Houston, United States ; Kyung Hee University, Department of Computer Science and Engineering, Seoul, South Korea
External co-authors :
yes
Language :
English
Title :
Advancements in RIS-Assisted UAV for Empowering Multiaccess Edge Computing: A Survey
Publication date :
2025
Journal title :
IEEE Internet of Things Journal
eISSN :
2327-4662
Publisher :
Institute of Electrical and Electronics Engineers Inc.
Ministry of Education of China (MOE) Project of Humanities and Social Sciences Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation NSF, Toyota Amazon and Japan Science and Technology Agency (JST) Adopting Sustainable Partnerships for Innovative Research Ecosystem
Funding text :
This work was supported in part by the Ministry of Education of China (MOE) Project of Humanities and Social Sciences under Grant 23YJAZH169; in part by the Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation under Grant T2020017; in part by the NSF, Toyota under Grant ECCS-2302469; and in part by the Amazon and Japan Science and Technology Agency (JST) Adopting Sustainable Partnerships for Innovative Research Ecosystem (ASPIRE) under Grant JPMJAP2326. (Corresponding author: Salman Raza.) Manzoor Ahmed and Fang Xu are with the School of Computer and Information Science and also with the Institute for AI Industrial Technology Research, Hubei Engineering University, Xiaogan 432000, China.
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