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A self-adaptive regression-based multivariate data compression scheme with error bound in wireless sensor networks

Zhang, J and Yang, K and Xiang, L and Luo, Y and Xiong, B and Tang, Q (2013) 'A self-adaptive regression-based multivariate data compression scheme with error bound in wireless sensor networks.' International Journal of Distributed Sensor Networks, 2013. ISSN 1550-1329

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Wireless sensor networks (WSNs) have limited energy and transmission capacity, so data compression techniques have extensive applications. A sensor node with multiple sensing units is called a multimodal or multivariate node. For multivariate stream on a sensor node, some data streams are elected as the base functions according to the correlation coefficient matrix, and the other streams from the same node can be expressed in relation to one of these base functions using linear regression. By designing an incremental algorithm for computing regression coefficients, a multivariate data compression scheme based on self-adaptive regression with infinite norm error bound for WSNs is proposed. According to error bounds and compression incomes, the self-adaption means that the proposed algorithms make decisions automatically to transmit raw data or regression coefficients, and to select the number of data involved in regression. The algorithms in the scheme can simultaneously explore the temporal and multivariate correlations among the sensory data. Theoretically and experimentally, it is concluded that the proposed algorithms can effectively exploit the correlations on the same sensor node and achieve significant reduction in data transmission. Furthermore, the algorithms perform consistently well even when multivariate stream data correlations are less obvious or non-stationary. © 2013 Jianming Zhang et al.

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: Users 161 not found.
Date Deposited: 15 Sep 2015 15:23
Last Modified: 30 Jan 2019 16:18

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