URNFresh: Age-of-infomation-based 60 GHz UAV relay networks for video surveillance in linear environments

Abstract

In recent years, video surveillance has been widely deployed and utilized, with linear deployment environments such as roads and rivers being very common. With the rapid development and widespread application of 60 GHz communication and unmanned aerial vehicle (UAV) technologies, 60 GHz UAV relay networks have become an ideal solution for high-rate data collection in video surveillance. In this network scenario, the collaborative scheduling of multiple UAVs has become a key issue. However, the existing scheduling schemes are usually designed for two-dimensional or three-dimensional scenarios, lacking relevant considerations and designs for the characteristics of one-dimensional linear scenarios. In addition, these methods rarely consider ensuring data freshness and the age-of-information (AoI) metric to meet the needs of latency-sensitive applications. To this end, we consider the 60 GHz UAV relay network for video surveillance, and investigate the AoI-based multi-UAV collaborative scheduling mechanism in linear environments. Firstly, We formulate the energy-storage-limited and AoI-guaranteed Multi-UAV scheduling problem, which aims to minimize the average cumulative AoI, while considering the constraints of their energy and data storage capacity. Then, we propose the hierarchical reinforcement learning-based multi-UAV collaborative scheduling mechanism called URNFresh, and design corresponding strategies for option selection and fine-grained action selection in aspects such as flight control, data collection, data offloading, and battery replacement. Finally, we conduct simulation experiments to evaluate the performance of URNFresh mechanism. Experimental results demonstrate that the proposed solution outperforms traditional reinforcement learning approaches, and achieves a significant improvement in average cumulative AoI.