Imputing missing values in sensor networks using sparse data representations

Sensor networks are increasingly being used to provide timely information about the physical, urban and human environment. Algorithms that depend on sensor data often assume that the readings are complete. However, node failures or communication breakdowns result in missing data entries, preventing the use of such algorithms. To impute these missing values, we propose a method of exploiting spatial correlations which is based on the sparse autoencoder and inspired by the conditional Restricted Boltzmann Machine that contested for the Netflix Prize. We modify the autoencoder to cope with missing data, and test it on data from a sensor testbed in Santander, Spain. We show that our algorithm extracts features from datasets with high proportions of missing data and uses these features to accurately and efficiently impute missing entries.