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Amateur Drone Surveillance: Applications, Architectures, Enabling Technologies, and Public Safety Issues

Feature Topic


The advancement in communication, networking, computation, and sensing technologies has attracted researchers, hobbyists, and investors to deploy mini-drones, officially called unmanned aerial vehicles (UAVs), due to their enormous applications. Drones have boundless viable applications as well due to their small size and capability to fly without an on-board pilot such as in agriculture, photography, surveillance, and numerous public services. The use of drones for achieving high-speed wireless communication is one of the most significant applications for next-generation communication systems (5G).  Indeed, drone-based communication network offers versatile solutions to provide wireless connectivity for devices without infrastructure coverage due to e.g., severe shadowing by urban or mountainous terrain, or damage to the communications infrastructure caused by natural disasters. But its deployment poses several public safety (PS) threats to national institutions and assets such as nuclear power plants, historical sites, and government leaders’ houses because of drone’s ability to carry the explosive and other destructive chemicals and agents.

In order to cope with these security threats, surveillance drones (SDrs) deployment is required for surveillance, hunting, and jamming of the amateur drone (ADr). The main motivation of deploying SDrs is to keep an eye on the ADr which can lead to serious disasters in cases where no precautionary measures are taken in a timely manner. The SDr architecture should have the capability to self-configure in case of emergency situations without the help of the central ground control station (GCS). The increasing usage of SDr in surveillance of ADr presents some challenges such as robust detection, tracking, intruder localization, and jamming. The accuracy of detection is a basic requirement of the system. In general, the accurate detection is time-consuming. In fact, a precise moving object detection method makes tracking more reliable and faster, and supports correct classification, which is quite important for SDr to be successful. The existing motion detection algorithms have the problems of computational cost and lower robustness. However, because of rapidly changing extrinsic and intrinsic camera parameters such as pan, tilt, translation, rotation, and zooming, algorithms of highest accuracy are required. Moreover, the machine learning and pattern recognition algorithms are required to detect the ADr by using the characteristics of the electromagnetic waves, sound, images that can efficiently detect the ADr.

The next major step after detecting the ADr is its tracking and the localization of the ADr intruder. To accurately estimate the position of the ADr and its intruder, the 3D position estimation algorithms are desired to accurately determine the position of the ADr. The FANET (Flying Ad Hoc Networks) architecture-based deployment and utilization of the commercial frequency bands for SDr also presents the challenges of interference management with the existing system. So, proper spectrum management schemes are desired which can take care of the dynamically changing environment while allocating the spectrum. The last important step after detection, localization, and tracking is the Jamming and hunting of the ADr. For example, the jamming technologies such as by using excess power and global positioning services (GPS) spoofing can generate the high interference to the SDr signal. So, Jamming signal power control algorithm needs to be designed to avoid surrounding SDr jamming. After jamming the ADr, hunting of the ADr should be done by taking care of security of the surrounding environment. That is, ADr in the air should be landed outside the highly sensitive areas. Thus, to get this goal context-aware path-design algorithms are required to safely land the hunted ADr. Hence, the technologies like frequency band recognition, power control, jamming and hunting are required to efficiently detect, control, jam, and hunt the ADr.

The goal of the proposed Feature Topic (FT) is to publish comprehensive original research for all readers of the Magazine regardless of their specialty. The main objective of this FT is to bring most recent advances in amateur drone surveillance network architecture and technologies. Moreover, its goal is to address the challenges related to public safety issues posed by the flying of drone in the No-fly zone. Original research papers are to be solicited in topics of interest including, but not limited to, the following themes on applications, architectures, enabling technologies, and public safety issues with amateur drone surveillance.

  • Innovative P2P and FANET cross-layer architectures and protocols for FANET
  • Radio resource management schemes for surveillance drone
  • Power control schemes for surveillance drone
  • Spectrum sharing and future spectrum requirements for surveillance drones
  • Routing and MAC protocols for drones
  • Cross layer protocols for drones
  • Smart and time-efficient trajectory generation schemes
  • surveillance drone control parameters optimization schemes
  • Pattern recognition-based amateur drone tracking
  • Machine learning algorithms to discriminate amateur drone with moving object
  • Sound and electromagnetic waves detection schemes
  • Thermal imaging-based amateur drone detection scheme
  • Coverage extension schemes for amateur drone detection
  • Signal strength, inertial sensor, and cell tower-based positioning schemes
  • Active and passive object tracking schemes
  • Amateur drone detection and tracking using holographic radars
  • Context-aware localization schemes
  • Energy efficient localization of amateur drone
  • Frequency band recognition technologies for amateur drone jamming
  • Location detection technologies and protocols
  • 3D location detection protocols
  • Disaster resilient location detection protocols
  • Robust location accuracy technology development
  • Multi-hop and relay-based communications
  • Drone classification and identification using data mining techniques
  • Interdisciplinary research for catching amateur drone
  • Testbeds and experimental results of surveillance drone


Zeeshan Kaleem (Corresponding Guest Editor)
COMSATS Institute of Information Technology, Pakistan

Mubashir Husain Rehmani
COMSATS Institute of Information Technology, Pakistan

Ejaz Ahmed
National Institute of Standards and Technology, Gaithersburg, USA

Abbas Jamalipour
University of Sydney, Australia

Joel J. P. C. Rodrigues
National Institute of Telecommunications (Inatel), Brazil

Hassna Moustafa
Intel Corporation, USA

Wael Guibene
Intel Labs Europe, Ireland