A new regularized recursive dynamic factor analysis with variable forgetting factor for wireless sensor networks with missing data

Missing data imputation is often required in wireless sensor networks (WSNs) to fill up missing measurements due to transmission loss, hardware failure and other factors. In this paper, we propose new variable forgetting factor (VFF) and regularization extensions to the recursive dynamic factor analysis (RDFA) algorithm for imputation of missing data in WSN data. It takes advantage of the correlated structure of the redundancy among WSN measurements by decomposing WSN measurements into orthogonal factor loadings and de-correlated factors. A new local polynomial model (LPM) based variable forgetting factor is proposed for the RDFA algorithm and it enables us to better adapt to the time-varying environment. Finally, ℓ2 regularization is further incorporated to RDFA for improving the numerical conditioning. Experimental results using a real WSN dataset show that the proposed algorithm is able to achieve better accuracy than other conventional approaches.