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Optimizing Smart Parking System by Using Fog Computing

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Advances in Computing and Data Sciences (ICACDS 2019)

Abstract

Finding the vacant space for parking a vehicle during peak hours is becoming a difficult task at ones end. Parking process whether in shopping malls, restaurants, or offices etc. is a long process and also leads to waste of gasoline. Smart car parking helps in finding the parking slot through Vehicular Ad Hoc Networks (VANET’s). For vehicle communication, some devices such as roadside units and on-board units are present that provides parking slot information. In the proposed work, we have introduced an online reservation facility for parking slot. People can reserve their parking space in advance before reaching to their venues in advance. This will help in reducing the waiting time for the parking allocation to the particular vehicle. This will also help to enhance the parking capabilities and will increase the efficiency when compared to other parking strategies. Our proposed approach can minimize the cost of parking on per person basis, exhaust of vehicle, and indirectly it will impact on save of wastage of gasoline and will keep the environment green.

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References

  1. Aydin, I., Karakose, M., Karakose, E.: A navigation and reservation based smart parking platform using genetic optimization for smart cities. In: Proceedings of 5th International Istanbul Smart Grid and Cities Congress and Fair (ICSG), pp. 120–124. IEEE (2017)

    Google Scholar 

  2. Tang, C., Wei, X., Zhu, C., Chen, W., Rodrigues, J.J.P.C.: Towards smart parking based on fog computing. IEEE Access 6, 70172–70185 (2018)

    Article  Google Scholar 

  3. Lin, T., Rivano, H., Le Mouël, F.: A survey of smart parking solutions. IEEE Trans. Intell. Transp. Syst. 18(12), 3229–3253 (2017). https://doi.org/10.1109/TITS.2017.2685143

    Article  Google Scholar 

  4. Hassoune, K., Dachry, W., Moutaouakkil, F., Medromi, H.: Smart parking systems: a survey. In: Proceedings of 11th International Conference on Intelligent Systems: Theories and Applications (SITA), Mohammedia, pp. 1–6, (2016). https://doi.org/10.1109/SITA.2016.7772297

  5. Idris, M.Y.I., Leng, Y.Y., Tamil, E.M., Noor, N.M.: Car park system: a review of smart parking system and its technology. Inf. Technol. J. 8(2), 101–103 (2009). https://doi.org/10.3923/itj.2009.101.113

    Article  Google Scholar 

  6. Polycarpou, E., Lambrinos, L., Protopapadakis, E.: Smart parking solutions for urban areas. In: Proceedings of 14th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), Madrid, pp. 1–6 (2013). https://doi.org/10.1109/WoWMoM.2013.6583499

  7. Delot, T., llarri, S., Lecomte, S., Ceneratio, N.: Sharing with caution: managing parking spaces in vehicular networks. Mob. Inf. Syst. 9(1), 69–98 (2013). https://doi.org/10.3233/MIS-2012-0149

    Article  Google Scholar 

  8. Mahmud, S.A., Khan, G.M., Rahman, M., Zafar, H.: A survey of intelligent car parking system. J. Appl. Res. Technol. 11(5), 714–726 (2013). https://doi.org/10.1016/S1665-6423(13)71580-3

    Article  Google Scholar 

  9. Balzano, W., Vitale, F.: DiG-Park: a smart parking availability searching method using V2V/V2I and DGP-class problem. In: Proceedings of 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA), Taipei, pp. 698–703 (2017). https://doi.org/10.1109/WAINA.2017.104

  10. Wang, T., et al.: Data collection from WSNs to the cloud based on mobile Fog elements. Future Gener. Comput. Syst. (2017). https://doi.org/10.1016/j.future.2017.07.031

  11. Wang, T., et al.: Fog-based storage technology to fight with cyber threat. Future Gener. Comput. Syst. 83, 208–218 (2018). https://doi.org/10.1016/j.future.2017.12.036

    Article  Google Scholar 

  12. Gupta, P.K., Maharaj, B.T., Malekian, R.: A novel and secure IoT based cloud centric architecture to perform predictive analysis of users activities in sustainable health centres. Multimed. Tools Appl. 76(18), 18489–18512 (2017). https://doi.org/10.1007/s11042-016-4050-6

    Article  Google Scholar 

  13. Malekian, R., Kavishe, A.F., Maharaj, B.T., et al.: Smart vehicle navigation system using hidden Markov model and RFID technology. Wirel. Pers. Commun. 90(4), 1717–1742 (2016). https://doi.org/10.1007/s11277-016-3419-1

    Article  Google Scholar 

  14. Gupta, P.K., Tyagi, V., Singh, S.K.: Predictive Computing and Information Security. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-5107-4

    Book  Google Scholar 

  15. Zhu, C., Li, X., Leung, V.C.M., Yang, L.T., Ngai, E.C., Shu, L.: Towards pricing for sensor-cloud. IEEE Trans. Cloud Comput. (2017). https://doi.org/10.1109/TCC.2017.264952C

  16. Hou, X., Li, Y., Chen, M., Wu, D., Jin, D., Chen, S.: Vehicular fog computing: a viewpoint of vehicles as the infrastructures. IEEE Trans. Veh. Technol. 65(6), 3860–3873 (2016). https://doi.org/10.1109/TVT.2016.2532863

    Article  Google Scholar 

  17. Huang, C., Xu, K.: Reliable realtime streaming in vehicular cloud-fog computing networks. In: International Conference on Communications in China (ICCC), Chengdu, pp. 1–6 (2016). https://doi.org/10.1109/ICCChina.2016.7636838

  18. Kim, O.T.T., Tri, N.D., Nguyen, V.D., Tran, N.H., Hong, C.S.: A shared parking model in vehicular network using fog and cloud environment. In: Proceedings of 17th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 321–326. IEEE, Busan (2015). https://doi.org/10.1109/APNOMS.2015.7275447

  19. Mukherjee, M., Shu, L., Wang, D., Li, K., Chen, Y.: A fog computing-based framework to reduce traffic overhead in large-scale industrial applications. In: Proceedings of Conference on Computer Communications Workshops (INFOCOM WKSHPS), Atlanta, pp. 1008–1009. IEEE (2017). https://doi.org/10.1109/INFCOMW.2017.8116534

  20. Zhang, W., Zhang, Z., Chao, H.: Cooperative fog computing for dealing with big data in the internet of vehicles: architecture and hierarchical resource management. IEEE Commun. Mag. 55(12), 60–67 (2017). https://doi.org/10.1109/MCOM

    Article  Google Scholar 

  21. Park, S., Yoo, Y.: Network intelligence based on network state information for connected vehicles utilizing fog computing. Mob. Inf. Syst. 1–9 (2017). https://doi.org/10.1155/2017/7479267

    Google Scholar 

  22. Rajabioun, T., Ioannou, P.A.: On-street and off-street parking availability prediction using multivariate spatiotemporal models. IEEE Trans. Intell. Transp. Syst. 16(5), 2913–2924 (2015)

    Article  Google Scholar 

  23. Mei, Z., Tian, Y.: Optimized combination model and algorithm of parking guidance information configuration. EURASIP J. Wirel. Commun. Netw. (1), 101 (2011)

    Google Scholar 

  24. Zoeter, O., Dance, C., Clinchant, S., Andreoli, J.: New algorithms for parking demand management and a city-scale deployment. In: Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 1819–1828. ACM (2014)

    Google Scholar 

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Correspondence to P. K. Gupta .

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Tandon, R., Gupta, P.K. (2019). Optimizing Smart Parking System by Using Fog Computing. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1046. Springer, Singapore. https://doi.org/10.1007/978-981-13-9942-8_67

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  • DOI: https://doi.org/10.1007/978-981-13-9942-8_67

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