Network Resource Optimization for ML-Based UAV Condition Monitoring with Vibration Analysis

ACCEPTED IN: IEEE NETWORKING LETTERS 1 Network Resource Optimization for ML-Based UA V Condition Monitoring with Vibration Analysis Alexandre Gemayel, Dimitrios Michael Manias, and Abdallah Shami Abstract --As smart cities begin to materialize, the role of Unmanned Aerial V ehicles (UA Vs) and their reliability becomes increasingly important. One aspect of reliability relates to Condition Monitoring (CM), where Machine Learning (ML) models are leveraged to identify abnormal and adverse conditions. Given the resource-constrained nature of next-generation edge networks, the utilization of precious network resources must be minimized. This work explores the optimization of network resources for ML-based UA V CM frameworks. The developed framework uses experimental data and varies the feature extraction aggregation interval to optimize ML model selection. Additionally, by leveraging dimensionality reduction techniques, there is a 99.9% reduction in network resource consumption. I NTRODUCTION E MERGING Unmanned Aerial V ehicle (UA V) applications, such as Smart Cities, have highlighted the necessity of real-time Condition Monitoring (CM) through Anomaly Detection (AD) and health analytics to ensure operational safety and integrity [1].