Abstract
This paper proposes a reliable, intelligent, model-based (hybrid) fault detection and diagnosis (FDD) technique for wireless sensor networks (WSNs) in the presence of noise and uncertainties. A wireless sensor network is a network formed by a large number of sensor nodes in which each node is equipped with a sensor to detect physical phenomena such as light, heat, pressure, and temperature. Increasing the number of sensor nodes can cause an increase in the number of faulty nodes, which adversely affects the quality of service (QoS). Herein, the WSN modeling is based on an adaptive method that combines the fuzzy C-means clustering algorithm with the modified auto-regressive eXternal (ARX) model and is utilized for fault identification in WSNs. The proposed adaptive nonlinear ARX fuzzy C-means (NARXNF) clustering technique obtains both an improved convergence and error reduction relative to that of the traditional fuzzy C-means clustering algorithm. In addition, the proportional integral (PI) distributed observation is used for diagnosing multiple faults, where the convergence, robustness, and stability are validated by a fuzzy linear matrix inequality (FLMI). To test the proposed method, this technique was implemented through simulation using Omnet++ and MATLAB.
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Acknowledgements
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20181510102160, 20162220100050, 20161120100350, and 20172510102130). It was also funded in part by the Leading Human Resource Training Program of Regional Neo Industry through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (NRF-2016H1D5A1910564), and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A3B03931927).
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Piltan, F., Kim, JM. (2019). Fault Diagnosis of a Wireless Sensor Network Using a Hybrid Method. In: Park, J., Shen, H., Sung, Y., Tian, H. (eds) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2018. Communications in Computer and Information Science, vol 931. Springer, Singapore. https://doi.org/10.1007/978-981-13-5907-1_14
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DOI: https://doi.org/10.1007/978-981-13-5907-1_14
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