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Spatial gaussian process regression with mobile sensor networks

Gu, D and Hu, H (2012) 'Spatial gaussian process regression with mobile sensor networks.' IEEE Transactions on Neural Networks and Learning Systems, 23 (8). 1279 - 1290. ISSN 2162-237X

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Abstract

This paper presents a method of using Gaussian process regression to model spatial functions for mobile wireless sensor networks. A distributed Gaussian process regression (DGPR) approach is developed by using a sparse Gaussian process regression method and a compactly supported covariance function. The resultant formulation of the DGPR approach only requires neighbor-to-neighbor communication, which enables each sensor node within a network to produce the regression result independently. The collective motion control is implemented by using a locational optimization algorithm, which utilizes the information entropy from the DGPR result. The collective mobility of sensor networks plus the online learning capability of the DGPR approach also enables the mobile sensor network to adapt to spatiotemporal functions. Simulation results are provided to show the performance of the proposed approach in modeling stationary spatial functions and spatiotemporal functions. © 2012 IEEE.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 12 Feb 2013 09:57
Last Modified: 23 Jan 2019 00:16
URI: http://repository.essex.ac.uk/id/eprint/5505

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