Abstract
In the context of big data, data fusion in the perception layer of the Internet of Things is extremely necessary. Fusion data can reduce the amount of data traffic in the network, avoid wasting network resources and bring great convenience to users’ observation and analysis. Aiming at the high computational complexity of the data fusion algorithm at the current, an approximate data fusion algorithm for the perception layer of the Internet of Things based on the probability distribution is proposed in this paper. Firstly, the data fusion model of the perception layer of the Internet of Things and the probability distribution model of the node data are analyzed. And then, disturbances are applied to the node data to achieve the purpose of concealing the collected data. Finally, the approximate fusion of data in the sensing layer is achieved by collecting the probability distribution of the data. The experimental results verify the effectiveness of the fusion algorithm and test the influence of the algorithm parameters on the fusion effect, which provides a reference for the engineering implementation of the algorithm.
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Inner Mongolia National University Research Project (NMDYB1729). Inner Mongolia Autonomous Region Science and Technology Innovation Guide Project in 2018: KCBJ2018028.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wu, Xq., Wu, L., Tu, L. (2019). Approximate Data Fusion Algorithm for Internet of Things Based on Probability Distribution. In: Liu, S., Yang, G. (eds) Advanced Hybrid Information Processing. ADHIP 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-19086-6_15
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DOI: https://doi.org/10.1007/978-3-030-19086-6_15
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