Monitoring of UAVs Through Remote Identification and Mobile Crowd Sensing

This paper presents a framework for remote monitoring of unmanned aerial vehicles (UAVs) developed specifically for urban environments through crowd sensing. This system is built around a centralized control mechanism that manages UAV traffic to reduce the risks of airspace congestion and sudden accidents. The UAVs periodically broadcast their remote identifications (RIDs), which are received by ground observers on their mobile devices through dedicated applications. These RIDs contain multiple information, including the drone's ID, location, altitude, velocity, and a timestamp. The ground observers then forward the received RIDs to the surveillance station after appending their coordinates, ID, and a time stamp. A key component of our approach is the incorporation of signal quality metrics - path loss, shadowing, and received signal strength (RSS) - along with distance considerations in the activation process of ground observers for RID message forwarding. To address potential data congestion and incentive overuse in densely populated areas, our model uses a predictive strategy for dynamic observer activation, ensuring that the surveillance system processes an optimal number of RID reports efficiently. This comprehensive consideration of both signal integrity and spatial proximity significantly improves the detection and monitoring precision of UAVs, increasing the system's ability to effectively monitor UAV activities while conserving resources in urban environments.