Survey on Worldwide Implementation of Remote Identification and Discussion on Drone Identification in China

He, Zihe; Tan, Tian Wen

doi:10.1109/iccasit53235.2021.9633559

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…Moreover, this study presents the current attacks and threats affecting Remote ID and certain emerging research challenges to mitigate the aforementioned privacy issues, and discover future development directions. Previous studies on the privacy aspects of Internet of Drones (IoD), such as [10], [14], [15], [16], [17], have only focused on a limited number of vulnerabilities owing to Remote ID; an overview of potential defense strategies or countermeasures has not been provided. Therefore, an extensive study specifically designed to address the privacy concerns related to Remote ID is currently lacking.…”

Section: A Our Contributionsmentioning

confidence: 99%

Privacy-Aware Remote Identification for Unmanned Aerial Vehicles: Current Solutions, Potential Threats, and Future Directions

Tedeschi

Nuaimi

Awad

et al. 2024

IEEE Trans. Ind. Inf.

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The Federal Aviation Administration (FAA) recently introduced a new standard, namely, remote identification, to improve accountability for Unmanned Aerial Vehicles (UAVs) operations. This rule requires UAV operators to broadcast messages revealing sensitive data, such as identity and location on the wireless channel. However, this leads to security and privacy concerns among UAV operators. Unauthorized parties may easily discover the location and identity of a UAV flying in a specific area and launch attacks on it such as using wireless jamming or tracking its activity. This review investigates and systematizes the main weaknesses affecting the Remote ID capability required of modern UAVs, and the approaches through which attackers can exploit these weaknesses to disrupt safety and accountability. Moreover, this paper analyzes current solutions that mitigate privacy issues associated with Remote ID. Finally, we identify multiple challenges that require to be addressed by both industry and academia, and we propose future research directions to improve the security and privacy of UAVs.

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Section: A Our Contributionsmentioning

confidence: 99%

Privacy-Aware Remote Identification for Unmanned Aerial Vehicles: Current Solutions, Potential Threats, and Future Directions

Tedeschi

Nuaimi

Awad

et al. 2024

IEEE Trans. Ind. Inf.

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“…As a giant industrialized country, China has shown limitless interest in commercial UAV applications [16]. As a result, the Civil Aviation Administration of China (CAAC) released four rulemakings to regulate UAVs in the urban environment.…”

Section: Chinamentioning

confidence: 99%

“…The other one, as presented in Figure 9b, is based on the connection of the UAV with Air Traffic services (ATS) system via the Internet like 4G/5G. The future Remote ID systems will undoubtedly be based on both methods instead of just one method because the two methods are complementary [16], [61]. For example, ScaleFlyt Remote ID offers direct broadcast and network channels (compliant with ASTM and ASD -STAN) and provides secure communication channels based on cryptography in tamper-proof embedded eSIM [62].…”

Section: Technology and Industry A Overview Of The Rid-technologymentioning

confidence: 99%

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Unmanned Aerial Vehicles’ Remote Identification: A Tutorial and Survey

et al. 2022

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UAV remote identification is an emerging technology that allows ground observers to identify a drone in the airspace and obtain information about it and its operator. The goal is to enhance safe operation over people and at night and protect public privacy. Two modes are known for remote identification: broadcast-based and network-based. Although the technical implementation of both modes seems to be straightforward, remote identification is challenging because it includes multiple agents that follow different interests such as safety, security, privacy, and businesses. Currently, enormous efforts for regulation, standardization, design, implementation, and testing are being made to put this technology forward. This paper aims to outline the landscape of these activities as a survey and tutorial to inform regulators, standardization organizations, industry, and researchers about the state of the art in this technology and to highlight its opportunities and challenges.

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“…This article focuses on drone detection through RF spectrum monitoring [22]. It presents a comprehensive and advanced approach to detect and decode Drone ID signals [23]. To assess the effectiveness of the proposed system, a measurement campaign was carried out over a larger area, involving three drones: DJI Mavic Air, DJI Mavic 2 Pro, and DJI Mavic 3, equipped with RF Drone ID signals.…”

Section: Introductionmentioning

confidence: 99%

Drone Detection and Tracking Using RF Identification Signals

Aouladhadj,

Kpre,

Deniau

et al. 2023

Sensors

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The market for unmanned aerial systems (UASs) has grown considerably worldwide, but their ability to transmit sensitive information poses a threat to public safety. To counter these threats, authorities, and anti-drone organizations are ensuring that UASs comply with regulations, focusing on strategies to mitigate the risks associated with malicious drones. This study presents a technique for detecting drone models using identification (ID) tags in radio frequency (RF) signals, enabling the extraction of real-time telemetry data through the decoding of Drone ID packets. The system, implemented with a development board, facilitates efficient drone tracking. The results of a measurement campaign performance evaluation include maximum detection distances of 1.3 km for the Mavic Air, 1.5 km for the Mavic 3, and 3.7 km for the Mavic 2 Pro. The system accurately estimates a drone’s 2D position, altitude, and speed in real time. Thanks to the decoding of telemetry packets, the system demonstrates promising accuracy, with worst-case distances between estimated and actual drone positions of 35 m for the Mavic 2 Pro, 17 m for the Mavic Air, and 15 m for the Mavic 3. In addition, there is a relative error of 14% for altitude measurements and 7% for speed measurements. The reaction times calculated to secure a vulnerable site within a 200 m radius are 1.83 min (Mavic Air), 1.03 min (Mavic 3), and 2.92 min (Mavic 2 Pro). This system is proving effective in addressing emerging concerns about drone-related threats, helping to improve public safety and security.

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Survey on Worldwide Implementation of Remote Identification and Discussion on Drone Identification in China

Cited by 7 publications

References 2 publications

Privacy-Aware Remote Identification for Unmanned Aerial Vehicles: Current Solutions, Potential Threats, and Future Directions

Privacy-Aware Remote Identification for Unmanned Aerial Vehicles: Current Solutions, Potential Threats, and Future Directions

Unmanned Aerial Vehicles’ Remote Identification: A Tutorial and Survey

Drone Detection and Tracking Using RF Identification Signals

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