In this paper, we consider mobile sensor networks that use spatiotemporal Gaussian processes to predict a wide range of spatiotemporal physical phenomena. Nonparametric Gaussian process regression that is based on truncated observations is proposed for mobile sensor networks with limited memory and computational power. We first provide a theoretical foundation of Gaussian process regression with truncated observations. In particular, we demonstrate that prediction using all observations can be well approximated by prediction using truncated observations under certain conditions. Inspired by the analysis, we then propose a centralized navigation strategy for mobile sensor networks to move in order to reduce prediction error variances at points of interest. For the case in which each agent has a limited communication range, we propose a distributed navigation strategy. Particularly, we demonstrate that mobile sensing agents with the distributed navigation strategy produce an emergent, swarming-like, collective behavior for communication connectivity and are coordinated to improve the quality of the collective prediction capability.
Bibliographical noteFunding Information:
Manuscript received April 29, 2010; revised October 20, 2010 and March 14, 2011; accepted July 13, 2011. Date of publication August 18, 2011; date of current version December 8, 2011. This paper was recommended for publication by Associate Editor D. Song and Editor W. K. Chung upon evaluation of the reviewers’ comments. This work was supported in part by the National Science Foundation through CAREER Award CMMI-0846547. The work of S. Oh was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science, and Technology under Grant 2010-0013354.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering