Temporal and Spatial Nearest Neighbor Values Based Missing Data Imputation in Wireless Sensor Networks.

Yulong Deng,Jian Guo,Lijuan Sun,Chong Han

doi:10.3390/s21051782

Yulong Deng, Jian Guo + Show 2 more

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https://doi.org/10.3390/s21051782

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Abstract

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.

Highlights

Imputation in Wireless SensorThe dataset will become incomplete if some data are lost in the acquisition stage, which may be caused by many different reasons [1]
Most of them are caused by the human errors especially when the data come from manual questionnaires or by the non-human errors when the data are obtained from a system automatically, for example, the faults of the sensors or communication units in wireless sensor networks
Tttnn si, u j and sgnn i j i j sdnn ttdnn si, u j ; the first two values come from spatial nearest neighbors in geometrical distance and in data distance, respectively, and the last two values come from the temporal nearest neighbors in time distance and in data distance, respectively

Summary

Introduction

The dataset will become incomplete if some data are lost in the acquisition stage, which may be caused by many different reasons [1]. Most of them are caused by the human errors especially when the data come from manual questionnaires or by the non-human errors when the data are obtained from a system automatically, for example, the faults of the sensors or communication units in wireless sensor networks. The latter will be focused on in this paper. In human activity recognition where the Support Vector Machine (SVM)

Methods

Discussion

Conclusion