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.

翻译：低空无人机已成为物联网的关键赋能者，通过提供增强的覆盖范围、改善的连接性以及对偏远地区的接入能力。限制其运行能力的一个关键挑战在于空中平台与地面传感器的能量约束。本文探讨无线激光能量传输作为无人机辅助物联网网络中可持续能量供给的变革性解决方案。我们首先系统性地研究无线激光能量传输的基本原理并分析其比较优势。随后，我们提出三种系统集成操作范式，识别关键挑战，并讨论相应的潜在解决方案。在案例研究中，我们提出一种多智能体强化学习框架，以应对支持无线激光能量传输的无人机辅助物联网数据收集中面临的协调与优化挑战。仿真结果表明，该框架显著提升了能量可持续性与数据新鲜度。最后，我们探讨了若干未来研究方向。