Reconfigurable Intelligent Surfaces for Wireless Energy Transfer in IoT: A Survey on Architectures, Applications, and Open Challenges

Abstract

Radio-frequency wireless energy transfer (RF-WET) has emerged as a promising solution for extending the lifetime of energy-constrained Internet of Things (IoT) devices, particularly in large-scale, obstructed, and hard-to-access deployments. However, practical RF-WET systems are limited by severe propagation losses, blockage, inefficient energy focusing, costly channel state information (CSI) acquisition, and the nonlinear behavior of RF–DC energy harvesting circuits. Reconfigurable intelligent surfaces (RISs) offer a new degree of freedom for addressing these limitations by reshaping the wireless propagation environment and directing RF energy toward energy-limited devices. The purpose of this paper is to provide a WET-centered survey of RIS-assisted RF energy transfer systems for IoT networks. Unlike previous surveys that primarily focus on wireless information transfer (WIT) or treat WET as a secondary application, this work jointly examines conventional RIS, simultaneously transmitting and reflecting RIS (STAR-RIS), and beyond-diagonal RIS (BD-RIS) architectures from an energy-transfer perspective. The main contribution is to consolidate RF-WET fundamentals, RIS operating principles, IoT use cases, optimization frameworks, and CSI acquisition strategies under a unified WET-centered perspective, while distinguishing WET-oriented studies from wireless-powered communication systems in which WET serves only as an energy-supply phase. The main findings are that conventional RIS, STAR-RIS, and BD-RIS provide complementary benefits for RIS-assisted WET, including passive energy focusing, blockage mitigation, full-space energy coverage, and enhanced electromagnetic control. However, practical deployment remains constrained by nonlinear EH modeling, scalable optimization, CSI overhead, PB/RIS placement, near-field operation, hardware impairments, RF exposure, experimental validation, and benchmarking. These findings point to future directions toward practical, sustainable, and deployable RIS-assisted WET systems.