FedLSTM: a federated learning framework for sensor fault detection in wireless sensor networks
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electronics-13-04907-v3.pdf
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Issue Date
2024-12-12Subjects
machine learningIoT
data privacy
Wireless Sensor Networks
fault detection
Subject Categories::G760 Machine Learning
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The rapid growth of Internet of Things (IoT) devices has significantly increased reliance on sensor-generated data, which are essential to a wide range of systems and services. Wireless sensor networks (WSNs), crucial to this ecosystem, are often deployed in diverse and challenging environments, making them susceptible to faults such as software bugs, communication breakdowns, and hardware malfunctions. These issues can compromise data accuracy, stability, and reliability, ultimately jeopardizing system security. While advanced sensor fault detection methods in WSNs leverage a machine learning approach to achieve high accuracy, they typically rely on centralized learning, and face scalability and privacy challenges, especially when transferring large volumes of data. In our experimental setup, we employ a decentralized approach using federated learning with long short-term memory (FedLSTM) for sensor fault detection in WSNs, thereby preserving client privacy. This study utilizes temperature data enhanced with synthetic sensor data to simulate various common sensor faults: bias, drift, spike, erratic, stuck, and data-loss. We evaluate the performance of FedLSTM against the centralized approach based on accuracy, precision, sensitivity, and F1-score. Additionally, we analyze the impacts of varying the client participation rates and the number of local training epochs. In federated learning environments, comparative analysis with established models like the one-dimensional convolutional neural network and multilayer perceptron demonstrate the promising results of FedLSTM in maintaining client privacy while reducing communication overheads and the server load.Citation
Khan R, Saeed U, Koo I (2024) 'FedLSTM: a federated learning framework for sensor fault detection in wireless sensor networks', Electronics (Switzerland), 13 (24), 4907Journal
Electronics (Switzerland)Additional Links
https://www.mdpi.com/2079-9292/13/24/4907Type
ArticleLanguage
enEISSN
2079-9292Sponsors
This research was supported by the Regional Innovation Strategy (RIS) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021RIS-003).ae974a485f413a2113503eed53cd6c53
10.3390/electronics13244907
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