title: Spatio-temporal functional data analysis for wireless sensor networks data.
abstract: A new methodology is proposed for the analysis, modeling and forecasting of data collected from a wireless sensor network. Our approach is considered in the framework of a functional data analysis paradigm where observed data is represented in a functional form. To reduce dimensionality, functional principal components analysis is applied to highlight important underlying characteristics and find patterns of variations. The principal scores are modeled with tensor product smooths that allow for smoothing over space and time. The model is then used for simultaneous spatial prediction at unsampled locations and to forecast future observations. We consider soil temperature data from a wireless sensor network of 50 sensor nodes in two different land types (grassland and forest) observed during 60 consecutive days in private property close to Yass, New South Wales, Australia.
authors.affiliation: BCAM - Basque Center for Applied Mathematics, Bilbao, Spain.
authors.forename: D-J
authors.initials: DJ
authors.lastname: Lee
authors.affiliation: Department of Statistics, Iowa State University, Ames, USA.
authors.forename: Z
authors.initials: Z
authors.lastname: Zhu
authors.affiliation: Commonwealth Scientific and Industrial Organization, Risk Analytics Group - Digital Productivity Flagship, Private Bag 10, South Clayton, Australia.
authors.forename: P
authors.initials: P
authors.lastname: Toscas
keywords: Wireless sensor networks; forecasting; functional data analysis; functional principal components; non-parametric smoothing; penalized splines