Universiti Teknologi Malaysia Institutional Repository

Principal component analysis-based data reduction model for wireless sensor networks

Rassam, Murad Abdo and Zainal, Anazida and Maarof, Mohd. Aizaini (2015) Principal component analysis-based data reduction model for wireless sensor networks. International Journal of Ad Hoc and Ubiquitous Computing, 18 (1-2). pp. 85-101. ISSN 1743-8225

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Official URL: http://dx.doi.org/10.1504/IJAHUC.2015.067756


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.

Item Type:Article
Uncontrolled Keywords:networks, data reduction
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:55063
Deposited By: Muhamad Idham Sulong
Deposited On:24 Aug 2016 14:41
Last Modified:01 Aug 2017 12:48

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