Toward a Sustainable Low-Altitude Economy: A Survey of Energy-Efficient RIS-UAV Networks

April 02, 2025 ยท The Cartographer ยท ๐Ÿ› IEEE Internet of Things Journal

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
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"Title-pattern auto-detect: Toward a Sustainable Low-Altitude Economy: A Survey of Energy-Efficient RIS-UAV Networks"

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Authors Manzoor Ahmed, Aized Amin Soofi, Feroz Khan, Salman Raza, Wali Ullah Khan, Lina Su, Fang Xu, Zhu Han arXiv ID 2504.02162 Category cs.ET: Emerging Technologies Cross-listed cs.NI Citations 17 Venue IEEE Internet of Things Journal Last Checked 10 days ago
Abstract
The integration of RIS into UAV networks presents a transformative solution for achieving energy-efficient and reliable communication, particularly within the rapidly expanding low-altitude economy (LAE). As UAVs facilitate diverse aerial services-spanning logistics to smart surveillance-their limited energy reserves create significant challenges. RIS effectively addresses this issue by dynamically shaping the wireless environment to enhance signal quality, reduce power consumption, and extend UAV operation time, thus enabling sustainable and scalable deployment across various LAE applications. This survey provides a comprehensive review of RIS-assisted UAV networks, focusing on energy-efficient design within LAE applications. We begin by introducing the fundamentals of RIS, covering its operational modes, deployment architectures, and roles in both terrestrial and aerial environments. Next, advanced EE-driven strategies for integrating RIS and UAVs. Techniques such as trajectory optimization, power control, beamforming, and dynamic resource management are examined. Emphasis is placed on collaborative solutions that incorporate UAV-mounted RIS, wireless energy harvesting (EH), and intelligent scheduling frameworks. We further categorize RIS-enabled schemes based on key performance objectives relevant to LAE scenarios. These objectives include sum rate maximization, coverage extension, QoS guarantees, secrecy rate improvement, latency reduction, and age of information (AoI) minimization. The survey also delves into RIS-UAV synergy with emerging technologies like MEC, NOMA, V2X communication, and WPT. These technologies are crucial to the LAE ecosystem. Finally, we outline open research challenges and future directions, emphasizing the critical role of energy-aware, RIS-enhanced UAV networks in shaping scalable, sustainable, and intelligent infrastructures within the LAE.
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