State determination algorithm of WSN based on twice grey Markov forecasting.

Saved in:
Bibliographic Details
Title: State determination algorithm of WSN based on twice grey Markov forecasting.
Authors: LIN Wei1 linwei@hrbeu.edu.cn, LI Bo1, HAN Li-hong1 xueni85426@163.com
Source: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Aug2013, Vol. 35 Issue 8, p41-45. 5p.
Abstract: According to characteristics of wireless sensor networks, for the sake of saving energy of nodes in wireless sensor networks, a twice-unbiased Gryy-Markov forecasting model is proposed. Based on Markov prediction, according to the state determination rule and twice-unbiased Gray prediction, the environment state of wireless sensor networks is determined, and the flag of the state is transferred, so that wireless sensor networks can reduce the energy consumption. Simulation results show that the model improves the prediction accuracy and achieves effective state prediction. [ABSTRACT FROM AUTHOR]
Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:According to characteristics of wireless sensor networks, for the sake of saving energy of nodes in wireless sensor networks, a twice-unbiased Gryy-Markov forecasting model is proposed. Based on Markov prediction, according to the state determination rule and twice-unbiased Gray prediction, the environment state of wireless sensor networks is determined, and the flag of the state is transferred, so that wireless sensor networks can reduce the energy consumption. Simulation results show that the model improves the prediction accuracy and achieves effective state prediction. [ABSTRACT FROM AUTHOR]
ISSN:1007130X
DOI:10.3969/j.issn.1007-130X.2013.08.008