An Amateur Drone Surveillance System Based on the Cognitive Internet of Things

Guoru Ding, Qihui Wu, Linyuan Zhang, Yun Lin, Theodoros A. Tsiftsis, Yu Dong Yao

Research output: Contribution to journalArticlepeer-review

143 Citations (Scopus)


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 has become a very important but largely unexplored topic. In this article, we first present a brief survey to show the stateof-the-art studies on amateur drone surveillance. Then we propose a vision, named Dragnet, tailoring the recently emerging Cognitive Internet of Things framework for amateur drone surveillance. Next, we discuss the key enabling techniques for Dragnet in detail, accompanied by 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.

Original languageEnglish
Article number8255734
Pages (from-to)29-35
Number of pages7
JournalIEEE Communications Magazine
Issue number1
Publication statusPublished - Jan 2018

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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