Estimating Missing Data in Wireless Sensor Network Through Spatial-Temporal Correlation.

Wireless sensor networks consist of a set of smart sensors with limited memory and wireless communication capabilities. These sensors get data from the environment and send them to an application center. However, data loss has happened due to the characteristics of sensors, which negatively affect the accuracy of applications. To solve this problem, we need to estimate the missing data for applications that depend on accurate data collecting. In this study, we present an algorithm that uses the most significant historical data to estimate the missing data based on spatial and temporal correlations. In the proposed algorithm, we combine the spatial correlation by using data from the closest sensor based on the missing pattern and the temporal correlation by referring to the closest data prior to the missing instance. The experimental results demonstrate that the proposed algorithm lowers estimation errors when compared to current algorithms for a variety of missing data patterns.

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