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| Abstract: |
| A Wireless Sensor Network (WSN) comprises a series of spatially distributed autonomous devices, each equipped with sophisticated sensors. These sensors play a crucial role in monitoring diverse environmental conditions such as light intensity, air pressure, temperature, humidity, wind, etc. These sensors are generally deployed in harsh and hostile conditions; hence they suffer from different kinds of faults. However, identifying faults in WSN data remains a complex task, as existing fault detection methods, including centralized, distributed, and hybrid approaches, rely on the spatio-temporal correlation among sensor nodes. Moreover, existing techniques predominantly leverage classification-based machine learning methods to discern the fault state within WSN. In this paper, we propose a regression-based bagging method to detect the faults in the network. The proposed bagging method is consisted of GRU (Gated Recurrent Unit) and Prophet model. Bagging allows weak learners to combine efforts to outperform a strong learner, hence it is appropriate to use in WSN. The proposed bagging method was first trained at the base station, then they were deployed at each SN (Sensor Node). Most of the common faults in WSN,such as transient, intermittent and permanent faults,were considered. The validity of the proposed scheme was tested using a trusted online published dataset. Using experimental studies, compared to the latest state-of-the-art machine learning models, the effectiveness of the proposed model is shown for fault detection. Performance evaluation in terms of false positive rate, accuracy, and false alarm rate shows the efficiency of the proposed algorithm. |
| Key words: fault detection GRU prophet deep learning wireless sensor networks |
| DOI：10.11916/j.issn.1005-9113.2024051 |
| Clc Number:TP277 |
| Fund: |
