A spatial-temporal imputation technique for classification with missing data in a wireless sensor network

We have developed a novel method to estimate missing observations in wireless sensor networks. We use a hierarchical unsupervised fuzzy ART neural network to represent the data cluster prototypes. We then estimate missing inputs by using a new spatial-temporal imputation technique. We have evaluated this approach through experiments on both real sensor data and artificially generated data. Our experimental results show that our proposed approach performs better than nine other estimation algorithms including moving average and expectation-maximization (EM) imputation.