Skip to main content

Advertisement

SpringerLink
Log in

An Investigation of Garbage Disposal Electric Vehicles (GDEVs) Integrated with Deep Neural Networking (DNN) and Intelligent Transportation System (ITS) in Smart City Management System (SCMS)

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

A smart city is a urban developed city that delivers the solution to the residents smarter especially using Information and Communication Technology. The conventional smart city management modules use sensors or IoT devices in conjunction with Intelligent Traffic System (ITS), however, these frameworks fail in managing the Electric Vehicles (EVs) routing with security or scheduling the EV for charging and smart energy distribution for the EVs. In this paper, we present a novel Smart City Management System (SCMS) with the integration of three immersive technologies are adopted to improve the management of garbage disposal EVs (GDEVs). Initially, the study uses IoT devices to collect the status of garbage bins. Secondly, the ITS is integrated with Deep Neural Networks (DNNs) to manage the GDEVs for effective traffic management and speed monitoring based on the collected information like garbage payloads, climatic conditions and distance between the collection, disposal of waste etc. Finally, the entire transmitted data between IoTs and EVs are secured effectively using blockchain technology, which protects them against cybersecurity attacks. The experimental validation on proposed SCMS with DNN-ITS and secured blockchain model offers improved energy efficiency, faster transmission and enhanced security capabilities than existing methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Availability of Data and Material (data transparency)

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Code Availability (software application or custom code)

The code that is used this study are available from the corresponding author, upon reasonable request.

References

  1. Sharma, M., Joshi, S., Kannan, D., Govindan, K., Singh, R., & Purohit, H. C. (2020). Internet of Things (IoT) adoption barriers of smart cities’ waste management: An Indian context. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2020.122047

    Article  Google Scholar 

  2. Arasteh, H., Hosseinnezhad, V., Loia, V., Tommasetti, A., Troisi, O., Shafie-khah, M., & Siano, P. (2016, June). Iot-based smart cities: a survey. In 2016 IEEE 16th international conference on environment and electrical engineering (EEEIC) (pp. 1–6). IEEE.

  3. Marques, P., Manfroi, D., Deitos, E., Cegoni, J., Castilhos, R., Rochol, J., Pignaton, E., & Kunst, R. (2019). An IoT-based smart cities infrastructure architecture applied to a waste management scenario. Ad Hoc Networks, 87, 200–208.

    Article  Google Scholar 

  4. Anagnostopoulos, T., Zaslavsky, A., Kolomvatsos, K., Medvedev, A., Amirian, P., Morley, J., & Hadjieftymiades, S. (2017). Challenges and opportunities of waste management in IoT-enabled smart cities: A survey. IEEE Transactions on Sustainable Computing, 2(3), 275–289.

    Article  Google Scholar 

  5. Kim, T. H., Ramos, C., & Mohammed, S. (2017). Smart city and IoT. Future Generation Computer Systems, 76, 159–162.

    Article  Google Scholar 

  6. Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for smart cities. IEEE Internet of Things journal, 1(1), 22–32.

    Article  Google Scholar 

  7. Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer, O., Schuh, G., Sihn, W., & Ueda, K. (2016). Cyber-physical systems in manufacturing. Cirp Annals, 65(2), 621–641.

    Article  Google Scholar 

  8. Misra, G., Kumar, V., Agarwal, A., & Agarwal, K. (2016). Internet of things (IoT)–a technological analysis and survey on vision, concepts, challenges, innovation directions, technologies, and applications (an upcoming or future generation computer communication system technology). American Journal of Electrical and Electronic Engineering, 4(1), 23–32.

    Google Scholar 

  9. Atlam, H. F., Alenezi, A., Alassafi, M. O., & Wills, G. (2018). Blockchain with Internet of Things: Benefits, challenges, and future directions. International Journal of Intelligent Systems and Applications, 10(6), 40–48.

    Article  Google Scholar 

  10. Reyna, A., Martín, C., Chen, J., Soler, E., & Díaz, M. (2018). On blockchain and its integration with IoT. Challenges and opportunities. Future generation computer systems, 88, 173–190.

    Article  Google Scholar 

  11. Trappey, A. J., Trappey, C. V., Govindarajan, U. H., Chuang, A. C., & Sun, J. J. (2017). A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0. Advanced Engineering Informatics, 33, 208–229.

    Article  Google Scholar 

  12. Khan, M. A., & Salah, K. (2018). IoT security: Review, blockchain solutions, and open challenges. Future Generation Computer Systems, 82, 395–411.

    Article  Google Scholar 

  13. Bharadwaj, A. S., Rego, R., & Chowdhury, A. (2016). IoT based solid waste management system: A conceptual approach with an architectural solution as a smart city application. In 2016 IEEE Annual India conference (INDICON) (pp. 1–6). IEEE.

  14. Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020). Applications of Artificial Intelligence and Machine learning in smart cities. Computer Communications., 154, 313–323.

    Article  Google Scholar 

  15. Al-Garadi, M. A., Mohamed, A., Al-Ali, A., Du, X., Ali, I., & Guizani, M. (2020). A survey of machine and deep learning methods for internet of Things (IoT) security. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/COMST.2020.2988293

    Article  Google Scholar 

  16. Makhdoom, I., Zhou, I., Abolhasan, M., Lipman, J., & Ni, W. (2020). PrivySharing: A blockchain-based framework for privacy-preserving and secure data sharing in smart cities. Computers & Security, 88, 1–24.

    Article  Google Scholar 

  17. Meza, J. K. S., Yepes, D. O., Rodrigo-Ilarri, J., & Cassiraga, E. (2019). Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks. Heliyon, 5(11), e02810.

    Article  Google Scholar 

  18. Abbasi, M., & El Hanandeh, A. (2016). Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste management, 56, 13–22.

    Article  Google Scholar 

  19. Foresti, R., Rossi, S., Magnani, M., Bianco, C. G. L., & Delmonte, N. (2020). Smart society and artificial intelligence: Big data scheduling and the global standard method applied to smart maintenance. Engineering. In Press Corrected Proof. https://doi.org/10.1016/j.eng.2019.11.014

  20. Nowakowski, P., Szwarc, K., & Boryczka, U. (2020). Combining an artificial intelligence algorithm and a novel vehicle for sustainable e-waste collection. Science of The Total Environment, 730, 138726.

    Article  Google Scholar 

  21. Garre, A., Ruiz, M. C., & Hontoria, E. (2020). Application of Machine Learning to support production planning of a food industry in the context of waste generation under uncertainty. Operations Research Perspectives, 7, 100147.

    Article  Google Scholar 

  22. Kontokosta, C. E., Hong, B., Johnson, N. E., & Starobin, D. (2018). Using machine learning and small area estimation to predict building-level municipal solid waste generation in cities. Computers, Environment and Urban Systems, 70, 151–162.

    Article  Google Scholar 

  23. Kannangara, M., Dua, R., Ahmadi, L., & Bensebaa, F. (2018). Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches. Waste Management, 74, 3–15.

    Article  Google Scholar 

  24. Ahmad, S., Jamil, F., Iqbal, N., & Kim, D. (2020). Optimal route recommendation for waste carrier vehicles for efficient waste collection: A step forward towards sustainable cities. IEEE Access, 8, 77875–77887.

    Article  Google Scholar 

  25. Geetha, S., Narayanamoorthy, S., Kang, D., & Kureethara, J. V. (2019). A novel assessment of healthcare waste disposal methods: Intuitionistic hesitant Fuzzy MULTIMOORA decision making approach. IEEE Access, 7, 130283–130299.

    Article  Google Scholar 

  26. Sodanil, M., & Chatthong, P. (2014). Artificial neural network-based time series analysis forecasting for the amount of solid waste in Bangkok. In Ninth International conference on digital information management (ICDIM 2014) (pp. 16–20). IEEE.

  27. Kumar, J. N. A., & Chimmani, S. (2019). Proposal of smart home resource management for waste reduction and sustainability using AI and ML. In 2019 International conference on communication and electronics systems (ICCES) (pp. 992–998). IEEE.

  28. Nižetić, S., Djilali, N., Papadopoulos, A., & Rodrigues, J. J. (2019). Smart technologies for promotion of energy efficiency, utilization of sustainable resources and waste management. Journal of cleaner production., 231, 565–591.

    Article  Google Scholar 

  29. Dreiseitl, S., & Ohno-Machado, L. (2002). Logistic regression and artificial neural network classification models: A methodology review. Journal of biomedical informatics, 35(5–6), 352–359.

    Article  Google Scholar 

  30. Baral, C., Fuentes, O., & Kreinovich, V. (2018). Why deep neural networks: a possible theoretical explanation. In Constraint programming and decision making: Theory and applications (pp. 1–5). Springer, Cham.

  31. Cogswell, M., Ahmed, F., Girshick, R., Zitnick, L., & Batra, D. (2015). Reducing overfitting in deep networks by decorrelating representations. arXiv preprint áarXiv:1511.06068.

  32. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).

  33. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85–117.

    Article  Google Scholar 

  34. Monzambe, G. M., Mpofu, K., & Daniyan, I. A. (2019). Statistical analysis of determinant factors and framework development for the optimal and sustainable design of municipal solid waste management systems in the context of industry 4.0. Procedia CIRP, 84, 245–250.

    Article  Google Scholar 

Download references

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author information

Authors and Affiliations

  1. Research and Development, ICT Academy, Chennai, India

    N. Yuvaraj

  2. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, AP, India

    K. Praghash

  3. Research and Development, ICT Academy, Chennai, India

    R. Arshath Raja

  4. Department of Electronics and Telecommunications Engineering, University of Technology and Applied Sciences (Higher College of Technology), Muscat, Oman

    T. Karthikeyan

Authors
  1. N. Yuvaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Praghash
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Arshath Raja
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. T. Karthikeyan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: NY; Methodology: KP; Formal analysis and investigation: RAR, TK; Writing—original draft preparation: KP, TK; Writing—review and editing: KP, RAR, TK.

Corresponding author

Correspondence to N. Yuvaraj.

Ethics declarations

Conflict of interest

The author(s) have no conflict of interest to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yuvaraj, N., Praghash, K., Raja, R.A. et al. An Investigation of Garbage Disposal Electric Vehicles (GDEVs) Integrated with Deep Neural Networking (DNN) and Intelligent Transportation System (ITS) in Smart City Management System (SCMS). Wireless Pers Commun 123, 1733–1752 (2022). https://doi.org/10.1007/s11277-021-09210-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-09210-8

Keywords

Search

Navigation