Network Resource Optimization for ML-Based UAV Condition Monitoring with Vibration Analysis
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866929724841787392 |
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| author | Gemayel, Alexandre Manias, Dimitrios Michael Shami, Abdallah |
| author_facet | Gemayel, Alexandre Manias, Dimitrios Michael Shami, Abdallah |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15491 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Network Resource Optimization for ML-Based UAV Condition Monitoring with Vibration Analysis Gemayel, Alexandre Manias, Dimitrios Michael Shami, Abdallah Machine Learning Networking and Internet Architecture Systems and Control Signal Processing 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. |
| title | Network Resource Optimization for ML-Based UAV Condition Monitoring with Vibration Analysis |
| topic | Machine Learning Networking and Internet Architecture Systems and Control Signal Processing |
| url | https://arxiv.org/abs/2502.15491 |