LI2: A New Learning-Based Approach to Timely Monitoring of Points-of-Interest With UAV

作者：Ziyao Huang, Weiwei Wu, Kui Wu, Hang Yuan, Chenchen Fu, Feng Shan, Jianping Wang, Junzhou Luo · 发表于：IEEE Transactions on Mobile Computing · 年份：2024 · DOI：10.1109/tmc.2024.3461708 · 被引用次数：6 · 研究领域：Target Tracking and Data Fusion in Sensor Networks、Infrared Target Detection Methodologies、Robotics and Sensor-Based Localization

Unmanned aerial vehicles (UAVs) play a critical role in disaster response, swiftly gathering information from various points-of-interest (PoIs) across extensive areas. The freshness of this information is measured by the age of information (AoI), representing the time since the latest information acquisition of a specific PoI. However, devising AoI-minimizing routes for UAVs in obstructed post-disaster environments poses unique challenges that have yet to be fully overcome. Obstacles, like post-disaster barriers, can impede direct flight paths between PoIs, and limited battery life requires energy-conscious route planning. Additionally, existing solutions fail to universally minimize varying data freshness requirements. This research addresses the AoI-driven UAV travel problem, seeking to establish periodic routes that optimize AoI metrics while considering energy and general graph constraints. We develop a learning-based algorithm to enhance the current route iteratively, utilizing guidance from a deep reinforcement learning (DRL) agent and executing a series of operations to potentially decrease AoI while adhering to topological and energy constraints. The algorithm is validated on real post-disaster datasets, demonstrating significant improvements in various AoI metrics compared to other learning-based approaches. Furthermore, our algorithm outperforms approximation algorithms and can approach the global optimum when tailored to existing AoI-minimizing problems.