Amateur Drone Monitoring: State-of-the-Art Architectures, Key Enabling Technologies, and Future Research Directions

Abstract

The unmanned air-vehicle (UAV) or mini-drones equipped with sensors are becoming increasingly popular for various commercial, industrial, and public-safety applications. However, drones with uncontrolled deployment poses challenges for highly security-sensitive areas such as President house, nuclear plants, and commercial areas because they can be used unlawfully. In this article, to cope with security-sensitive challenges, we propose point-to-point and flying ad-hoc network (FANET) architectures to assist the efficient deployment of monitoring drones (MDr). To capture amateur drone (ADr), MDr must have the capability to efficiently and timely detect, track, jam, and hunt the ADr. We discuss the capabilities of the existing detection, tracking, localization, and routing schemes and also present the limitations in these schemes as further research challenges. Moreover, the future challenges related to co-channel interference, channel model design, and cooperative schemes are discussed. Our findings indicate that MDr deployment is necessary for caring of ADr, and intensive research and development is required to fill the gaps in the existing technologies.

Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference

“…However, due to its limited detection capability it is not recommended in highly sensitive areas, as the presence of multiple drones goes undetected. In practical scenario, the existence of multiple drone in the restricted area is highly probable, the proposed scheme in [21] employs the point to point detection mechanism, which is capable of detecting single drone, but fail to detect the existence of multiple drone. Surveillance drone based AmDr detection is performed in [22], where the authors developed a technique based on strength of the radio frequency (RF) signals and modula-tion classification of the signals.…”

“…Multiple UVAs detection [21] 2018 Ad-hoc network architectures No [22] 2017 RF signal strength No [23] 2017 Cognitive Internet of Things No [24] 2018 Image processing No [25] 2017 Image processing No [26] 2018 Machine learning No [27] 2017 Machine Learning No [28] 2016 Radar technology No [29] 2016 RF based experimental system No [30] 2018 Radio access network No [31] 2016 Sound correlation No [32] 2017 Video signal processing No [33] 2018 Hidden Markov model No In this paper, a more practical approach of the AmDr detection is proposed. The proposed approach is a combination of the supervised and the un-supervised machine learning techniques.…”

Routing Schemes in FANETs: A Survey

“…Table 1 summarizes papers conducting surveys on FANETs, covering various foci, which are self-explanatory given examples, as follows: Open issues, such as enhancing QoS and addressing the dynamicity of network topology. Axel et al [2] 2010 × × × Bauer et al [28] 2011 × × × × Neji et al [29] 2013 × × × × × Bekmezci et al [7] 2013 × × × × × × × × × Ozgur et al [18] 2013 × × × × × × Xie et al [30] 2014 × × × × × × Naser et al [31] 2016 × × × × × × × Gupta et al [32] 2016 × × × × × × × × [34] 2017 × × × × × × Omar et al [35] 2017 × × × × × × × × Zeeshan et al [36] 2018 × × × × × × Khan et al [37] 2018 × × × × × × × Antonio et al [38] 2018 × × × × × × Kaur et al [39] 2018 × × × × × × × Otto et al [40] 2018 × × × × × × × × Jinfang et al [41] 2018 × × × × × × Our paper 2019 × × × × × × × × × × × × × A cross × indicates that the option, which is represented by the column, applies to the reference, which is represented by the row.…”

Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network

“…Kaleem and Rehmani [25] also have successfully applied an SDN architecture for FANET management. In this work, the authors present an amateur drone monitoring and tracking service as they might be a threat to citizens in a variety of scenarios.…”