Date of Award

January 2023

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Prakash Ranganathan

Abstract

Automatic Dependent Surveillance-Broadcast (ADS-B) is an alternative technology adopted by the FAA instead of ground radar to enhance accurate navigation by relying on GPS satellites for precise aircraft position information. Factors such as jamming, multipath fading, and solar activities influence GPS data integrity issues, causing dropouts or missing data, thus affecting flight safety and navigational accuracy. To mitigate such potential GPS dropout-related incidents, there is a need for robust data-driven models. This thesis focuses on multiple studies: (1) investigate five distinct machine learning (ML) models to impute missing data on ADS-B/GPS information; (2) design a federated learning (FL) framework for aviation network data; and (3) conduct a benchmarking study to validate multiple quality attributes for the proposed aviation Fed-CPS framework. Preliminary results indicate (a) k-NN yields better accuracy over other ML models (Bayesian Ridge, Random Forest, AdaBoost, Extra Tree, and k-NN) even at the highest missing rate of 30%; (b) deployment of LSTM and k-Means in a federated setting indicate that LSTM results in both MAPE and computation run-time savings. Specifically, LSTM shows (i) performance-per-dollar of 1.5 times (client) and 0.5 times (server) than k-Means and (ii) energy-efficiency-per-watt of 1.5 times (client) and 0.5 times (server) than k-Means.

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