Data missing is a common problem in wireless sensor networks. Currently, to ensure the performance of data processing, making imputation for the missing data is the most common method before getting into sensor data analysis. In this paper, the temporal and spatial nearest neighbor values-based missing data imputation (TSNN), a new imputation based on the temporal and spatial nearest neighbor values has been presented. First, four nearest neighbor values have been defined from the perspective of space and time dimensions as well as the geometrical and data distances, which are the bases of the algorithm that help to exploit the correlations among sensor data on the nodes with the regression tool. Next, the algorithm has been elaborated as well as two parameters, the best number of neighbors and spatial–temporal coefficient. Finally, the algorithm has been tested on an indoor and an outdoor wireless sensor network, and the result shows that TSNN is able to improve the accuracy of imputation and increase the number of cases that can be imputed effectively.

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.