RSS difference-aware graph-based semi-supervised learning (RG-SSL) RSS smoothing method for crowdsourcing indoor localization

Liye Zhang; Shahrokh Valaee; Yubin Xu; Lin Ma; Le Zhang

doi:10.1109/GlobalSIP.2015.7418216

RSS difference-aware graph-based semi-supervised learning (RG-SSL) RSS smoothing method for crowdsourcing indoor localization

Liye Zhang
, Shahrokh Valaee
, Yubin Xu
, Lin Ma
, Le Zhang

School of Electronics and Information Engineering

Harbin Institute of Technology
University of Toronto
Ministry of Public Security of the People's Republic of China

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

In order to realize the rapid deployment of indoor localization systems, the crowdsourcing method has been proposed to reduce the collection workload. However, compared to conventional methods, the reduced number of received signal strength (RSS) values lends greater influence to noises and erroneous measurements in RSS values. In this paper, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation of RSS values at nearby locations to infer an optimal RSS value at each location in terms of error. The RSS difference between different locations is used as a part of cost function to improve the performance of G-SSL. Experimental results show that the proposed method results in a smoother radio map and improved localization accuracy.

Original language	English
Title of host publication	2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	353-357
Number of pages	5
ISBN (Electronic)	9781479975914
DOIs	https://doi.org/10.1109/GlobalSIP.2015.7418216
State	Published - 23 Feb 2016
Event	IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 - Orlando, United States Duration: 13 Dec 2015 → 16 Dec 2015

Publication series

Name	2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015

Conference

Conference	IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
Country/Territory	United States
City	Orlando
Period	13/12/15 → 16/12/15

Access to Document

10.1109/GlobalSIP.2015.7418216

Cite this

Zhang, L., Valaee, S., Xu, Y., Ma, L., & Zhang, L. (2016). RSS difference-aware graph-based semi-supervised learning (RG-SSL) RSS smoothing method for crowdsourcing indoor localization. In 2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 (pp. 353-357). Article 7418216 (2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2015.7418216

Zhang, Liye ; Valaee, Shahrokh ; Xu, Yubin et al. / RSS difference-aware graph-based semi-supervised learning (RG-SSL) RSS smoothing method for crowdsourcing indoor localization. 2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 353-357 (2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015).

@inproceedings{a23988126c894b3eb97a273f566039ca,

title = "RSS difference-aware graph-based semi-supervised learning (RG-SSL) RSS smoothing method for crowdsourcing indoor localization",

abstract = "In order to realize the rapid deployment of indoor localization systems, the crowdsourcing method has been proposed to reduce the collection workload. However, compared to conventional methods, the reduced number of received signal strength (RSS) values lends greater influence to noises and erroneous measurements in RSS values. In this paper, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation of RSS values at nearby locations to infer an optimal RSS value at each location in terms of error. The RSS difference between different locations is used as a part of cost function to improve the performance of G-SSL. Experimental results show that the proposed method results in a smoother radio map and improved localization accuracy.",

author = "Liye Zhang and Shahrokh Valaee and Yubin Xu and Lin Ma and Le Zhang",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 ; Conference date: 13-12-2015 Through 16-12-2015",

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doi = "10.1109/GlobalSIP.2015.7418216",

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series = "2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015",

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booktitle = "2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015",

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Zhang, L, Valaee, S, Xu, Y, Ma, L & Zhang, L 2016, RSS difference-aware graph-based semi-supervised learning (RG-SSL) RSS smoothing method for crowdsourcing indoor localization. in 2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015., 7418216, 2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015, Institute of Electrical and Electronics Engineers Inc., pp. 353-357, IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015, Orlando, United States, 13/12/15. https://doi.org/10.1109/GlobalSIP.2015.7418216

RSS difference-aware graph-based semi-supervised learning (RG-SSL) RSS smoothing method for crowdsourcing indoor localization. / Zhang, Liye; Valaee, Shahrokh; Xu, Yubin et al.
2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 353-357 7418216 (2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - RSS difference-aware graph-based semi-supervised learning (RG-SSL) RSS smoothing method for crowdsourcing indoor localization

AU - Zhang, Liye

AU - Valaee, Shahrokh

AU - Xu, Yubin

AU - Ma, Lin

AU - Zhang, Le

PY - 2016/2/23

Y1 - 2016/2/23

N2 - In order to realize the rapid deployment of indoor localization systems, the crowdsourcing method has been proposed to reduce the collection workload. However, compared to conventional methods, the reduced number of received signal strength (RSS) values lends greater influence to noises and erroneous measurements in RSS values. In this paper, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation of RSS values at nearby locations to infer an optimal RSS value at each location in terms of error. The RSS difference between different locations is used as a part of cost function to improve the performance of G-SSL. Experimental results show that the proposed method results in a smoother radio map and improved localization accuracy.

AB - In order to realize the rapid deployment of indoor localization systems, the crowdsourcing method has been proposed to reduce the collection workload. However, compared to conventional methods, the reduced number of received signal strength (RSS) values lends greater influence to noises and erroneous measurements in RSS values. In this paper, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation of RSS values at nearby locations to infer an optimal RSS value at each location in terms of error. The RSS difference between different locations is used as a part of cost function to improve the performance of G-SSL. Experimental results show that the proposed method results in a smoother radio map and improved localization accuracy.

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015

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ER -

Zhang L, Valaee S, Xu Y, Ma L, Zhang L. RSS difference-aware graph-based semi-supervised learning (RG-SSL) RSS smoothing method for crowdsourcing indoor localization. In 2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 353-357. 7418216. (2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015). doi: 10.1109/GlobalSIP.2015.7418216

RSS difference-aware graph-based semi-supervised learning (RG-SSL) RSS smoothing method for crowdsourcing indoor localization

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