A double weighted LS-SVM model for data estimation in wireless sensor networks

In wireless sensor networks, data missing is a common problem due to sensor faults, time synchronization, malicious attacks, and communication malfunctions, which may degrade the network’s performance or lead to inefficient decisions. Therefore, it is necessary to effectively estimate the missing data. A double weighted least squares support vector machines (DWLS-SVM) model for the missing data estimation in wireless sensor networks is proposed in this paper. The algorithm first applies the weighted LS-SVM (WLS-SVM) to estimate the missing data on temporal domain and spatial domain respectively, and then uses the weighted average of these two candidates as the final estimated value. DWLS-SVM considers the possibility of outliers in the dataset and utilizes spatio-temporal dependencies among sensor nodes fully, which makes the estimate more robust and precise. Experimental results on real world dataset demonstrate that the proposed algorithm is outlier robust and can estimate the missing values accurately.