دورية أكاديمية
Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks
| العنوان: | Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks |
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| المؤلفون: | S. Gayathri, D. Surendran |
| المصدر: | Journal of Cloud Computing: Advances, Systems and Applications, Vol 13, Iss 1, Pp 1-21 (2024) |
| بيانات النشر: | SpringerOpen, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Computer engineering. Computer hardware LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | Wireless sensor networks, Online anomaly detection, Energy efficiency, Federated learning, Machine learning, Cloud computing, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | Abstract Anomaly detection in Wireless Sensor Networks (WSNs) is critical for their reliable and secure operation. Optimizing resource efficiency is crucial for reducing energy consumption. Two new algorithms developed for anomaly detection in WSNs—Ensemble Federated Learning (EFL) with Cloud Integration and Online Anomaly Detection with Energy-Efficient Techniques (OAD-EE) with Cloud-based Model Aggregation. EFL with Cloud Integration uses ensemble methods and federated learning to enhance detection accuracy and data privacy. OAD-EE with Cloud-based Model Aggregation uses online learning and energy-efficient techniques to conserve energy on resource-constrained sensor nodes. By combining EFL and OAD-EE, a comprehensive and efficient framework for anomaly detection in WSNs can be created. Experimental results show that EFL with Cloud Integration achieves the highest detection accuracy, while OAD-EE with Cloud-based Model Aggregation has the lowest energy consumption and fastest detection time among all algorithms, making it suitable for real-time applications. The unified algorithm contributes to the system's overall efficiency, scalability, and real-time response. By integrating cloud computing, this algorithm opens new avenues for advanced WSN applications. These promising approaches for anomaly detection in resource constrained and large-scale WSNs are beneficial for industrial applications. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2192-113X |
| Relation: | https://doaj.org/toc/2192-113X |
| DOI: | 10.1186/s13677-024-00595-y |
| URL الوصول: | https://doaj.org/article/d8c50873a2174537a4a6c0aa50ece3ee |
| رقم الأكسشن: | edsdoj.8c50873a2174537a4a6c0aa50ece3ee |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 2192113X |
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| DOI: | 10.1186/s13677-024-00595-y |