A Crowdsensing-based Cyber-physical System for Drone Surveillance Using Random Finite Set Theory

Abstract

Given the popularity of drones for leisure, commercial, and government (e.g., military) usage, there is increasing focus on drone regulation. For example, how can the city council or some government agency detect and track drones more efficiently and effectively, say, in a city, to ensure that the drones are not engaged in unauthorized activities? Therefore, in this article, we propose a crowdsensing-based cyber-physical system for drone surveillance. The proposed system, CSDrone, utilizes surveillance data captured and sent from citizens’ mobile devices (e.g., Android and iOS devices, as well as other image or video capturing devices) to facilitate jointly drone detection and tracking. Our system uses random finite set (RFS) theory and RFS-based Bayesian filter. We also evaluate CSDrone’s effectiveness in drone detection and tracking. The findings demonstrate that in comparison to existing drone surveillance systems, CSDrone has a lower cost, and is more flexible and scalable.