Weighted Adaptive Partition for Heterogeneous IoT Data Stream

Abstract

In the past, many researchers used sliding-window mechanism in Internet of Things (IoT) data stream mining, which can divide the incoming large volume data into small segments and improve the information processing rate. With the emergence of heterogeneous IoT data streams, data forms become richer and more diverse. The fusion analysis of multimodality information can improve the accuracy of information processing and network decision-making, because it can make up for the lack of some information in a single-mode data stream. However, the representation of information in different data sources is different, and the amount of information contained in it is distinct. Thus, a weighted adaptive partition algorithm is proposed in this article, which performs a dynamic weighted fusion of multisource heterogeneous IoT data in sliding windows. The article adopts the Gaussian mixture model (GMM) to adaptively determine the weights of each data source, which is calculated based on the judgement confidence of each modal data to the change in data stream. Besides, the probability distribution change detection mechanism based on multimodality restricted Boltzmann machine (multimodality RBM) is proposed, which measures the joint probability distribution by the weighted data’s energy value. Finally, according to the energy change rate of the data stream, the size of the window is adjusted adaptively to realize the partition for heterogeneous IoT data stream. The algorithm proposed in this article is verified on the CUAVE data set and the analysis of results shows that the data under adaptive weighted fusion has better performance.