Maximizing Harvested Energy in mmWave IoT Networks: A Tractable Blockage-Aware RIS Framework

This paper presents a novel analytical framework for energy harvesting (EH) in Reconfigurable intelligent surface (RIS) assisted mmWave IoT networks under realistic blockage conditions. By leveraging stochastic geometry, we develop a mathematical model that explicitly distinguishes between Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) propagation based on blockage density. We derive closed-form expressions for the average harvested energy, revealing that RIS deployment significantly mitigates the severe propagation losses caused by urban blockages. Furthermore, we formulate an optimal joint deployment strategy for base stations (BSs) and RISs under budget constraints, proposing an efficient bisection-based algorithm to maximize energy availability. Extensive numerical results validate our analytical framework and demonstrate that strategic RIS deployment is essential for ensuring energy sustainability in dense, blockage-prone mmWave environments.