Mobile robots for learning spatio-temporal interpolation models in sensor networks — The Echo State map approach

Abstract

Sensor networks have limited capabilities to model complex phenomena occuring between sensing nodes. Mobile robots can be used to close this gap and learn local interpolation models. In this paper, we utilize Echo State Networks in order to learn the calibration and interpolation model between sensor nodes using measurements collected by a mobile robot. The use of Echo State Networks allows to deal with temporal dependencies implicitly, while the spatial mapping with a Gaussian Process estimator exploits the fact that Echo State Networks learn linear combinations of complex temporal dynamics. The resulting Echo State Map elegantly combines spatial and temporal cues into a single representation. We showcase the method in the exposure modeling task of building dust distribution maps for foundries, a challenge which is of great interest to occupational health researchers. Results from simulated data and real world experiments highlight the potential of Echo State Maps. While we focus on particulate matter measurements, the method can be applied for any other environmental variables like temperature or gas concentration.