Improving accuracy in indoor localization system using fingerprinting technique

Saddam Alraih; Abdulraqeb Alhammadi; Ibraheem Shayea; Ahmed M. Al-Samman

doi:10.1109/ICTC.2017.8190985

Improving accuracy in indoor localization system using fingerprinting technique

Saddam Alraih, Abdulraqeb Alhammadi, Ibraheem Shayea, Ahmed M. Al-Samman

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

27 Citations (Scopus)

Abstract

Indoor localization system is an important topic in the wireless navigation system. Many technologies, such as Wi-Fi, Bluetooth, and ZigBee, have been developed for the indoor localization system; however, these systems still demonstrate poor accuracy and performance. The indoor localization system using Wi-Fi signals is the best option for indoor localization because most of the buildings are covered by Wi-Fi access points. In this paper, we proposed a clustering algorithm to improve the system accuracy for indoor environment and reduce computational time. Fingerprinting technique was used to evaluate the performance of the proposed algorithm. Results show that the clustering algorithm achieves an average distance error of 2.4 m compared with the non-clustering algorithm, which has an average distance error of 3.4 m. In particular, the clustering algorithm improves the system accuracy by 41% compared with the conventional algorithm. Moreover, the clustering algorithm reduces the computational time of the system that requires computing the prediction of mobile user location.

Original language	English
Title of host publication	International Conference on Information and Communication Technology Convergence
Subtitle of host publication	ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	274-277
Number of pages	4
ISBN (Electronic)	9781509040315
DOIs	https://doi.org/10.1109/ICTC.2017.8190985
Publication status	Published - 12 Dec 2017
Externally published	Yes
Event	8th International Conference on Information and Communication Technology Convergence, ICTC 2017 - Jeju Island, Korea, Republic of Duration: 18 Oct 2017 → 20 Oct 2017

Publication series

Name	International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
Volume	2017-December

Conference

Conference	8th International Conference on Information and Communication Technology Convergence, ICTC 2017
Country/Territory	Korea, Republic of
City	Jeju Island
Period	18/10/17 → 20/10/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Bayesian
fingerprinting
indoor localization

Access to Document

10.1109/ICTC.2017.8190985

Cite this

Alraih, S., Alhammadi, A., Shayea, I., & Al-Samman, A. M. (2017). Improving accuracy in indoor localization system using fingerprinting technique. In International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017 (pp. 274-277). (International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017; Vol. 2017-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICTC.2017.8190985

Alraih, Saddam ; Alhammadi, Abdulraqeb ; Shayea, Ibraheem et al. / Improving accuracy in indoor localization system using fingerprinting technique. International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 274-277 (International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017).

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abstract = "Indoor localization system is an important topic in the wireless navigation system. Many technologies, such as Wi-Fi, Bluetooth, and ZigBee, have been developed for the indoor localization system; however, these systems still demonstrate poor accuracy and performance. The indoor localization system using Wi-Fi signals is the best option for indoor localization because most of the buildings are covered by Wi-Fi access points. In this paper, we proposed a clustering algorithm to improve the system accuracy for indoor environment and reduce computational time. Fingerprinting technique was used to evaluate the performance of the proposed algorithm. Results show that the clustering algorithm achieves an average distance error of 2.4 m compared with the non-clustering algorithm, which has an average distance error of 3.4 m. In particular, the clustering algorithm improves the system accuracy by 41% compared with the conventional algorithm. Moreover, the clustering algorithm reduces the computational time of the system that requires computing the prediction of mobile user location.",

keywords = "Bayesian, fingerprinting, indoor localization",

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Alraih, S, Alhammadi, A, Shayea, I & Al-Samman, AM 2017, Improving accuracy in indoor localization system using fingerprinting technique. in International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017. International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017, vol. 2017-December, Institute of Electrical and Electronics Engineers Inc., pp. 274-277, 8th International Conference on Information and Communication Technology Convergence, ICTC 2017, Jeju Island, Korea, Republic of, 18/10/17. https://doi.org/10.1109/ICTC.2017.8190985

Improving accuracy in indoor localization system using fingerprinting technique. / Alraih, Saddam; Alhammadi, Abdulraqeb; Shayea, Ibraheem et al.
International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 274-277 (International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017; Vol. 2017-December).

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

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AU - Alhammadi, Abdulraqeb

AU - Shayea, Ibraheem

AU - Al-Samman, Ahmed M.

PY - 2017/12/12

Y1 - 2017/12/12

N2 - Indoor localization system is an important topic in the wireless navigation system. Many technologies, such as Wi-Fi, Bluetooth, and ZigBee, have been developed for the indoor localization system; however, these systems still demonstrate poor accuracy and performance. The indoor localization system using Wi-Fi signals is the best option for indoor localization because most of the buildings are covered by Wi-Fi access points. In this paper, we proposed a clustering algorithm to improve the system accuracy for indoor environment and reduce computational time. Fingerprinting technique was used to evaluate the performance of the proposed algorithm. Results show that the clustering algorithm achieves an average distance error of 2.4 m compared with the non-clustering algorithm, which has an average distance error of 3.4 m. In particular, the clustering algorithm improves the system accuracy by 41% compared with the conventional algorithm. Moreover, the clustering algorithm reduces the computational time of the system that requires computing the prediction of mobile user location.

AB - Indoor localization system is an important topic in the wireless navigation system. Many technologies, such as Wi-Fi, Bluetooth, and ZigBee, have been developed for the indoor localization system; however, these systems still demonstrate poor accuracy and performance. The indoor localization system using Wi-Fi signals is the best option for indoor localization because most of the buildings are covered by Wi-Fi access points. In this paper, we proposed a clustering algorithm to improve the system accuracy for indoor environment and reduce computational time. Fingerprinting technique was used to evaluate the performance of the proposed algorithm. Results show that the clustering algorithm achieves an average distance error of 2.4 m compared with the non-clustering algorithm, which has an average distance error of 3.4 m. In particular, the clustering algorithm improves the system accuracy by 41% compared with the conventional algorithm. Moreover, the clustering algorithm reduces the computational time of the system that requires computing the prediction of mobile user location.

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Alraih S, Alhammadi A, Shayea I, Al-Samman AM. Improving accuracy in indoor localization system using fingerprinting technique. In International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 274-277. (International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017). doi: 10.1109/ICTC.2017.8190985

Improving accuracy in indoor localization system using fingerprinting technique

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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