通过优化特征聚合与降维，大幅降低无人机状态监测的网络开销。
Network Resource Optimization for ML-Based UAV Condition Monitoring with Vibration Analysis
- 调整特征提取间隔，动态选择最优机器学习模型
- 结合降维技术，网络资源消耗降低99.9%
- 适合边缘计算环境下轻量化无人机监测应用
随着智慧城市逐步落地，无人机（UAV）及其可靠性愈发关键。状态监测（CM）是保障可靠性的重要环节，当前常借助机器学习（ML）模型识别异常工况。然而，下一代边缘网络资源受限，需最小化网络资源占用。本文针对基于机器学习的无人机状态监测框架，提出网络资源优化方法：利用实验数据，调整特征提取聚合间隔以优化模型选择；同时采用降维技术，实现网络资源消耗降低99.9%。
原文摘要 · Abstract (English)
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.
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