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IJML 2012 Vol.2(1): 83-88
doi: 10.7763/IJMLC.2012.V2.94

Intelligent Anti-Theft and Tracking System for Automobiles

Montaser N. Ramadan1 , Mohammad A. Al-Khedher1 , Sharaf A. Al-Kheder2

  • 1Mechatronics Engineering Department, Al-balqa Applied University, Amman 11134, Jordan.
  • 2Department Of Civil Engineering, Faculty Of Engineering & Applied Sciences, ALHOSN University, Abu Dhabi, UAE.

Abstract

An efficient automotive security system is implemented for anti-theft using an embedded system occupied with a Global Positioning System (GPS) and a Global System of Mobile (GSM). The client interacts through this system with vehicles and determines their current locations and status using Google Earth. The user can track the position of targeted vehicles on Google Earth. Using GPS locator, the target current location is determined and sent, along with various parameters received by vehicle’s data port, via Short Message Service (SMS) through GSM networks to a GSM modem that is connected to PC or laptop. The GPS coordinates are corrected using a discrete Kalman filter. To secure the vehicle, the user of a group of users can turn off any vehicle of the fleet if any intruders try to run it by blocking the gas feeding line. This system is very safe and efficient to report emergency situations as crash reporting or engine failure.

Keywords

  • Vehicle tracker
  • embedded system
  • GSM
  • GPS
  • Google earth
  • kalman filter
94-T043

How to Cite

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Montaser N. Ramadan, Mohammad A. Al-Khedher, and Sharaf A. Al-Kheder, "Intelligent Anti-Theft and Tracking System for Automobiles," International Journal of Machine Learning, vol. 2, no. 1, pp. 83-88, 2012. https://doi.org/10.7763/IJMLC.2012.V2.94

Copyright & License

Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • DOI: 10.18178/IJML
  • Frequency: Quarterly
  • Average Days to Accept: 68 days
  • Acceptance Rate: 27%
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • DOI: 10.18178/IJML
  • Abstracing/Indexing: Google Scholar, Crossref, ProQuest, Electronic Journals Library, CNKI.
  • E-mail: editor@ijml.org
  • APC: 500USD