Abstract
With the large-scale application of the Internet of Things, various sensors continue to produce new and various environmental data. Among them, there may be some failure data caused by environmental interference, device aging, etc., and these failure data are given to relevant scientific researchers and The Internet of Things system brings huge problems. We propose a new kind of Internet of Things nodes combined with programmable switches failure detection method. Different from the method proposed by the predecessors, we perform failure detection during the sensor data packet transmission. This method realizes the interaction between the programmable switch and the local controller. It can perform failure detection on a large amount of sensor data in real-time. Use the processing power of programmable switches to reduce the feature extraction time in machine learning algorithms. In this article, we reviewed the technical background of programmable switch The Internet of Things failure detection and explained its architecture. To prove the feasibility of the system, we implemented it on the bmv2 software switch. The prototype was verified through experiments, simulation evaluation was performed on the real data set, and the average time for the machine learning algorithm to classify each sensor data was 1.26 ms.
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Acknowledgement
When the thesis is finished, I would like to thank my instructor, Junxing Zhang, for his warm care and careful guidance. In the process of writing the thesis, I also received valuable suggestions from Guangfeng Guo and Renbo Yang, and I would like to express my sincere thanks.
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Zhang, X., Yang, R., Guo, G., Zhang, J. (2021). Sensor Failure Detection Based on Programmable Switch and Machine Learning. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12737. Springer, Cham. https://doi.org/10.1007/978-3-030-78612-0_41
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DOI: https://doi.org/10.1007/978-3-030-78612-0_41
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