University of California, Riverside (Student Affairs)
Recovering missing sensor data is a critical problem for sensor networks, especially when nodes duty cycles their activity or may experience periodic downtimes due to limited energy. Fortunately, sensor readings are often correlated across different nodes and sensor types. Among state-of-the-art statistical data estimation techniques, latent variable based factor models have emerged as a powerful framework for recovering missing data. In this paper, the authors propose the use of latent variable models to estimate missing data in heterogeneous sensor networks.