HDAC High-Dimensional Data Aggregation Control Algorithm for Big Data in Wireless Sensor Networks

Abstract

The process of high-dimensional data is a hot research area in data mining technology. Due to sparsity of the high-dimensional data, there is significant difference between the high-dimensional space and the low-dimensional space, especially in terms of the data process. Many sophisticated algorithms of low-dimensional space cannot achieve the expected effect, even cannot be used in the high-dimensional space. Thus, this paper proposes a High-dimensional Data Aggregation Control Algorithm for Big Data (HDAC). The algorithm uses information to eliminate the dimension not matching with the specified requirements. Then it uses the principal components method to analyze the rest dimension. Thus, the simplest method is used to reduce the calculation of dimensionality reduction as can as it possible. In the process of data aggregation, the self-adaptive data aggregation mechanism is used to reduce the phenomenon of network delay. Finally, the simulation shows that the algorithm can improve the performance of node energy-consumption, rate of the data post-back and the data delay.