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Distributed least square support vector regression for environmental field estimation

Lu, B and Gu, D and Hu, H (2011) Distributed least square support vector regression for environmental field estimation. In: UNSPECIFIED, ? - ?.

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Abstract

A distributed approach to monitoring the environmental field function with mobile sensor networks is presented in this paper. With this approach, a mobile sensor network is capable to estimate a model of field functions in real-time. This approach consists of two stages, a field function learning stage and a locational optimising stage. A distributed least square support vector regression (LS-SVR) is developed for the field function learning stage. On the locational optimising stage, a gradient based method: centroidal Voronoi tessellation (CVT) is used to allocate each sensor node's position. These two stages are running alternately in a loop so that the field function learning stage can keep updating the field function with new sensor readings resulted from the locational optimising stage, and simultaneously, the locational optimising stage can relocate sensor nodes according to a more accurate field function model. Eventually, the field function is estimated and the sensor nodes are distributed based on the estimated model. The simulation results given in this paper show the effectiveness of this approach. © 2011 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2011 IEEE International Conference on Information and Automation, ICIA 2011
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: Clare Chatfield
Date Deposited: 02 Jul 2013 11:05
Last Modified: 23 Jan 2019 00:16
URI: http://repository.essex.ac.uk/id/eprint/4225

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