Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks
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 Integr...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-02-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-024-00595-y |
| _version_ | 1827326204633415680 |
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| author | S. Gayathri D. Surendran |
| author_facet | S. Gayathri D. Surendran |
| author_sort | S. Gayathri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T14:41:12Z |
| format | Article |
| id | doaj.art-d8c50873a2174537a4a6c0aa50ece3ee |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-03-07T14:41:12Z |
| publishDate | 2024-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-d8c50873a2174537a4a6c0aa50ece3ee2024-03-05T20:22:10ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2024-02-0113112110.1186/s13677-024-00595-yUnified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networksS. Gayathri0D. Surendran1Department of Computer Science and Engineering, Bannari Amman Institute of TechnologyDepartment of Information Technology, Karpagam College of Engineering Othakkal MandapamAbstract 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.https://doi.org/10.1186/s13677-024-00595-yWireless sensor networksOnline anomaly detectionEnergy efficiencyFederated learningMachine learningCloud computing |
| spellingShingle | S. Gayathri D. Surendran Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks Journal of Cloud Computing: Advances, Systems and Applications Wireless sensor networks Online anomaly detection Energy efficiency Federated learning Machine learning Cloud computing |
| title | Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks |
| title_full | Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks |
| title_fullStr | Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks |
| title_full_unstemmed | Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks |
| title_short | Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks |
| title_sort | unified ensemble federated learning with cloud computing for online anomaly detection in energy efficient wireless sensor networks |
| topic | Wireless sensor networks Online anomaly detection Energy efficiency Federated learning Machine learning Cloud computing |
| url | https://doi.org/10.1186/s13677-024-00595-y |
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