Prediction and adaptive sampling for mobile sensor networks

We have developed spatio-temporal prediction and adaptive sampling algorithms for mobile sensor networks. Mobile sensing agents need to locally learn the uncertain environment collaboratively with neighboring agents to achieve a global goal such as exploration and environmental monitoring. We are developing environmental adaptive sampling algorithms for mobile sensor networks to predict scalar fields of interest using nonparametric approaches raning from Gaussian processes, Gaussian Markov random fields, and kernel regression. This project has been funded by an NSF CAREER Award. A collection of successful outcomes will be disseminated as a SpringerBrief: “Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks,” by Xu, Choi, Dass, and Maiti.