Reconfigurable Intelligent Surfaces for Enhanced Wireless Power Transfer in Massive IoT Networks

Author

OGBOGBO Lucky Joseph, OMOROGIUWA Osaremwinda


Abstract

 The deployment of massive Internet of Things (mIoT) networks encompassing thousands to millions of ultra-low-power sensor nodes faces a critical energy sustainability challenge. Conventional battery-powered architectures are economically and environmentally untenable at scale, while existing wireless power transfer (WPT) solutions suffer from severe path loss, blockage vulnerability, and inability to serve ultra-dense device populations efficiently. This study introduces a novel Reconfigurable Intelligent Surface (RIS)-enhanced WPT architecture specifically designed for massive IoT ecosystems. By deploying programmable metasurface arrays that intelligently reconfigure the wireless propagation environment, our system creates virtual line-of-sight energy channels through non-line-of-sight (NLoS) regions, effectively transforming blocked or weak signal paths into high-efficiency power conduits. The study present a comprehensive framework comprising: (1) a scalable RIS phase-shift optimization algorithm based on deep reinforcement learning with graph neural network state encoding, (2) a multi-RIS cooperative beamforming protocol for simultaneous multi-device charging, and (3) an energy-aware network slicing mechanism that dynamically partitions RIS resources across heterogeneous IoT service classes. Through large-scale simulation (10,000-node networks) and hardware-in-the-loop emulation using a 256-element programmable metasurface prototype, we demonstrate that the proposed architecture achieves 68.3% end-to-end WPT efficiency in NLoS scenarios (a 340% improvement over conventional WPT), supports concurrent charging of 500+ devices with Jain's fairness index exceeding 0.91, and reduces total cost of ownership by 71% compared to battery-powered baselines over a 10-year operational horizon. These findings establish RIS-enhanced WPT as a transformative enabler for sustainable massive IoT, providing a reproducible pathway toward truly perpetual, maintenance-free sensor networks.



Keywords

reconfigurable intelligent surfaces, wireless power transfer, massive IoT, metasurface, deep reinforcement learning, graph neural networks, sustainable sensor networks, smart radio environment, energy harvesting



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