RFID technology for vehicle tracking using hybrid kinematic integration and positioning algorithm

Mudassir Salim, Abeer Alsadoon, P. W. C. Prasad, Amr Elchouemi, Ashutosh Kumar Singh

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

1 Citation (Scopus)

Abstract

Vehicle tracking today is getting tougher with the passage of time, and it is getting difficult to keep track of these vehicles for security and safety purposes. People are today more concerned to keep them safe using the latest technology. The objective of this research is to provide, and RFID based solution for vehicle tracking to limit vehicle theft and enhance the safety of the passengers. Physical architecture modification and algorithm redesigning is done in order to eliminate the drawbacks of the existing solutions. The approach used for vehicle tracking is to calculate vehicle speed and then estimating the location of the vehicle. Positioning algorithm and Kinematic Integration algorithm are merged to create a hybrid solution which will be used to track the vehicles. As a result of this research work, the vehicle owners would be capable of know the exact location of the vehicle regardless of the network connectivity. This would ensure that the passengers in the vehicle are safe, and the vehicle is recovered in occurrence of a theft. For existing solutions, the outcome was not as accurate as our proposed solution. Furthermore, the processing time taken in proposed solution is less than the processing time taken for existing solutions.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, (COMITCon 2019), 14th-16th February, 2019, India
PublisherIEEE
Pages360-365
Number of pages6
ISBN (Print)9781728102115
DOIs
Publication statusPublished - 2019
EventInternational Conference on Machine Learning_Big Data_Cloud and Parallel Computing -
Duration: 14 Feb 2019 → …

Conference

ConferenceInternational Conference on Machine Learning_Big Data_Cloud and Parallel Computing
Period14/02/19 → …

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