Abstract
As the ‘Industry 4.0’ and ‘Made in China 2025’ has been put forward, the need of the large-scale system integration for Internet of Things (IoT) has been more and more urgent. At present, different IoT systems have different database types, table structures and denominating rules for sensing parameters. So for the existing IoT system integration, there are such as sensing parameter’s conversion difficulty, complex matching process, low integrating efficiency issues. To solve these problems, we propose a novel model for IoT sensing parameter automatically matching which can achieve the IoT system integration on a large-scale. Meanwhile combining KNN thought, using a weighted method to improve the KNN algorithm, we put forward the automatic IoT sensing parameters matching algorithm. By the multiple practical IoT system integration cases, we validate the rationality and efficiency of the model and the algorithm. The result shows that the model and the algorithm are feasible and efficient. They realize the rapid automatic matching for the heterogeneous IoT sensing parameters, improving the IoT system’s integration efficiency. It is conducive to the large-scale heterogeneous IoT system quick integration and has great significance to promote the IoT’s application in large scale.
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Acknowledgment
Zhijin Qiu and Naijun Hu contributed equally to this work and should be regarded as co-first authors. This work is supported by the National Natural Science Foundation of China (Grant No. 61379127, No. 61572448 and No. 61170258), and Natural Science Foundation of Shandong ZR2014JL043. I would like to express my sincere gratitude to Yingjian Liu for her encouragement and constructive feedback.
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Qiu, Z., Hu, N., Guo, Z., Qiu, L., Guo, S., Wang, X. (2016). IoT Sensing Parameters Adaptive Matching Algorithm. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds) Big Data Computing and Communications. BigCom 2016. Lecture Notes in Computer Science(), vol 9784. Springer, Cham. https://doi.org/10.1007/978-3-319-42553-5_17
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DOI: https://doi.org/10.1007/978-3-319-42553-5_17
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