Edge Compute Architecture for Localized Air Traffic Control Resilience and Data Ingestion Filtering
Keywords:
ADS-B ingestion filtering, Air traffic control, Edge computing, Embedded resilience, Graceful degradation, Information freshness, Single board computers, Synthetic simulationAbstract
Air traffic management facilities at the edge of the network, such as radar sites and towers, depend on upstream links that can fail and then return under heavy backlog. The literature describes edge computing and ADS-B channel behaviour, but it gives little guidance on how a low-cost node should divide limited resources between ingestion filtering, local information mirroring, and a minimal fallback display, or on how a site should rejoin a recovering network. This study specifies a three-stage edge architecture for non-core services and evaluates it in a synthetic simulation. Stage 1 decodes and filters ADS-B and radar streams and releases stored data at a paced rate. Stage 2 mirrors weather and notice records for read-only local access. Stage 3 draws a standby track display from local ADS-B reception only. Message rates and equipage follow published ADS-B measurements; loss, interference, link, weather, and node parameters are stated assumptions. Thirty seeded replicates of a 600 s link outage were compared with an unfiltered forwarding baseline. The edge design removed 77.9% of raw bytes before forwarding (standard deviation 0.04 points), reduced the filtered backlog from 8.76 MB to 1.93 MB, and held peak post-outage upstream use to 26.3% of the assumed 2,000 kbps link, whereas the unfiltered baseline saturated it for 35.0 s. Local request availability was 1.000 under the assumed fault model. All equipped aircraft were tracked via local ADS-B throughout the outage (FTC 1.000). Variant runs showed that removing deduplication lowered DIFI from 0.779 to 0.532, and a 60-minute outage exhausts a 3,600 s METAR validity window. No hardware was measured. The results are synthetic and conditional, but the listed parameters, seed range, and checklist allow other groups to rerun the study, test it against measured site data, and adapt the design.
References
M. Strohmeier, M. Schäfer, V. Lenders and I. Martinovic, “Realities and challenges of nextgen air traffic management: the case of ADS-B,” in IEEE Communications Magazine, vol. 52, no. 5, pp. 111–118, May 2014.
M. Schäfer, M. Strohmeier, V. Lenders, I. Martinovic and M. Wilhelm, “Bringing up OpenSky: A large-scale ADS-B sensor network for research,” IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks, Berlin, Germany, 2014, pp. 83–94.
W. Shi, J. Cao, Q. Zhang, Y. Li and L. Xu, “Edge computing: Vision and challenges,” in IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, Oct. 2016.
J. Pan and J. McElhannon, “Future edge cloud and edge computing for Internet of Things applications,” in IEEE Internet of Things Journal, vol. 5, no. 1, pp. 439–449, Feb. 2018.
K. Cao, Y. Liu, G. Meng and Q. Sun, “An overview on edge computing research,” in IEEE Access, vol. 8, pp. 85714–85728, 2020.
J. Qadir, B. Sainz-De-Abajo, A. Khan, B. García-Zapirain, I. De La Torre-Díez and H. Mahmood, “Towards mobile edge computing: Taxonomy, challenges, applications and future realms,” in IEEE Access, vol. 8, pp. 189129–189162, 2020.
C. Huang, I. Petrunin and A. Tsourdos, “Distributed trajectory management for urban air mobility operations with ground-based edge intelligence,” 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC), Barcelona, Spain, 2023, pp. 1–8.
T. Edwards and P. Lee, “Designing graceful degradation into complex systems: Identification of causes of degradation, interactions, and mitigation of degradation in air traffic control,” in Proceedings of the AIAA Aviation Forum, 2018.
V. Mofolo, “Towards a resilience framework for securing ADS-B surveillance systems in air traffic management,” Proceedings of the 25th European Conference on Cyber Warfare and Security (ECCWS 2026), 2026, pp. 1005–1008.
J. Sun, H. Vû, J. Ellerbroek and J. M. Hoekstra, “pyModeS: Decoding Mode-S surveillance data for open air transportation research,” in IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 7, pp. 2777–2786, Jul. 2020.
B. Varghese et al., “A survey on edge performance benchmarking,” ACM Computing Surveys, vol. 54, no. 3, pp. 1–33, Apr. 2022.
A. Hassan et al., “Performance evaluation of Raspberry Pi as an IoT edge signal processing device for a real-time flash flood forecasting system,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 10, 2022.
E. Gamess and S. Hernandez, “Performance evaluation of different Raspberry Pi models for a broad spectrum of interests,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 2, 2022.
H. Knoche and H. Eichelberger, “The Raspberry Pi: A platform for replicable performance benchmarks?” Softwaretechnik-Trends, vol. 37, no. 3, pp. 14–16, 2017.
R. D. Yates, Y. Sun, D. R. Brown, S. K. Kaul, E. Modiano and S. Ulukus, “Age of information: An introduction and survey,” in IEEE Journal on Selected Areas in Communications, vol. 39, no. 5, pp. 1183–1210, May 2021.
