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An IOT-Based Automotive and Intelligent Toll Gate Using RFID

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Abstract

The automated toll collection system is a relatively recent piece of technology that has the potential to collect tolls in a manner that is both more efficient and expedient. It is an excellent alternative to the requirement of having to wait for a considerable length of time at manual toll plazas. A toll collection system that is based on RFID technology was built with the help of the Raspberry Pi. This system is fully automated. This was done in order to reduce the amount of time and gasoline that was squandered. The city's registration office issues RFID cards, which are one-of-a-kind identifiers, to each and every vehicle in the city. These cards can be read using radio waves. When a vehicle that has such a unique ID drives up to a toll plaza, the RFID card reader that is attached to the toll plaza will read the card and then send the unique ID of the vehicle to the Raspberry Pi. As a direct consequence of this, the processor carries out its duties and deducts an established sum of money from the prepaid card. If the card ID being used is valid and has sufficient balance, the central processing unit will issue a command to the servo motor, instructing it to begin operating and open the gate. This will make it possible for the car to move through the space. If the card is not genuine or if there is not enough money on the card, it will ask you to move the vehicle to the lane where manual tolls are collected, and you will be required to do so. Additionally, a notification will be sent to the mobile number that was registered.

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Data regarding the research results are available from the corresponding author upon reasonable request.

References

  1. Kumar MA, Suresh Kumar A. A body area network approach for stroke-related disease diagnosis using artificial intelligence with deep learning techniques. In: International conference on advances in computing and data sciences. Cham: Springer; 2022. p. 243–56.

    Chapter  Google Scholar 

  2. Arumugam SK, Amin SM, Kalpana N, Kanagachidambaresan R, Goyal SB, Chaman V, Traian CM, Calin OS. A novel energy efficient threshold based algorithm for wireless body sensor network. Energies. 2022;15(16):6095.

    Article  Google Scholar 

  3. Pai HA, Almuzaini KK, Ali L, Javeed A, Pant B, Pareek PK, Akwafo R. Delay-driven opportunistic routing with multichannel cooperative neighbor discovery for industry 4.0 wireless networks based on power and load awareness. Wirel Commun Mob Comput. 2022. https://doi.org/10.1155/2022/5256133.

    Article  Google Scholar 

  4. Onyema EM, Kumar MA, Balasubaramanian S, Bharany S, Rehman AU, Eldin ET, Shafiq M. A security policy protocol for detection and prevention of internet control message protocol attacks in software defined networks. Sustainability. 2022;14(19):11950.

    Article  Google Scholar 

  5. Chandrappa S, Dharmanna L, Neetha KIR. Automatic elimination of noises and enhancement of medical eye images through image processing techniques for better glaucoma diagnosis. Int Conf Adv Inf Technol (ICAIT). 2019. https://doi.org/10.1109/ICAIT47043.2019.8987312.

    Article  Google Scholar 

  6. Suresh RP, Kavalakkal SS, Shereef S, Sreeragh AS, Sebastian J. IoT based toll gate system using RFID. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). 2019, pp. 1–5. https://doi.org/10.1109/ICOEI.2019.8862619.

  7. Ghosh P, Mahesh TR. A privacy preserving mutual authentication protocol for RFID based automated toll collection system. 2016 International Conference on ICT in Business Industry & Government (ICTBIG). 2016, pp. 1–5. https://doi.org/10.1109/ICTBIG.2016.7892668.

  8. Pašalić D, Cvijić B, Bundalo D, Bundalo Z, Stojanović R. Vehicle toll payment system based on Internet of Things concept. 2016 5th Mediterranean Conference on Embedded Computing (MECO). 2016, pp. 485–488. https://doi.org/10.1109/MECO.2016.7525699.

  9. Shoaib S, Muhammad J, Fahad M. Vehicle number recognition system for automatic toll tax collection. 2012 International Conference on Robotics and Artificial Intelligence, ICRAI. 2012, pp. 125–129. https://doi.org/10.1109/ICRAI.2012.6413377.

  10. Syafei WA, Listyono AF, Darjat. Hardware design of queuing free environmental friendly automatic toll gate using RFID. 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE). 2017, pp. 142–146. https://doi.org/10.1109/ICITACEE.2017.8257692.

  11. Balamurugan K, Elangovan S, Mahalakshmi R, Pavithra R. Automatic check-post and fast track toll system using RFID and GSM module with security system. 2017 International Conference on Advances in Electrical Technology for Green Energy (ICAETGT). 2017, pp. 83–87. https://doi.org/10.1109/ICAETGT.2017.8341461.

  12. Gowrisubadra K, Jeevitha S, Selvarasi N. A survey onrfid based automatic toll gatemanagement. 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN). 2017, pp. 1–6. https://doi.org/10.1109/ICSCN.2017.8085672.

  13. Krishnamurthy J, Mohan N, Hegde R. Automation of Toll Gate and Vehicle Tracking. 2008 International Conference on Computer Science and Information Technology. 2008, pp. 705–708. https://doi.org/10.1109/ICCSIT.2008.148.

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This article is part of the topical collection “Advances in Computational Intelligence for Artificial Intelligence, Machine Learning, Internet of Things and Data Analytics” guest edited by S. Meenakshi Sundaram, Young Lee and Gururaj K S.

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Chandrappa, S., Guruprasad, M.S., Kumar, H.N.N. et al. An IOT-Based Automotive and Intelligent Toll Gate Using RFID. SN COMPUT. SCI. 4, 154 (2023). https://doi.org/10.1007/s42979-022-01569-0

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