Machine learning-enabled UAV Networking
We leverage machine learning to improve the quality of service for the 5G-enabled Unmanned Aerial Vehicle (UAV) networking which is important to increase the scale of UAV swarms.
5G-enabled Optimal Bi-Throughput For UAS Swarm Networking
5G_enabled_Optimal_Bi_Throughput_for_UAS_Swarm_Networking__Prensentation_.pdfThe emerging 5G New Radio (5G NR) stimulates the evolution of applications in many fields. Compared with 4G, 5G NR can provide much more reliable, efficient, flexible networking access services to mobile devices which can escalate the quality of services (QoS) for the users remarkably. The advantages of affordability, easy assembling and quick driving, allow the deployment of Unmanned Aircraft System (UAS) on a large scale, and provide the elevation for the industrial and the civilian implementations ubiquitously.
Knowledge Distillation based GPS Spoofing Detection for Small UAS
As a core component of small Unmanned Aerial Vehicle (UAV), GPS is playing a critical role to provide localization for UAV navigation. UAV is an important factor in the large scale deployment of the Internet of Things (IoT) and Cyber-Physical Systems (CPS). However, GPS is vulnerable to spoofing attacks that can mislead UAV to fly to a sensitive area and make threats to public safety and private security. The conventional spoofing detection methods need too much overhead which stops an efficient classifier to work in a computation-constrained UAV and provides an efficient response to the attacks. In this paper, we proposed a novel approach to implement a lightweight classifier in UAV system so that GPS spoofing attacks can be detected from a long distance. With Recurrent Neural Networks (RNN), we proposed a novel classifier on the ground control stations and then we transferred it into the control system of UAV with knowledge distillation in a compact size. The experimental results show that our lightweight classifier runs in UAV systems reliably and it can achieve a good accuracy of GPS spoofing detection.
