Hashemi, Seyed Mohammad, Hashemi, Seyed Ali, Botez, Ruxandra Mihaela et Ghazi, Georges.
2023.
« A novel fault-tolerant air traffic management methodology using autoencoder and P2P blockchain consensus protocol ».
Aerospace, vol. 10, nº 4.
Compte des citations dans Scopus : 9.
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Résumé
This paper presents a methodology for designing a highly reliable Air Traffic Management and Control (ATMC) methodology using Neural Networks and Peer-to-Peer (P2P) blockchain. A novel data-driven algorithm was designed for Aircraft Trajectory Prediction (ATP) based on an Autoencoder architecture. The Autoencoder was considered in this study due to its excellent fault-tolerant ability when the input data provided by the GPS is deficient. After conflict detection, P2P blockchain was used for securely decentralized decision-making. A meta-controller composed of this Autoencoder, and P2P blockchain performed the ATMC task very well. A comprehensive database of trajectories constructed using our UAS-S4 Ehécatl was used for algorithms validation. The accuracy of the ATP was evaluated for a variety of data failures, and the high-performance index confirmed the excellent efficiency of the autoencoder. Aircraft were considered in several local encounter scenarios, and their trajectories were securely managed and controlled using our in-house Smart Contract software developed on the Ethereum platform. The Sharding approach improved the P2P blockchain performance in terms of computational complexity and processing time in real-time operations. Therefore, the probability of conflicts among aircraft in a swarm environment was significantly reduced using our new methodology and algorithm.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Professeur: | Professeur Botez, Ruxandra Ghazi, Georges |
| Affiliation: | Génie des systèmes, Génie des systèmes |
| Date de dépôt: | 30 mai 2023 20:55 |
| Dernière modification: | 31 mai 2023 15:39 |
| URI: | https://espace2.etsmtl.ca/id/eprint/26484 |
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