DOI QR코드

DOI QR Code

Comparative Analysis of IoT Enabled Multi Scanning Parking Model for Prediction of Available Parking Space with Existing Models

  • Anchal, Anchal (Maharshi Dayanand University, Department of Computer Science) ;
  • Mittal, Pooja (Maharshi Dayanand University, Department of Computer Science)
  • Received : 2022.08.05
  • Published : 2022.08.30

Abstract

The development in the field of the internet of things (IoT) have improved the quality of the life and also strengthened different areas in the society. All cities across the world are seeking to become smarter. The creation of a smart parking system is the essential use case in smart cities. In recent couple of years, the number of vehicles has increased significantly. As a result, it is critical to make the use of technology that enables hassle-free parking in both public and private spaces. In conventional parking systems, drivers are not able to find free parking space. Conventional systems requires more human interference in a parking lots. To manage these circumstances there is an intense need of IoT enabled parking solution that includes the well defined architecture that will contain the following components such as smart sensors, communication agreement and software solution. For implementing such a smart parking system in this paper we proposed a design of smart parking system and also compare it with convetional system. The proposed design utilizes sensors based on IoT and Data Mining techniques to handle real time management of the parking system. IoT enabled smart parking solution minimizes the human interference and also saves energy, money and time.

Keywords

References

  1. Takizawa, H., Yamada, K., & Ito, T. (2004, June). Vehicles detection using sensor fusion. In IEEE Intelligent Vehicles Symposium, 2004 (pp. 238-243). IEEE.
  2. Bong, D. B. L., Ting, K. C., & Lai, K. C. (2008). Integrated Approach in the Design of Car Park Occupancy Information System (COINS). IAENG International Journal of Computer Science, 35(1).
  3. Geng, Y., & Cassandras, C. G. (2013). New "smart parking" system based on resource allocation and reservations. IEEE Transactions on intelligent transportation systems, 14(3), 1129-1139. https://doi.org/10.1109/TITS.2013.2252428
  4. Pham, T. N., Tsai, M. F., Nguyen, D. B., Dow, C. R., & Deng, D. J. (2015). A cloud-based smart-parking system based on Internet-of-Things technologies. IEEE Access, 3, 1581-1591. https://doi.org/10.1109/ACCESS.2015.2477299
  5. Sarangi, M., Mohapatra, S., Tirunagiri, S. V., Das, S. K., & Babu, K. S. (2020). IoT aware automatic smart parking system for smart city. In Cognitive Informatics and Soft Computing (pp. 469-481). Springer, Singapore.
  6. Kumar, I., Manuja, P., Soni, Y., & Yadav, N. S. (2020). An Integrated Approach towards Smart Parking Implementation for Smart Cities in India. In Advances in Data and Information Sciences (pp. 343-349). Springer, Singapore.
  7. Umbaugh, S. E. (1997). Computer vision and image processing: a practical approach using cviptools with cdrom. Prentice Hall PTR.
  8. Yang, F., & Ma, Z. (2005, October). Vehicle license plate location based on histogramming and mathematical morphology. In Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05) (pp. 89-94). IEEE.
  9. Mehena, J. Vehicle License Plate Segmentation and Extraction Based on Mathematical Morphology.
  10. Lumonics, G. S. I. (1999). QuantArray analysis software. Operator's Manual.
  11. Goyal, A., & Verma, B. (2007, April). A neural network based approach for the vehicle classification. In 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing (pp. 226-231). IEEE.
  12. Huang, K.; Chen, K.; Huang, M.; Shen, L. Multilayer perceptron with particle swarm optimization for well log data inversion. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22-27 July 2012; pp. 6103-6106. .
  13. Anchal D., Pooja M. (2021) Evaluation of Data Mining Techniques and Its Fusion with IoT Enabled Smart Technologies foe Effective Prediction of Available Parking Space (pp. 187-197) , IJECES.
  14. Goyal, A., & Verma, B. (2007, April). A neural network based approach for the vehicle classification. In 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing (pp. 226-231). IEEE
  15. Lipton, Z.C.; Elkan, C.; Naryanaswamy, B. Optimal thresholding of classifiers to maximize F1 measure. In Proceedings of the Joint European Conf. on Machine Learning and Knowledge Discovery in Databases, Nancy, France, 15-19 September 2014; pp. 225-239.