Energy-efficient UAV communication via relay-RIS-aided SWIPT for disaster-resilient networks: Performance analysis and optimization

Abstract

The deployment of internet-of-things (IoT) devices in disaster-prone regions necessitates robust and energy-efficient wireless connectivity to support emergency monitoring, search-and-rescue, and post-disaster recovery operations. This paper investigates the integration of uncrewed aerial vehicle (UAV) with reconfigurable intelligent surfaces (RIS) and relay-assisted networks to enhance IoT communication performance. We propose a system model where a UAV operates in an integrated relay-RIS mode, facilitating simultaneous wireless information and power transfer (SWIPT). To extend the UAV’s operational duration, energy harvesting (EH) capabilities are integrated into its design. At the destination, signal reception is enhanced through the use of selection combining (SC) and maximal ratio combining (MRC) techniques. System performance is evaluated using critical metrics such as outage probability (OP), average symbol error rate (ASER), outage capacity (OC), and ergodic capacity (EC) are analyzed under Nakagami-m fading channels. Closed-form expressions for OP, ASER, OC, and EC are derived and validated through Monte-Carlo simulations, demonstrating significant reliability gains over conventional standalone IoT systems. Furthermore, three optimization problems are formulated with the objectives of maximizing the achievable data rate, the UAV’s stored energy, and energy efficiency (EE) subject to quality-of-service (QoS) constraints. Closed-form solutions are derived by applying the Karush-Kuhn-Tucker (KKT) optimality conditions for rate and UAV’s stored energy. The EE optimization is solved by Dinkelbach transform (DT) and quadratic transform (QT). The proposed framework offers a robust foundation for deploying UAV assisted relay-RIS systems, enabling efficient and resilient communications in dynamic environments such as smart cities, emergency response, and remote connectivity. © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.