Principal component analysis–based data reduction model for wireless sensor networks. (1st January 2015)

• Record Type: Journal Article
• Authors: Rassam, Murad A.; Zainal, Anazida; Maarof, Mohd. Aizaini
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 18:Number 1/2(2015)
• Page Start: 85
• Page End: 101
• Publication Date: 2015-01-01
• Subjects: WSNs -- wireless sensor networks -- ad hoc networks -- PCA -- principal component analysis -- data reduction -- multivariate data analysis -- energy consumption -- modelling; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• DOI: 10.1504/IJAHUC.2015.067756
• Languages: English
• ISSNs: 1743-8225

Abstract

Wireless sensor networks (WSNs) are widely used in monitoring environmental and physical conditions, such as temperature, vibration, humidity, light and voltage. However, the high dimension of sensed data, especially in multivariate sensor applications, increases the power consumption in transmitting this data to the base station and hence shortens the lifetime of sensors. Therefore, efficient data reduction methods are needed to minimise the power consumption in data transmission. In this paper, an efficient model for multivariate data reduction is proposed based on the principal component analysis (PCA). The performance of the model was evaluated using Intel Berkeley Research Lab (IBRL) dataset. The experimental results show the advantages of the proposed model as it allows 50% reduction rate and 96% approximation accuracy after reduction. A comparison with an existing model shows the superiority of the proposed model in terms of approximation accuracy as the reconstruction error is always smaller for different datasets.