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

2025-02-21

AI Summary

To address UAV vibration state monitoring in resource-constrained edge networks for smart cities, this paper proposes a network–algorithm co-optimization framework. The method jointly models dynamic feature extraction interval adaptation and PCA-based dimensionality reduction—first of its kind—to simultaneously compress transmission data volume and computational overhead. It integrates time-series vibration signal analysis, lightweight ML model selection, and fine-grained network traffic modeling. Evaluated on real-world experimental data, the approach achieves 99.9% bandwidth reduction while maintaining high anomaly detection accuracy. This framework significantly enhances the real-time performance and deployability of ML-driven UAV health monitoring at the edge, establishing a novel paradigm for low-power, high-reliability edge intelligence.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Learning & Optimization for CV
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Optimize network resources
• ML-based UAV condition monitoring
• Reduce network consumption significantly

Innovation

• Optimizes network resource usage
• Utilizes ML for UAV monitoring
• Applies dimensionality reduction techniques