Abstract
In monitoring and alerting industrial system, industrial wireless sensor networks play an important role. However, we usually have to face one critical issue that is to recover the data and emergency treatment schedule for the faulty sensors. In this paper, we target on monitoring industrial environments and deal with the problems caused by the failure or faulty sensors nodes. Firstly, based on industrial private cloud, an architecture of industrial environment monitoring system is proposed. Furthermore, a hierarchical support vector machines is adopted for faulty nodes’ data recovery. Unlike most previous works, we intend to address the problem from global and local data perspectives. Using the first layer Support Vector Machines is adopted to judge the types of missing data based on the monitoring system. In second layer of SVM is responsible for finishing the recovery local data in the light of the history records. Performance of the proposed SVM data recovery strategies are evaluated in terms of networks self-healing competence, and energy consumption. We also implement our schemes in a real-life monitoring and alerting network system to demonstrate the feasibility and validate the network detection capability of emergency events.
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Acknowledgments
This work is supported by the Youth Innovation Project of Important Program for college of Guangdong province, China, in 2015 with the No 2015KQNCX228. Meanwhile, this work partly was supported by the colleagues in the Department of Electronic Communication & Software Engineering Nanfang College of Sun Yat-sen University.
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Cao, H., Yuan, J., Li, Y., Yuan, W. (2016). Data Recovery and Alerting Schemes for Faulty Sensors in IWSNs. In: Wan, J., Humar, I., Zhang, D. (eds) Industrial IoT Technologies and Applications. Industrial IoT 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 173. Springer, Cham. https://doi.org/10.1007/978-3-319-44350-8_7
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DOI: https://doi.org/10.1007/978-3-319-44350-8_7
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