Skip to main content
Log in

BDN-GWMNN: Internet of Things (IoT) Enabled Secure Smart City Applications

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Nowadays, next-generation networks such as the Internet of Things (IoT) and 6G are played a vital role in providing an intelligent environment. The development of technologies helps to create smart city applications like the healthcare system, smart industry, and smart water plan, etc. Any user accesses the developed applications; at the time, security, privacy, and confidentiality arechallenging to manage. So, this paper introduces the blockchain-defined networks with a grey wolf optimized modular neural network approach for managing the smart environment security. During this process, construction, translation, and application layers are created, in which user authenticated based blocks are designed to handle the security and privacy property. Then the optimized neural network is applied to maintain the latency and computational resource utilization in IoT enabled smart applications. Then the efficiency of the system is evaluated using simulation results, in which system ensures low latency, high security (99.12%) compared to the multi-layer perceptron, and deep learning networks.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Xing, L. (2020). Reliability in Internet of Things: Current Status and Future Perspectives. IEEE Internet of Things Journal. DOI:https://doi.org/10.1109/JIOT.2020.2993216.

    Article  Google Scholar 

  2. Gomathi, P., Baskar, S., & Shakeel, P. M. (2020) Concurrent service access and management framework for usercentric future internet of things in smart cities. Complex Intell. Syst. (2020). https://doi.org/10.1007/s40747-020-00160-5.

  3. Tsihrintzis, G. A., & Virvou, M. (2020). Advances in Core Computer Science-Based Technologies. In Advances in Core Computer Science-Based Technologies (pp. 1–6). Cham: Springer.

    Google Scholar 

  4. Ali, M. S., Vecchio, M., Pincheira, M., Dolui, K., Antonelli, F., & Rehmani, M. H. (2019). Applications of blockchains in the Internet of Things: A comprehensive survey. IEEE Communications Surveys & Tutorials, 21(2), 1676–1717.

    Article  Google Scholar 

  5. Sheron, P. F., Sridhar, K. P., Baskar, S., & Shakeel, P. M. (2019). A decentralized scalable security framework for end-to‐end authentication of future IoT communication. Transactions on Emerging Telecommunications Technologies, e3815.https://doi.org/10.1002/ett.3815.

  6. Ge, M., Hong, J. B., Walter Guttmann, and Dong Seong Kim. (2017) “A framework for automating security analysis of the internet of things.“ Journal of Network and Computer Applications 83 (2017): 12–27.

  7. Preeth, S. S. L., & Dhanalakshmi, R. &Shakeel, P. M. (2019). An intelligent approach for energy efficient trajectory design for mobile sink based IoT supported wireless sensor networks. Peer-to-PeerNetworking and Applications, 1–12. https://doi.org/10.1007/s12083-019-00798-0

  8. Biswas, K., & Muthukkumarasamy, V. (2016, December). Securing smart cities using blockchain technology. In 2016 IEEE 18th international conference on high performance computing and communications; IEEE 14th international conference on smart city; IEEE 2nd international conference on data science and systems (HPCC/SmartCity/DSS) (pp. 1392–1393). IEEE.

  9. Singh, S. K., Jeong, Y. S., & Park, J. H. (2020). A Deep Learning-based IoT-oriented Infrastructure for Secure Smart City. Sustainable Cities and Society,Volume, 60, 102252.

    Article  Google Scholar 

  10. Rahman, M. A., Asyharia, A. T., Leong, L. S., Satrya, G. B., Tao, M. H., & Zolkipli, M. F. (2020). Scalable Machine Learning-Based Intrusion Detection System for IoT-Enabled Smart Cities. Sustainable Cities and Society, 61, 102324.

    Article  Google Scholar 

  11. Rahman, M. A., Asyhari, A. T., Kurniawan, I. F., Ali, M. J., Rahman, M. M., & Karim, M. (2020). A scalable hybrid MAC strategy for traffic-differentiated IoT-enabled intra-vehicular networks. Computer Communications.157, PP. 320–328.

  12. Aikins, S. K. (2019). Managing Cybersecurity Risks of SCADA Networks of Critical Infrastructures in the IoT Environment. In Security, Privacy and Trust in the IoT Environment (pp. 3–23). Cham: Springer.

    Chapter  Google Scholar 

  13. Ziegler, S., Menon, M., & Annichino, P. (2019). IoT Privacy and Security in Smart Cities. In Internet of Things Security and Data Protection (pp. 149–171). Cham: Springer.

    Chapter  Google Scholar 

  14. Butt, T. A., & Afzaal, M. (2019). Security and privacy in smart cities: issues and current solutions. In Smart Technologies and Innovation for a Sustainable Future (pp. 317–323). Cham: Springer.

    Chapter  Google Scholar 

  15. Popescul, D., & &Genete, L. D. (2016). Data security in smart cities: challenges and solutions. InformaticaEconomică, 20(1), .PP29–38.

    Google Scholar 

  16. Yaqoob, I., Hashem, I. A. T., Mehmood, Y., Gani, A., Mokhtar, S., & Guizani, S. (2017). Enabling communication technologies for smart cities. IEEE Communications Magazine, 55(1), 112–120.

    Article  Google Scholar 

  17. Le-Dang, Q., & Le-Ngoc, T. (2018). Internet of Things (IoT) Infrastructures for Smart Cities. In Handbook of Smart Cities (pp. 1–30). Cham: Springer.

    Google Scholar 

  18. Raj, S. N., & Sherly, E. (2020). “Blockchain-Based Shared Security Architecture. In “In Cognitive Informatics and Soft Computing (pp. 29–35). Singapore: Springer.

    Chapter  Google Scholar 

  19. Wang, Z. H., & Wang, J. (2019, October). An IOT Data Collection Mechanism Based on Cloud-Edge Coordinated Deep Learning. In China Conference on Wireless Sensor Networks (pp. 76–89). Springer, Singapore.

  20. de Morais, C. M., Sadok, D., & Kelner, J. (2019). An IoT sensor and scenario survey for data researchers. Journal of the Brazilian Computer Society, 25(4), 1–17.

    Google Scholar 

  21. Parizi, R. M., Dehghantanha, A., Azmoodeh, A., & Choo, K. K. R. (2020). Blockchain in Cybersecurity Realm: An Overview. In Blockchain Cybersecurity, Trust and Privacy,79 (pp. 1–5). Cham: Springer.

    Google Scholar 

  22. Chen, K. (2015). Deep and modular neural networks. In Springer Handbook of Computational Intelligence (pp. 473–494). Berlin: Springer.

    Chapter  Google Scholar 

  23. Tanaka, G., Yamane, T., Nakano, D., Nakane, R., & Katayama, Y. (2015, July). Regularity and randomness in modular network structures for neural associative memories. In 2015 International Joint Conference on Neural Networks (IJCNN) (pp. 1–7). IEEE.

  24. Emary, E., Zawbaa, H. M., & Grosan, C. (2017). Experienced gray wolf optimization through reinforcement learning and neural networks. IEEE transactions on neural networks and learning systems, 29(3), 681–694.

    Article  MathSciNet  Google Scholar 

  25. https://thingsboard.io/smart-energy/.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vidhyacharan Bhaskar.

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

Peneti, S., Sunil Kumar, M., Kallam, S. et al. BDN-GWMNN: Internet of Things (IoT) Enabled Secure Smart City Applications. Wireless Pers Commun 119, 2469–2485 (2021). https://doi.org/10.1007/s11277-021-08339-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08339-w

Keywords

Navigation