Online Missing Data Imputation Using Virtual Temporal Neighbor in Wireless Sensor Networks

Abstract

A wireless sensor network (WSN) is one of the most typical applications of the Internet of Things (IoT). Missing values exist in the sensor data streams unavoidably because of the way WSNs work and the environments they are deployed in. In most cases, imputing missing values is the universally adopted approach before making further data processing. There are different ways to implement it, among which the exploitation of correlation information hidden in the sensor data interests many researchers, and lots of results have emerged. Researching in the same way, in this paper, we propose VTN imputation, an online missing data imputation algorithm based on virtual temporal neighbors. Firstly, the virtual temporal neighbor (VTN) in the sensor data stream is defined, and the calculation method is given. Next, the VTN imputation algorithm, which applies VTN to make estimates for missing values by regression is presented. Finally, we make experiments to evaluate the performance of imputing accuracy and computation time for our algorithm on three different real sensor datasets. The experiment results show that the VTN imputation algorithm benefited from the fuller exploitation of the correlation in sensor data and obtained better accuracy of imputation and acceptable processing time in the real applications of WSNs.