CFP last date
22 July 2024
Reseach Article

Real Time Location based Tracking using WIFI Signals

by Atul Gosai, Rushi Raval
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 101 - Number 5
Year of Publication: 2014
Authors: Atul Gosai, Rushi Raval
10.5120/17684-8542

Atul Gosai, Rushi Raval . Real Time Location based Tracking using WIFI Signals. International Journal of Computer Applications. 101, 5 ( September 2014), 21-26. DOI=10.5120/17684-8542

@article{ 10.5120/17684-8542,
author = { Atul Gosai, Rushi Raval },
title = { Real Time Location based Tracking using WIFI Signals },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 101 },
number = { 5 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 21-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume101/number5/17684-8542/ },
doi = { 10.5120/17684-8542 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:30:54.847587+05:30
%A Atul Gosai
%A Rushi Raval
%T Real Time Location based Tracking using WIFI Signals
%J International Journal of Computer Applications
%@ 0975-8887
%V 101
%N 5
%P 21-26
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now-a-days the difficult to tracking the mobile devices has become an issue. Various needs are arising for finding out ways of tracking mobile devices. In this research paper included different algorithm for location tracking. The activity of tracking includes learning and inference, sensing. Different algorithms have different mechanisms based on which the tracking is made possible. For tracking a device in nearby place efforts required are less than from far-away places. Algorithms include their own mechanisms for tracking the devices easily. Some of the algorithms are simple in nature while others are complex. The cost incurred for tracking devices differs when used via wireless against wired networks. Wireless technology is beneficial for tracking the devices in close areas easily. Wire-less technology such as WI-FI becomes very helpful in such cases. For tracking devices in indoor places the system named WITS (Wireless Indoor Tracking System) is used. WLAN based location tracking algorithms is categorized into two types: deterministic and probabilistic. In this paper algorithm such as Bayesian algorithm, nearest neighbor algorithm, History-based tracking algorithm, H. M. M. (Hidden Markov model), RADAR etc. is explained. The use of wireless 802. 11 frameworks is done to locate the devices. Moreover the WI-FI technology can also be used in forest area for tracking animals. WI-FI signals are much useful in places where the wired connections are not possible to set up. Despite of the use of WI-FI signals in location tracking, they are also used in tracking Bar Code stickers. Some of the WI-FI based techniques are only software based so it decreases the cost of hardware maintenance. Radio-frequency based tracking in WLAN signals has gained more and more popularity in recent years. In early days the WLAN was used to track only static devices but later on by making advances in the technology it was possible to track the moving devices as well.

References
  1. F. Barcelo1-Arroyo et al, Indoor Location for Safety Applications using Wireless Networks, in Proceeding of the 1st ERCIM Workshop on Mobility, Portugal, 2014.
  2. A. Haeberlen, E. Flannery, A. M. Ladd, A. Rudys, D. S. Wallach, and L. E. Kavraki, Practical robust localization.
  3. Gardner, W. A. , Chih-kang Chen, Signal-selective time-difference-of-arrival estimation for passive location of man-made signal sources in highly corruptive environments. I. Theory and method, Signal Processing, IEEE Transactions on Vol: 40, Issue: 5, August, 2002
  4. Tseng, Y. C. , Wu, S. L. , Liao, W. H. , and Chao, C. M. IEEE Computer (34:6), February 2014, pp. 46-52.
  5. Tracking system in Proceedings of IEEE Infocom.
  6. Fox, D. ; Hightower, J. ; Liao, L. ; Schulz, D. ; and Borriello, G. 2014, Bayesian filtering for location estimation, IEEE Pervasive Computing Magazine.
  7. Haeberlen, A. ; Flannery, E Ladd, A. ; Rudys, A. ; Wallach, 10th ACM International Conference on Mobile Computing and Networking.
  8. Krishnan, P. , Krishnakumar, A. ; Ju, W. H. ; Mallows, C. ; and Gani, S. 2014. Location estimation assisted by stationary emitters for indoor RF wireless networks. In Proc. of the IEEE Infocom
  9. Ladd, A. ; Bekris, K. ; Marceau, G. ; Rudys, A. ; Kavraki, L. ; and Wallach, D. 2002. Robotics-based location sensing using Wireless Ethernet, In Proceedings of MOBICOM-2002
  10. LaMarca, A. ; Chawathe, Y. ; Consolvo, S. ; Hightower, J. Smith, I. ; Scott, J. ; Sohn, T. ; Howard, J. ; Hughes, J. ; Potter, F. ; In International Conference on Pervasive Computing, Munich, Germany, 8-13 May 2005, pp. 116-133.
  11. Lauritzen, S. 1996. Graphical Models Oxford University Press, Liao, L. ; Fox, D: Hightower, J. ; Kautz, H. ; and Schulz, D. 2014. Voronoi tracking: Location estimation using sparse and noisy sensor data. In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  12. E. Foxlin2, "Pedestrian Tracking with Shoe-Mounted Inertial Sensors.
  13. P. Bahl, V. Padmanabhan, RADAR: an in-building RF-based user location and tracking system, in Proceedings of the IEEE Infocom 2000.
  14. A. Carlotto, M. Parodi, C. Bonamico, F. Lavagetto, M. Valla Proximity classification for mobile devices using Wi-Fi environment similarity.
  15. R. Zhou, Wireless Indoor Tracking System (WITS), Communication systems/Computing Centre, University of Freiburg
  16. L. Ni, Y. Liu, Y. Lau, and A. Patil, "LANDMARC: Indoor Location Sensing using Active RFID," Proceedings First IEEE International Conf. Pervasive Computing and Communication, pp. 407-416, Mar. 2003.
  17. Eoghan Furey, Kevin Curran, Paul Mc Kevitt , HABITS: A History Aware Based Wi-Fi Indoor Tracking System, PGNET conference, 2008.
  18. G. A. Fink. , Markov Models for pattern recognition, Advances in Computer vision and pattern recognition, DOI 10. 1007/978-1-4471-6308-4_5, Springer-Verlag, London, 2014.
  19. John Krumm, Eric Horvitz, Microsoft research, LOCADIO: Inferring Motion and Location from Wi-Fi Signal Strengths, August 22-26, 2004, Boston, MA, USA
  20. Scott cost, Steven salzberg, A weighted nearest neighbor algorithm for learning with symbolic future, Machine Learning, 10, 57-78.
Index Terms

Computer Science
Information Sciences

Keywords

WI-FI Location tracking Bayesian algorithm Wireless LAN Wireless Indoor Tracking System (W I T S) Hidden Markov Model (H M M)