A Taylor Based Localization Algorithm for Wireless Sensor Network Using Extreme Learning Machine

Xiong LUO; Xiaohui CHANG; Hong LIU

doi:10.1587/transinf.2013THP0019

A Taylor Based Localization Algorithm for Wireless Sensor Network Using Extreme Learning Machine

Xiong LUO, Xiaohui CHANG, Hong LIU

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More recently, there has been a growing interest in the study of wireless sensor network (WSN) technologies for Interest of Things (IoT). To improve the positioning accuracy of mobile station under the non-line-of-sight (NLOS) environment, a localization algorithm based on the single-hidden layer feedforward network (SLFN) using extreme learning machine (ELM) for WSN is proposed in this paper. Optimal reduction in the time difference of arrival (TDOA) measurement error is achieved using SLFN optimized by ELM. Compared with those traditional learning algorithms, ELM has its unique feature of a higher generalization capability at a much faster learning speed. After utilizing the ELM by randomly assigning the parameters of hidden nodes in the SLFN, the competitive performance can be obtained on the optimization task for TDOA measurement error. Then, based on that result, Taylor algorithm is implemented to deal with the position problem of mobile station. Experimental results show that the effect of NLOS propagation is reduced based on our proposed algorithm by introducing the ELM into Taylor algorithm. Moreover, in the simulation, the proposed approach, called Taylor-ELM, provides better performance compared with some traditional algorithms, such as least squares, Taylor, backpropagation neural network based Taylor, and Chan positioning methods.

Publication: IEICE TRANSACTIONS on Information Vol.E97-D No.10 pp.2652-2659

Publication Date: 2014/10/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2013THP0019

Type of Manuscript: Special Section PAPER (Special Section on Frontiers of Internet of Things)

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Authors

Xiong LUO
  University of Science and Technology Beijing (USTB),Beijing Key Laboratory of Knowledge Engineering for Materials Science
Xiaohui CHANG
  University of Science and Technology Beijing (USTB),Beijing Key Laboratory of Knowledge Engineering for Materials Science
Hong LIU
  Beihang University

Keyword

wireless sensor network, time difference of arrival, extreme learning machine, non-line-of-sight

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Xiong LUO, Xiaohui CHANG, Hong LIU, "A Taylor Based Localization Algorithm for Wireless Sensor Network Using Extreme Learning Machine" in IEICE TRANSACTIONS on Information, vol. E97-D, no. 10, pp. 2652-2659, October 2014, doi: 10.1587/transinf.2013THP0019.
Abstract: More recently, there has been a growing interest in the study of wireless sensor network (WSN) technologies for Interest of Things (IoT). To improve the positioning accuracy of mobile station under the non-line-of-sight (NLOS) environment, a localization algorithm based on the single-hidden layer feedforward network (SLFN) using extreme learning machine (ELM) for WSN is proposed in this paper. Optimal reduction in the time difference of arrival (TDOA) measurement error is achieved using SLFN optimized by ELM. Compared with those traditional learning algorithms, ELM has its unique feature of a higher generalization capability at a much faster learning speed. After utilizing the ELM by randomly assigning the parameters of hidden nodes in the SLFN, the competitive performance can be obtained on the optimization task for TDOA measurement error. Then, based on that result, Taylor algorithm is implemented to deal with the position problem of mobile station. Experimental results show that the effect of NLOS propagation is reduced based on our proposed algorithm by introducing the ELM into Taylor algorithm. Moreover, in the simulation, the proposed approach, called Taylor-ELM, provides better performance compared with some traditional algorithms, such as least squares, Taylor, backpropagation neural network based Taylor, and Chan positioning methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2013THP0019/_p

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@ARTICLE{e97-d_10_2652,
author={Xiong LUO, Xiaohui CHANG, Hong LIU, },
journal={IEICE TRANSACTIONS on Information},
title={A Taylor Based Localization Algorithm for Wireless Sensor Network Using Extreme Learning Machine},
year={2014},
volume={E97-D},
number={10},
pages={2652-2659},
abstract={More recently, there has been a growing interest in the study of wireless sensor network (WSN) technologies for Interest of Things (IoT). To improve the positioning accuracy of mobile station under the non-line-of-sight (NLOS) environment, a localization algorithm based on the single-hidden layer feedforward network (SLFN) using extreme learning machine (ELM) for WSN is proposed in this paper. Optimal reduction in the time difference of arrival (TDOA) measurement error is achieved using SLFN optimized by ELM. Compared with those traditional learning algorithms, ELM has its unique feature of a higher generalization capability at a much faster learning speed. After utilizing the ELM by randomly assigning the parameters of hidden nodes in the SLFN, the competitive performance can be obtained on the optimization task for TDOA measurement error. Then, based on that result, Taylor algorithm is implemented to deal with the position problem of mobile station. Experimental results show that the effect of NLOS propagation is reduced based on our proposed algorithm by introducing the ELM into Taylor algorithm. Moreover, in the simulation, the proposed approach, called Taylor-ELM, provides better performance compared with some traditional algorithms, such as least squares, Taylor, backpropagation neural network based Taylor, and Chan positioning methods.},
keywords={},
doi={10.1587/transinf.2013THP0019},
ISSN={1745-1361},
month={October},}

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TY - JOUR
TI - A Taylor Based Localization Algorithm for Wireless Sensor Network Using Extreme Learning Machine
T2 - IEICE TRANSACTIONS on Information
SP - 2652
EP - 2659
AU - Xiong LUO
AU - Xiaohui CHANG
AU - Hong LIU
PY - 2014
DO - 10.1587/transinf.2013THP0019
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2014
AB - More recently, there has been a growing interest in the study of wireless sensor network (WSN) technologies for Interest of Things (IoT). To improve the positioning accuracy of mobile station under the non-line-of-sight (NLOS) environment, a localization algorithm based on the single-hidden layer feedforward network (SLFN) using extreme learning machine (ELM) for WSN is proposed in this paper. Optimal reduction in the time difference of arrival (TDOA) measurement error is achieved using SLFN optimized by ELM. Compared with those traditional learning algorithms, ELM has its unique feature of a higher generalization capability at a much faster learning speed. After utilizing the ELM by randomly assigning the parameters of hidden nodes in the SLFN, the competitive performance can be obtained on the optimization task for TDOA measurement error. Then, based on that result, Taylor algorithm is implemented to deal with the position problem of mobile station. Experimental results show that the effect of NLOS propagation is reduced based on our proposed algorithm by introducing the ELM into Taylor algorithm. Moreover, in the simulation, the proposed approach, called Taylor-ELM, provides better performance compared with some traditional algorithms, such as least squares, Taylor, backpropagation neural network based Taylor, and Chan positioning methods.
ER -