Efficient space-time modeling for informative sensing

Authors:
Sahil Garg;Amarjeet Singh;Fabio Ramos
Affiliations:
Indraprastha Institute of Information Technology, Delhi;Indraprastha Institute of Information Technology, Delhi;School of Information Technologies, University of Sydney
Venue:
Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data
Year:
2012

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Abstract

Real world phenomena often exhibit complex dynamics across space and time. Understanding such dynamics requires deploying sensors that provide relevant information about the observed phenomena. It is imperative to develop an efficient and accurate model, representing the complex spacetime dynamics, that can both inform sensor placement for efficient monitoring as well as help in appropriate knowledge discovery from the collected sensor data. This paper extends a generic non-stationary non-separable space-time Gaussian Process (GP) model, that models complex correlation properties using variable hyper parameters across input space. We provide approaches to effectively observe the hyper parameter space and thus significantly reduce the training cost for the proposed model. We also formalize relationship between degree of non-stationarity of the phenomenon and learning cost of the proposed model. Extensive empirical validation of the proposed model is performed using two real world sensing datasets, exhibiting diverse dynamics across space and time, to demonstrate the real world applicability of the proposed approach.