Network Resource Optimization for ML-Based UAV Condition Monitoring with Vibration Analysis
This work addresses resource optimization for UAV reliability in smart cities, but it appears incremental as it applies existing techniques to a specific domain.
The paper tackled the problem of minimizing network resource consumption for machine learning-based condition monitoring of UAVs in edge networks, achieving a 99.9% reduction in resource usage through feature extraction and dimensionality reduction.
As smart cities begin to materialize, the role of Unmanned Aerial Vehicles (UAVs) 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 UAV 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.