An Amateur Drone Surveillance System Based on Cognitive Internet of Things

TL;DR

This paper introduces Dragnet, a novel cognitive IoT-based system for amateur drone surveillance, addressing safety, security, and privacy concerns through detection and classification techniques.

Contribution

It presents a new surveillance framework tailored for amateur drones using cognitive IoT, including key techniques, challenges, and a case study on drone detection.

Findings

Effective detection and classification of authorized vs. unauthorized drones

Identification of technical challenges in drone surveillance

Demonstration of Dragnet's application in real event scenarios

Abstract

Drones, also known as mini-unmanned aerial vehicles, have attracted increasing attention due to their boundless applications in communications, photography, agriculture, surveillance and numerous public services. However, the deployment of amateur drones poses various safety, security and privacy threats. To cope with these challenges, amateur drone surveillance becomes a very important but largely unexplored topic. In this article, we firstly present a brief survey to show the state-of-the-art studies on amateur drone surveillance. Then, we propose a vision, named Dragnet, by tailoring the recent emerging cognitive internet of things framework for amateur drone surveillance. Next, we discuss the key enabling techniques for Dragnet in details, accompanied with the technical challenges and open issues. Furthermore, we provide an exemplary case study on the detection and classification of authorized and unauthorized amateur drones, where, for example, an important event is being held and only authorized drones are allowed to fly over.