Multiple linear regression to improve prediction accuracy in WSN data reduction

Carlos Giovanni Nunes de Carvalho; Diogo Gonçalves Gomes; José Neuman de Souza; Nazim Agoulmine

doi:10.1109/LANOMS.2011.6102268

Conference papers

Multiple linear regression to improve prediction accuracy in WSN data reduction

Carlos Giovanni Nunes de Carvalho ¹ Diogo Gonçalves Gomes ¹ José Neuman de Souza ¹ Nazim Agoulmine ²

1 GREat - Group of Computer Networks, Software Engineering and Systems

2 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes

Abstract : Simple linear regression is usually used for WSN data reduction. The mechanism is concerned about energy consumption, but neglects the prediction accuracy. The prediction error from it is often ignored and inconsistencies are forwarded to the user application. This paper proposes to use a method based on multiple linear regression to improve prediction accuracy. The improvement is achieved by multivariate correlation of readings gathered by sensor nodes in field. Tests show that our solution outperforms some current solutions adopted in the literature.

Keywords : data reduction linear regression functions multivariate correlation Prediction accuracy wireless sensor network

Document type :

Conference papers

Domain :

Computer Science [cs] / Networking and Internet Architecture [cs.NI]

Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-00745080
Contributor : Frédéric Davesne <>
Submitted on : Wednesday, October 24, 2012 - 3:05:37 PM
Last modification on : Tuesday, June 30, 2020 - 11:56:08 AM

Multiple linear regression to improve prediction accuracy in WSN data reduction

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Multiple linear regression to improve prediction accuracy in WSN data reduction

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