A Novel Fault-Tolerant Air Traffic Management Methodology Using Autoencoder and P2P Blockchain Consensus Protocol
Abstract
:1. Introduction
2. Problem Statement
3. Methodologies
3.1. Trajectory Prediction
3.2. Airspace Allocation
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Nomenclature
A | Decoded GPS data |
B | Encoded GPS data |
E | Encoder transfer function |
D | Decoder transfer function |
Number of steps for future trajectory prediction | |
Number of executed agreements | |
Number of aircraft in a flight zone before sharding | |
Number of aircraft in a flight zone after sharding | |
L | Autoencoder loss function |
Number of districts that provide sub-flight zones after sharding | |
Number associated with a block in the chain | |
Q | Autoencoder quality function |
Time during which the aircraft is in step n |
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Specification | Value |
---|---|
Wingspan | 4.2 m |
Wing area | 2.3 m2 |
Total length | 2.5 m |
Mean aerodynamic chord | 0.57 m |
Empty weight | 50 kg |
Maximum take-off weight | 80 kg |
Loitering airspeed | 35 knots |
Maximum speed | 135 knots |
Service ceiling | 15,000 ft |
Operational range | 120 km |
Data Failures | Prediction Accuracy % | Precision | ||
---|---|---|---|---|
LSTM | Autoencoder | LSTM | Autoencoder | |
No failures | 99.5 | 98.7 | 0.01 | 0.01 |
Latitude | 91.6 | 98.1 | 0.04 | 0.03 |
Latitude and heading | 83.2 | 95.3 | 0.06 | 0.04 |
Latitude, altitude, and speed | 58.7 | 91.2 | 0.10 | 0.07 |
Data Failures | Number of Neurons in the Encoder and Decoder | Number of Neurons in the Latent | Prediction Accuracy % | Error Rate % |
---|---|---|---|---|
Latitude | 5 | 4 | 97.8 | 4.2 |
3 | 97.6 | 4.1 | ||
4 | 3 | 98.1 | 3.3 | |
2 | 43.3 | 17 | ||
Latitude and Heading | 5 | 4 | 94.9 | 5.2 |
3 | 94.6 | 6.3 | ||
4 | 3 | 95.3 | 4.4 | |
2 | 39.2 | 16.6 | ||
Latitude, Altitude, and Speed | 5 | 4 | 90.8 | 8.5 |
3 | 90.3 | 9.2 | ||
4 | 3 | 91.2 | 7.1 | |
2 | 32.6 | 19.8 |
Methodology | Number of Sub-Flight Zones | Attacks Imposed? | Error Rate % |
---|---|---|---|
Linear Consensus Protocol (LCP) | 1 | No | 5.3 |
Yes | 83.2 | ||
Smart Contract-based Consensus Protocol (SCCP) | 1 | No | 8.7 |
Yes | 18.6 | ||
Sharding-SCCP | 4 | No | 7.7 |
Yes | 15.4 |
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Hashemi, S.M.; Hashemi, S.A.; Botez, R.M.; Ghazi, G. A Novel Fault-Tolerant Air Traffic Management Methodology Using Autoencoder and P2P Blockchain Consensus Protocol. Aerospace 2023, 10, 357. https://doi.org/10.3390/aerospace10040357
Hashemi SM, Hashemi SA, Botez RM, Ghazi G. A Novel Fault-Tolerant Air Traffic Management Methodology Using Autoencoder and P2P Blockchain Consensus Protocol. Aerospace. 2023; 10(4):357. https://doi.org/10.3390/aerospace10040357
Chicago/Turabian StyleHashemi, Seyed Mohammad, Seyed Ali Hashemi, Ruxandra Mihaela Botez, and Georges Ghazi. 2023. "A Novel Fault-Tolerant Air Traffic Management Methodology Using Autoencoder and P2P Blockchain Consensus Protocol" Aerospace 10, no. 4: 357. https://doi.org/10.3390/aerospace10040357