Skip to main content
SpringerLink
Log in

An Efficient Authentication Scheme Using Blockchain Technology for Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The wireless sensor networks are very vulnerable environments for network attacks, so that they require appropriate security measures must be implemented. The blockchain technology is an effective way of protecting data handled and possessed by the wireless sensor networks. Many of the existing approaches are following conventional security approaches. But such traditional approaches are very prone to failures of the nodes more often. In this paper, a novel efficient approach for authentication in wireless sensor networks that uses blockchain technology for security. The nodes of the wireless sensor networks are from IoT network and they are being formulated by the base station, cluster heads and normal sensor nodes. The building of a blockchain network will shape a hierarchical blockchain model, including small chain and global chain, among various network nodes. Nodes in this hybrid model in different communication situations, identity secure connection is realized, normal node identification user authentication is in the blockchain network, local block chain technology and selected cluster node identification verification is done. Analyzation of the protection and results demonstrate that there is robust protection and higher results in the system. The experimental results are showing that the computational capability of over 300 bytes per phase has been achieved using the proposed approach.

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

Similar content being viewed by others

References

  1. Cui, Z., Sun, B., Wang, G., Xue, Y., & Chen, J. (2017). A novel oriented cuckoo search algorithm to improve dv-hop performance for cyber–physical systems. Journal of Parallel and Distributed Computing, 103, 42–52.

    Article  Google Scholar 

  2. Amin, R., Islam, S. K. H., Kumar, N., & Choo, K. K. R. (2018). An untraceable and anonymous password authentication protocol for heterogeneous wireless sensor networks. Journal of Network and Computer Applications, 104, 133–144.

    Article  Google Scholar 

  3. Cui, Z., Cao, Y., Cai, X., Cai, J., & Chen, J. (2019). Optimal leach protocol with modified bat algorithm for big data sensing systems in internet of things. Journal of Parallel and Distributed Computing, 132, 217–229.

    Article  Google Scholar 

  4. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.

    Article  Google Scholar 

  5. Khan, M. A., & Salah, K. (2017). Iot security: Review, blockchain solutions, and open challenges. Future Generation Computer Systems, 83, 395–411.

    Google Scholar 

  6. Wu, F., Li, X., Sangaiah, A. K., Xu, L., Kumari, S., Wu, L., & Shen, J. (2018). A lightweight and robust two-factor authentication scheme for personalized healthcare systems using wireless medical sensor networks. Future Generation Computer Systems, 82, 727–737.

    Article  Google Scholar 

  7. Ferrag, M. A., Maglaras, L. A., Janicke, H., Jiang, J., & Shu, L. (2017). Authentication protocols for internet of things: A comprehensive survey. Security and Communication Networks, 6, 562–953.

    Google Scholar 

  8. Ferrag, M. A., Maglaras, L., & Ahmim, A. (2017). Privacy-preserving schemes for ad hoc social networks: A survey. IEEE Communications Surveys & Tutorials, 19(4), 3015–3045.

    Article  Google Scholar 

  9. Biswas, S., Sharif, K., Li, F., Nour, B., & Wang, Y. (2019). A scalable blockchain framework for secure transactions in iot. IEEE Internet of Things Journal, 6(3), 4650–4659.

    Article  Google Scholar 

  10. Huang, J., Kong, L., Chen, G., Wu, M.-Y., Liu, X., & Zeng, P. (2019). Towards secure industrial IoT: Blockchain system with credit-based consensus mechanism. IEEE Transactions on Industrial Informatics, 15(6), 3680–3689.

    Article  Google Scholar 

  11. Wang, S., Ouyang, L., Yuan, Y., Ni, X., Han, X., Wang, F.-Y. (2019). Blockchain-enabled smart contracts: Architecture, applications, and future trends. IEEE Transactions on Systems, Man, and Cybernetics: Systems.

  12. Liu, M., Yu, F. R., Teng, Y., Leung, V. C. M., & Song, M. (2019). Distributed resource allocation in blockchain-based video streaming systems with mobile edge computing. IEEE Transactions on Wireless Communications, 18(1), 695–708.

    Article  Google Scholar 

  13. Cai, X., Gao, X.-Z., & Xue, Y. (2016). Improved bat algorithm with optimal forage strategy and random disturbance strategy. International Journal of Bio-Inspired computation, 8(4), 205–214.

    Article  Google Scholar 

  14. Deng, X., Jiang, P., Peng, X., & Mi, C. (2019). An intelligent outlier detection method with one class support tucker machine and genetic algorithm toward big sensor data in internet of things. IEEE Transactions on Industrial Electronics, 66(6), 4672–4683.

    Article  Google Scholar 

  15. Cai, X., Wang, H., Cui, Z., Cai, J., Xue, Y., & Wang, L. (2018). Bat algorithm with triangle-flipping strategy for numerical optimization. International Journal of Machine Learning and Cybernetics, 9(2), 199–215.

    Article  Google Scholar 

  16. Li, L., Liu, J., Cheng, L., Qiu, S., Wang, W., Zhang, X., & Zhang, Z. (2018). Creditcoin: A privacy-preserving blockchain-based incentive announcement network for communications of smart vehicles. IEEE Transactions on Intelligent Transportation Systems, 19(7), 2204–2220.

    Article  Google Scholar 

  17. Cui, Z., Xue, F., Cai, X., Cao, Y., Wang, G.-G., & Chen, J. (2018). Detection of malicious code variants based on deep learning. IEEE Transactions on Industrial Informatics, 14(7), 3187–3196.

    Article  Google Scholar 

  18. Cao, Y., Ding, Z., Xue, F., & Rong, X. (2018). An improved twin support vector machine based on multi-objective cuckoo search for software defect prediction. International Journal of Bio-InspiredComputation, 11(4), 282–291.

    Article  Google Scholar 

  19. Zhang, M., Wang, H., Cui, Z., & Chen, J. (2018). Hybrid multi-objective cuckoo search with dynamical local search. Memetic Computing, 10(2), 199–208.

    Article  Google Scholar 

  20. Cui, Z., Xue, F., Zhang, S., Cai, X., Cao, Y., Zhang, W., Chen, J. (2019). A Hybrid BlockChain-based identity authentication scheme for multi-WSN. IEEE Transactions on Service Computing.

  21. Sarpatwar, K. K., Ganapavarapu, V. S., Shanmugam, K., Rahman, A., Vacul, R. (2019). Blockchain enabled AI marketplace: The price you pay for trust. In IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2019, Long Beach,CA, USA, June 16–20, 2019.

  22. Cui, Z., Zhang, J., Wang, Y., Cao, Y., Cai, X., Zhang, W., & Chen, J. (2019). A pigeon-inspired optimization algorithm for many-objective optimization problems. Science China Information Sciences, 62(070), 2121–2123.

    Google Scholar 

  23. Reyna, A., Martin, C., Chen, J., Soler, E., & Diaz, M. (2018). Onblockchain and its integration with iot. challenges and opportunities. Future Generation Computer Systems, 88, 173–190.

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Pan, J., Wang, J., Hester, A., Alqerm, I., Liu, Y., & Zhao, Y. (2019). Edgechain: An edge-iot framework and prototype based on blockchain and smart contracts. IEEE Internet of Things Journal, 6(3), 4719–4732.

    Article  Google Scholar 

  26. Mubarakali, A., Bose, S. C., Srinivasan, K., et al. (2019). Design a secure and efficient health record transaction utilizing block chain (SEHRTB) algorithm for health record transaction in block chain. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01420-0.

    Article  Google Scholar 

Download references

Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the General Research Project under grant number (R.G.P.1/200/41). I would like to thank King Khalid university for the necessary support to lead this paper, we thank our colleagues who sustained greatly assisted this research. We would also like to show our gratitude for sharing their pearls of wisdom with us during this research, and we thank “anonymous” reviewers for their so-called insights.

Author information

Authors and Affiliations

  1. College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia

    Azath Mubarakali

Authors
  1. Azath Mubarakali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Azath Mubarakali.

Ethics declarations

Conflict of interest

The author declares that they no conflict of interest. The author of this research acknowledge that they are not involved in any financial interest.

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

Mubarakali, A. An Efficient Authentication Scheme Using Blockchain Technology for Wireless Sensor Networks. Wireless Pers Commun 127, 255–269 (2022). https://doi.org/10.1007/s11277-021-08212-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08212-w

Keywords

Search

Navigation