A Robust Noise Mitigation Method for the Mobile RFID Location in Built Environment

Changfeng Jing, Tiancheng Sun, Qiang Chen, Mingyi Du, Mingshu Wang, Shouqing Wang, Jian Wang

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
10 Downloads (Pure)

Abstract

The exact location of objects, such as infrastructure, is crucial to the systematic understanding of the built environment. The emergence and development of the Internet of Things (IoT) have attracted growing attention to the low-cost location scheme, which can respond to a dramatic increasing amount of public infrastructure in smart cities. Various Radio Frequency IDentification (RFID)-based locating systems and noise mitigation methods have been developed. However, most of them are impractical for built environments in large areas due to their high cost, computational complexity, and low noise detection capability. In this paper, we proposed a novel noise mitigation solution integrating the low-cost localization scheme with one mobile RFID reader. We designed a filter algorithm to remove the influence of abnormal data. Inspired the sampling concept, a more carefully parameters calibration was carried out for noise data sampling to improve the accuracy and reduce the computational complexity. To achieve robust noise detection results, we employed the powerful noise detection capability of the random sample consensus (RANSAC) algorithm. Our experiments demonstrate the effectiveness and advantages of the proposed method for the localization and noise mitigation in a large area. The proposed scheme has potential applications for location-based services in smart cities.
Original languageEnglish
Article number2143
Pages (from-to)1-16
Number of pages16
JournalSensors (Switserland)
Volume19
Issue number9
DOIs
Publication statusPublished - 1 May 2019

Keywords

  • Radio frequency identification (RFID)
  • Low-cost localization
  • Noise mitigation
  • Localization error
  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-GOLD

Cite this

Jing, Changfeng ; Sun, Tiancheng ; Chen, Qiang ; Du, Mingyi ; Wang, Mingshu ; Wang, Shouqing ; Wang, Jian. / A Robust Noise Mitigation Method for the Mobile RFID Location in Built Environment. In: Sensors (Switserland). 2019 ; Vol. 19, No. 9. pp. 1-16.
@article{1378fca9694141ccb18560f9142f1113,
title = "A Robust Noise Mitigation Method for the Mobile RFID Location in Built Environment",
abstract = "The exact location of objects, such as infrastructure, is crucial to the systematic understanding of the built environment. The emergence and development of the Internet of Things (IoT) have attracted growing attention to the low-cost location scheme, which can respond to a dramatic increasing amount of public infrastructure in smart cities. Various Radio Frequency IDentification (RFID)-based locating systems and noise mitigation methods have been developed. However, most of them are impractical for built environments in large areas due to their high cost, computational complexity, and low noise detection capability. In this paper, we proposed a novel noise mitigation solution integrating the low-cost localization scheme with one mobile RFID reader. We designed a filter algorithm to remove the influence of abnormal data. Inspired the sampling concept, a more carefully parameters calibration was carried out for noise data sampling to improve the accuracy and reduce the computational complexity. To achieve robust noise detection results, we employed the powerful noise detection capability of the random sample consensus (RANSAC) algorithm. Our experiments demonstrate the effectiveness and advantages of the proposed method for the localization and noise mitigation in a large area. The proposed scheme has potential applications for location-based services in smart cities.",
keywords = "Radio frequency identification (RFID), Low-cost localization, Noise mitigation, Localization error, ITC-ISI-JOURNAL-ARTICLE, ITC-GOLD",
author = "Changfeng Jing and Tiancheng Sun and Qiang Chen and Mingyi Du and Mingshu Wang and Shouqing Wang and Jian Wang",
year = "2019",
month = "5",
day = "1",
doi = "10.3390/s19092143",
language = "English",
volume = "19",
pages = "1--16",
journal = "Sensors (Switserland)",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute",
number = "9",

}

A Robust Noise Mitigation Method for the Mobile RFID Location in Built Environment. / Jing, Changfeng; Sun, Tiancheng; Chen, Qiang; Du, Mingyi; Wang, Mingshu; Wang, Shouqing; Wang, Jian.

In: Sensors (Switserland), Vol. 19, No. 9, 2143, 01.05.2019, p. 1-16.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - A Robust Noise Mitigation Method for the Mobile RFID Location in Built Environment

AU - Jing, Changfeng

AU - Sun, Tiancheng

AU - Chen, Qiang

AU - Du, Mingyi

AU - Wang, Mingshu

AU - Wang, Shouqing

AU - Wang, Jian

PY - 2019/5/1

Y1 - 2019/5/1

N2 - The exact location of objects, such as infrastructure, is crucial to the systematic understanding of the built environment. The emergence and development of the Internet of Things (IoT) have attracted growing attention to the low-cost location scheme, which can respond to a dramatic increasing amount of public infrastructure in smart cities. Various Radio Frequency IDentification (RFID)-based locating systems and noise mitigation methods have been developed. However, most of them are impractical for built environments in large areas due to their high cost, computational complexity, and low noise detection capability. In this paper, we proposed a novel noise mitigation solution integrating the low-cost localization scheme with one mobile RFID reader. We designed a filter algorithm to remove the influence of abnormal data. Inspired the sampling concept, a more carefully parameters calibration was carried out for noise data sampling to improve the accuracy and reduce the computational complexity. To achieve robust noise detection results, we employed the powerful noise detection capability of the random sample consensus (RANSAC) algorithm. Our experiments demonstrate the effectiveness and advantages of the proposed method for the localization and noise mitigation in a large area. The proposed scheme has potential applications for location-based services in smart cities.

AB - The exact location of objects, such as infrastructure, is crucial to the systematic understanding of the built environment. The emergence and development of the Internet of Things (IoT) have attracted growing attention to the low-cost location scheme, which can respond to a dramatic increasing amount of public infrastructure in smart cities. Various Radio Frequency IDentification (RFID)-based locating systems and noise mitigation methods have been developed. However, most of them are impractical for built environments in large areas due to their high cost, computational complexity, and low noise detection capability. In this paper, we proposed a novel noise mitigation solution integrating the low-cost localization scheme with one mobile RFID reader. We designed a filter algorithm to remove the influence of abnormal data. Inspired the sampling concept, a more carefully parameters calibration was carried out for noise data sampling to improve the accuracy and reduce the computational complexity. To achieve robust noise detection results, we employed the powerful noise detection capability of the random sample consensus (RANSAC) algorithm. Our experiments demonstrate the effectiveness and advantages of the proposed method for the localization and noise mitigation in a large area. The proposed scheme has potential applications for location-based services in smart cities.

KW - Radio frequency identification (RFID)

KW - Low-cost localization

KW - Noise mitigation

KW - Localization error

KW - ITC-ISI-JOURNAL-ARTICLE

KW - ITC-GOLD

UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2019/isi/wang_rob.pdf

UR - http://www.scopus.com/inward/record.url?scp=85065933542&partnerID=8YFLogxK

U2 - 10.3390/s19092143

DO - 10.3390/s19092143

M3 - Article

VL - 19

SP - 1

EP - 16

JO - Sensors (Switserland)

JF - Sensors (Switserland)

SN - 1424-8220

IS - 9

M1 - 2143

ER -