Feature-level Heterogeneous Data Fusion Method for Narrowband Internet of Things Based on Sliding Clustering
Due to the diverse characteristics of Internet of Things data and unclear requirements for different types of data,it is difficult to extract data feature sets.To improve the effect of heterogeneous data fusion in the Internet of Things,a feature lev-el heterogeneous data fusion method for narrowband Internet of Things based on sliding clustering is proposed.The temporal information of network data is obtained through translation transformation,and the quality of heterogeneous data collected by wireless sensor nodes is enhanced through wavelet transform.The sliding window difference value is caculated to determine the initial clustering point of the feature-level heterogeneous data in the Internet of Things,and the mean shift algorithm is used to complete the clustering of heterogeneous data states.The feature set extraction of similar data is completed through the aggre-gation mechanism to achieve the fusion of feature-level heterogeneous data.Experimental results show that the proposed meth-od effectively reduces the scale of Internet of Things data,diminish data fusion errors,and the information is not distorted after fusion,which has high application value in the field of data processing.
heterogeneous data in the Internet of Thingssliding windowdata fusionwavelet transformdata sihouette coef-ficientcluster center