Wireless Laser Power Transfer for Low-altitude Uncrewed Aerial Vehicle-assisted Internet of Things: Paradigms, Challenges, and Solutions

2025-09-30

AI Summary

To address the critical challenge of energy constraints on aerial platforms and ground sensors in low-altitude unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) networks—which severely limit system sustainability—this paper proposes, for the first time, a wireless laser power transfer (WLPT)-enabled sustainable energy supply paradigm. We innovatively design three WLPT-integrated UAV-IoT system architectures and formulate a multi-agent reinforcement learning framework jointly optimizing energy sustainability and data freshness. This framework coordinates UAV trajectory planning and ground node scheduling. Simulation results demonstrate that the proposed approach significantly improves energy delivery efficiency and information timeliness while extending network lifetime, thereby validating the feasibility and superiority of WLPT in practical low-altitude IoT deployments.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Sustainability of Web economics

Abstract

Low-altitude uncrewed aerial vehicles (UAVs) have become integral enablers for the Internet of Things (IoT) by offering enhanced coverage, improved connectivity and access to remote areas. A critical challenge limiting their operational capacity lies in the energy constraints of both aerial platforms and ground-based sensors. This paper explores WLPT as a transformative solution for sustainable energy provisioning in UAV-assisted IoT networks. We first systematically investigate the fundamental principles of WLPT and analysis the comparative advantages. Then, we introduce three operational paradigms for system integration, identify key challenges, and discuss corresponding potential solutions. In case study, we propose a multi-agent reinforcement learning framework to address the coordination and optimization challenges in WLPT-enabled UAV-assisted IoT data collection. Simulation results demonstrate that our framework significantly improves energy sustainability and data freshness. Finally, we discuss some future directions.

Problem

• Wireless laser power transfer for UAV-assisted IoT networks
• Addressing energy constraints in low-altitude UAV operations
• Optimizing energy sustainability and data freshness coordination

Innovation

• Wireless Laser Power Transfer for UAV energy
• Multi-agent reinforcement learning for coordination
• Enhances energy sustainability and data freshness