A Practical Indoor Localization System with Distributed Data Acquisition for Large Exhibition Venues

Hao Li*, Joseph K Y NG, Shuwei Qiu

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingChapterpeer-review

Abstract

In this paper, we focus on the Wi-Fi based indoor localization in large exhibition venues. We identify and describe the real-world problems in this scenario and present our system. We adopt a passive way to detect mobile devices with the consideration of users’ preference and iOS devices’ privacy issue, and collect signal strength data in a distributed manner which meets the practical demand in exhibition venues and save the power consumption of mobile devices. Since exhibition venues have many restrictions on traditional localization approaches, we propose our approach and solution to fit these special conditions. We propose the clustering and Gaussian process regression (GPR) method to improve localization accuracy. Series of experiments in Hong Kong Convention and Exhibition Centre (HKCEC) show our system’s feasibility and effectiveness. Our approach has significant improvement in the localization accuracy when compared with traditional trilateration, fingerprinting and the state-of-the-art approaches.

Original languageEnglish
Title of host publicationLecture Notes on Data Engineering and Communications Technologies
PublisherSpringer Science and Business Media Deutschland GmbH
Pages568-577
Number of pages10
DOIs
Publication statusPublished - 2019

Publication series

NameLecture Notes on Data Engineering and Communications Technologies
Volume22
ISSN (Print)2367-4512
ISSN (Electronic)2367-4520

Scopus Subject Areas

  • Media Technology
  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems

User-Defined Keywords

  • Clustering
  • Distributed data acquisition
  • Exhibition venues
  • Gaussian process regression
  • Wi-Fi localization

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