CFP last date
20 January 2025
Reseach Article

Real Map Virtualization for Indoor Positioning using Smartphone

by Hamid Mohammed Ali, Alaa Hamza Omran
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 112 - Number 1
Year of Publication: 2015
Authors: Hamid Mohammed Ali, Alaa Hamza Omran
10.5120/19630-1201

Hamid Mohammed Ali, Alaa Hamza Omran . Real Map Virtualization for Indoor Positioning using Smartphone. International Journal of Computer Applications. 112, 1 ( February 2015), 23-28. DOI=10.5120/19630-1201

@article{ 10.5120/19630-1201,
author = { Hamid Mohammed Ali, Alaa Hamza Omran },
title = { Real Map Virtualization for Indoor Positioning using Smartphone },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 112 },
number = { 1 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 23-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume112/number1/19630-1201/ },
doi = { 10.5120/19630-1201 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:48:17.792818+05:30
%A Hamid Mohammed Ali
%A Alaa Hamza Omran
%T Real Map Virtualization for Indoor Positioning using Smartphone
%J International Journal of Computer Applications
%@ 0975-8887
%V 112
%N 1
%P 23-28
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Indoor maps are highly essential for indoor positioning and location-based services. People existing in big building such as mall, airport and hospitals leads to rapid growth in location based applications. Todays, typical indoor positioning systems employ a training/positioning model using Wi-Fi fingerprints. While these approaches have practical results in terms of coverage, they require an indoor map, which is rarely available to the user and involves significant training costs. In this paper, we present a virtual map, generated automatically inside user smartphone, which lets the user watching her/himself moving forward, backward, left or right from well-known landmarks on the floor map. This map is generated in the form of rectangle of grid points with no prior information of the actual indoor map; therefore, it is suitable for using it at any building. An indoor positioning system based on Smartphone sensor (Accelerometer and Magnetometer) incorporated with existing APs in the building is used to test the suggested virtual map.

References
  1. Balas, 2011, Indoor Localization of Mobile Device for a Wireless Monitoring System based on Crowdsourcing, M. sc Thesis, University of Edinburgh, Edinburgh, United Kingdom.
  2. Kothari N. , Kannan B. , D. Glasgwow E. and B. Dias M. , 2012, Robust Indoor Localization on a Commercial Smartphone, Computer Science, Elsevier, Vol. 10, pp. 1114-1120.
  3. Xiao Z. , Wen H. , Markham A. and Trigoni N. , 2014, Lightweight Map Matching for Indoor Localization using Conditional Random Fields, IEEE.
  4. Li F. , Zhao C. , Ding G. , Gong J. , Lui C. and Zhao F. , 2012, A Reliable and Accurate Indoor Localization Method Using Phone Inertial Sensors, ACM.
  5. Wan S. and Foxlin E. , 2001, Improved pedestrian navigation based on drift reduced memsimu chip, in Proceedings of the 2010 International Technical Meeting of The Institute of Navigation, pp. 220–229.
  6. Angermann M. and Robertson P. , 2012, Footslam: Pedestrian simultaneous localization and mapping without exteroceptive sensorshitchhiking on human perception and cognition, Proceedings of the IEEE, vol. 100, no. Special Centennial Issue, pp. 1840–1848.
  7. Constandache I. , Choudhury R. , and Rhee I. , 2010, Towards mobile phone localization without war-driving, presented at the Proc. IEEE Conf. Comput. Commun. , San Diego, CA, Mar.
  8. Durrant-Whyte H. and Bailey T. , 2006, Simultaneous localization and mapping: part I. Robotics Automation Magazine, IEEE, 2006, Simultaneous localization and mapping: part I. Robotics Automation Magazine, IEEE.
  9. Shin H. , Chon Y. , and Cha H. , 2012, Unsupervised construction of an indoor floor plan using a smartphone. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE.
  10. Alzantot M. and Youssef M. , 2012, Crowdinside: automatic construction of indoor floorplans, in Proceedings of the 20th International Conference on Advances in Geographic Information Systems. ACM, pp. 99–108.
  11. Jiang Y. , Xiang Y. , Pan X. , Li K. , Lv Q. , P. Dick R. , Shang L. , and Hannigan M. , 2013, Hallway based automatic indoor floorplan construction using room fingerprints, in Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing. ACM, pp. 315–324.
  12. Yang Z. , Wu C. , and Liu Y. , 2012, Locating in fingerprint space: wireless indoor localization with little human intervention, In MobiCom '12.
  13. Calis G. , BECER_IK-GERBER B. , B. GOKTEPE A. , Lie S. and Lie N. , 2013, Analysis of the variability of RSSI values for active RFID-based indoor applications, Turkish Journal of Engineering & Environmental Sciences, Vol. 37, pp. 186-210.
Index Terms

Computer Science
Information Sciences

Keywords

Indoor Localization Indoor Positioning System Smart Phones fingerprinting virtual map.