Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (8): 2746-2752.doi: 10.13229/j.cnki.jdxbgxb.20240772

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Detection of mixed attribute features of multi-source heterogeneous data in software defined IoT

Lian-lian ZHANG1,2(),Wei GUO2,Feng LIU1()   

  1. 1.School of Electronics and Information Engineering，Beihang University，Beijing 100191，China
    2.School of Electrical Engineering，Hebei University of Architecture，Zhangjiakou 075000，China
  • Received:2024-07-12 Online:2025-08-01 Published:2025-11-14
  • Contact: Feng LIU E-mail:lianlianzhang@buaa.com.cn;liufuil@sina.com

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Abstract:

In the software defined Internet of Things （IoT）， multi-source heterogeneous data comes from different devices and has different formats， structures， and qualities， which increases the complexity of feature detection. Therefore， a mixed attribute feature detection method for multi-source heterogeneous data in software defined IoT is proposed. Using the joint Kalman filtering algorithm to fuse multi-source heterogeneous data in the software defined Internet of Things， completing the initial integration of heterogeneous data. Combined with evidence classification algorithms， network data with the same mixed attributes are divided into the same dataset to achieve classification of multi-source heterogeneous data. Based on the inverse similarity characteristics of multi-source data， an edge operator calculation method is introduced to split the classified data attribute features， and combined with support vector machines， accurate detection of multi-source heterogeneous data attribute features is achieved. The experiment shows that the covariance calculation results of the proposed method are always below 0.15， and the distinction between different attribute features is more obvious， with a detection probability of over 0.8. This method can achieve precise partitioning of mixed attributes of multi-source heterogeneous data in software defined IoT.

Key words: joint Kalman filtering algorithm, software defined Internet of things, evidence classification algorithm, edge operator, support vector machine

CLC Number: 

  • TP311.13

Fig.1

Data visualization detection environment"

Table 1

Comparison of data fusion effects"

数据集

编号

协方差值
本文方法时间序列算法加权融合算法
10.150.420.73
20.130.360.32
30.140.510.27
40.120.370.56
50.130.440.47
60.150.450.36
70.140.560.58
80.130.600.66

Fig.2

Mixed attribute feature division effect"

Fig.3

Comparison of detection effects of different methods"

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