Fountain Code Enabled ADS-B for Aviation Security and Safety Enhancement

Wang, Jian; Liu, Yongxin; Amal, Alfaidi; Song, Houbing; Stansbury, Richard S.; Yuan, Jiawei; Yang, Tianyu

doi:10.1109/pccc.2018.8711001

Cited by 8 publications

(4 citation statements)

References 32 publications

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“…AODV is decentralized, reactive, and has less overhead for management, which is robust to the dynamic topology. Nevertheless, the main drawbacks of AODV are the consumption of path discovery and the local optimization for routing generation [40,41]. In contrast, OLSR is a centralized algorithm that could achieve global optimization of routing generation with the sacrifice of overhead [42].…”

Section: Introductionmentioning

confidence: 99%

Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems

et al. 2022

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The emerging concept of drone swarms creates new opportunities with major societal implications. However, future drone swarm applications and services pose new communications and sensing challenges, particularly for collaborative tasks. To address these challenges, in this paper, we integrate sensor arrays and communication to propose a mathematical model to route a collection of autonomous unmanned aerial systems (AUAS), a so-called drone swarm or AUAS swarm, without having a base station of communication but communicating with each other using multiple spatio-temporal data. The theories of structured matrices, concepts in multi-beam beamforming, and sensor arrays are utilized to propose a swarm routing algorithm. We address the routing algorithm’s computational and arithmetic complexities, precision, and reliability. We measure bit-error-rate (BER) based on the number of elements in sensor arrays and beamformed output of the members of the swarm to authenticate and secure the routing for the decentralized AUAS networking. The proposed model has the potential to enable future drone swarm applications and services. Finally, we discuss future work on obtaining a machine-learning-based low-cost drone swarm routing algorithm.

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Section: Introductionmentioning

confidence: 99%

Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems

et al. 2022

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“…But, it was pointed out in [21] that the cost of the AntNet algorithm increases when searching for the shortest path in non-ad hoc environments, and degradation as the network size increases. It is vital to note that the OLSR algorithms achieve global optimization in routing generation but incur a higher overhead [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Multi-Beam Beamforming-Based ML Algorithm to Optimize the Routing of Drone Swarms

Myers,

Perera,

McLewee

et al. 2024

Drones

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The advancement of wireless networking has significantly enhanced beamforming capabilities in Autonomous Unmanned Aerial Systems (AUAS). This paper presents a simple and efficient classical algorithm to route a collection of AUAS or drone swarms extending our previous work on AUAS. The algorithm is based on the sparse factorization of frequency Vandermonde matrices that correspond to each drone, and its entries are determined through spatiotemporal data of drones in the AUAS. The algorithm relies on multibeam beamforming, making it suitable for large-scale AUAS networking in wireless communications. We show a reduction in the arithmetic and time complexities of the algorithm through theoretical and numerical results. Finally, we also present an ML-based AUAS routing algorithm using the classical AUAS algorithm and feed-forward neural networks. We compare the beamformed signals of the ML-based AUAS routing algorithm with the ground truth signals to minimize the error between them. The numerical error results show that the ML-based AUAS routing algorithm enhances the accuracy of the routing. This error, along with the numerical and theoretical results for over 100 drones, provides the basis for the scalability of the proposed ML-based AUAS algorithms for large-scale deployments.

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“…We first propose a blockchain enabled framework for the authentication of flight plans and exchanging secret keys between UAS and ATM infrastructures. We then provide an enhanced ADS-B protocol using fountain code [22], [23] for the reliable transmission of authentication information in capacity-limited wireless channels. Our contributions are as follows:…”

Section: Introductionmentioning

confidence: 99%

Blockchain Enabled Secure Authentication for Unmanned Aircraft Systems

Liu¹,

Wang²,

Chen³

et al. 2021

Preprint

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The integration of air and ground smart vehicles is becoming a new paradigm of future transportation. A decent number of smart unmanned vehicles or UAS will be sharing the national airspace for various purposes, such as express delivery, surveillance, etc. However, the proliferation of UAS also brings challenges considering the safe integration of them into the current Air Traffic Management (ATM) systems. Especially when the current Automatic Dependent Surveillance Broadcasting (ADS-B) systems do not have message authentication mechanisms, it can not distinguish whether an authorized UAS is using the corresponding airspace. In this paper, we aim to address these practical challenges in two folds. We first use blockchain to provide a secure authentication platform for flight plan approval and sharing between the existing ATM facilities. We then use the fountain code to encode the authentication payloads and adapt them into the de facto communication protocol of ATM. This maintains backward compatibility and ensures the verification success rate under the noisy broadcasting channel. We simulate the realistic wireless communication scenarios and theoretically prove that our proposed authentication framework is with low latency and highly compatible with existing ATM communication protocols.

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Fountain Code Enabled ADS-B for Aviation Security and Safety Enhancement

Cited by 8 publications

References 32 publications

Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems

Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems

Multi-Beam Beamforming-Based ML Algorithm to Optimize the Routing of Drone Swarms

Blockchain Enabled Secure Authentication for Unmanned Aircraft Systems

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