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Data Fusion Algorithm Based on Classification Adaptive Estimation Weighted Fusion in WSN

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Abstract

Special medical supplies such as blood and vaccines have strict temperature requirements in medical cold storage. In order to improve the stability of the temperature monitoring system in the medical cold storage and solve the problem of low measurement accuracy caused by node failure or abnormal data interference, we propose an algorithm based on classification adaptive estimation (CAEWF). The algorithm first monitors and classifies the data collected by the nodes, and uses the mutual support matrix between the data to filter out the validity abnormal data and transmit them to the cluster head. Then use the classification adaptive estimation weighted fusion algorithm to estimate the weighted fusion of the data. The simulation results show that the accuracy of the CAEWF algorithm is better than the arithmetic mean and batch estimation fusion algorithm, which can meet the temperature accuracy requirements of medical cold storage.

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Funding

Key R&D Program in Shandong Province (2019GGX105001); Shandong University Science and Technology Plan Project (J18KB164,J18KB163); Qingdao Huanghai University Science and Technology Project (2021KJ08).

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Authors and Affiliations

  1. Qingdao Huanghai University, Qingdao, 266000, China

    Dong Yan, Peixue Liu, Xiujie Yue & Penghao Wang

  2. Yantai University, Yantai, 264000, China

    Minghua Liu & Baoshun Li

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  1. Dong Yan
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  2. Peixue Liu
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  3. Xiujie Yue
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  4. Penghao Wang
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  5. Minghua Liu
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  6. Baoshun Li
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Correspondence to Dong Yan.

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Yan, D., Liu, P., Yue, X. et al. Data Fusion Algorithm Based on Classification Adaptive Estimation Weighted Fusion in WSN. Wireless Pers Commun 127, 2859–2871 (2022). https://doi.org/10.1007/s11277-022-09900-x

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  • DOI: https://doi.org/10.1007/s11277-022-09900-x

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