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 proceedingConference contributionpeer-review

25 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 languageEnglish
Title of host publicationInternational Conference on Information and Communication Technology Convergence
Subtitle of host publicationICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages274-277
Number of pages4
ISBN (Electronic)9781509040315
DOIs
Publication statusPublished - 12 Dec 2017
Externally publishedYes
Event8th International Conference on Information and Communication Technology Convergence, ICTC 2017 - Jeju Island, Korea, Republic of
Duration: 18 Oct 201720 Oct 2017

Publication series

NameInternational Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
Volume2017-December

Conference

Conference8th International Conference on Information and Communication Technology Convergence, ICTC 2017
Country/TerritoryKorea, Republic of
CityJeju Island
Period18/10/1720/10/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

  • Bayesian
  • fingerprinting
  • indoor localization

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