Skip to main content
SpringerLink
Log in

Adaptive Key Management-Based Cryptographic Algorithm for Privacy Preservation in Wireless Mobile Adhoc Networks for IoT Applications

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Mobile ad-hoc networks (MANETs) play an important role in the future of the industrial internet-of-things communication, where smart devices will be connected in a completely distributed manner. In the digital properties owing to the digital data properties, there exist difficulties in directly applying the encryption schemes to the one-dimension data. Thus, it is necessary to develop secure lightweight key frame extraction technique for improving privacy in the e-healthcare. This paper plans to develop the robust and reliable security protocol in MANET IoT application. A chaotic cryptography-based privacy preservation model is proposed in this paper for the purpose of improving the security in the MANET IoT. The key generation process in the chaotic map is optimized by generating optimal key pairs through the newly developed SA-SFO algorithm. The key selected from the chaotic map is influenced by selecting the optimal parameters through the proposed Self Adaptive Sail fish Optimization (SA-SFO). Finally, the experimental analysis is conducted, where for the case of character length as 100; the proposed SA-SFO eventually surpassed the existing ones with the cost function 22% as higher than PSO, 20% higher than GWO, 19% higher than WOA, and 21% higher than SFO respectively. The comparative analysis over the conventional models ensures the efficient performance of the proposed model in terms of diverse analysis in MANET and IoT platform.

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

References

  1. Khan, B. U. I., Anwar, F., Olanrewaju, R. F., Pampori, B. R., & Mir, R. N. (2020). A game theory-based strategic approach to ensure reliable data transmission with optimized network operations in futuristic mobile adhoc networks. IEEE Access, 8, 124097–124109.

    Article  Google Scholar 

  2. Govindan, K., & Mohapatra, P. (2012). Trust computations and trust dynamics in mobile adhoc networks: A survey. IEEE Communications Surveys & Tutorials, 14(2), 279–298.

    Article  Google Scholar 

  3. Ping, Y., Futai, Z., Xinghao, J., & Jianhua, L. (2007). Multi-agent cooperative intrusion response in mobile adhoc networks. Journal of Systems Engineering and Electronics, 18(4), 785–794.

    Article  Google Scholar 

  4. Ojetunde, B., Shibata, N., & Gao, J. (2019). Secure payment system utilizing MANET for disaster areas. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(12), 2651–2663.

    Article  Google Scholar 

  5. 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 

  6. Iftikhar, Z., Javed, Y., Zaidi, S. Y. A., Shah, M. A., Khan, I., Mussadiq, Z. S., & Abbasi, K. (2021). Privacy preservation in resource-constrained IoT devices using blockchain—A survey. Electronics, 10, 14.

    Article  Google Scholar 

  7. Shende, K., & Sonavane, S. S. (2020). CrowWhale-ETR: CrowWhale optimization algorithm for energy and trust aware multicast routing in WSN for IoT applications. Wireless Networks, 66, 1–19s.

    Google Scholar 

  8. Alharthi, A., Ni, Q., & Jiang, R. (2021). A privacy-preservation framework based on biometrics blockchain (BBC) to prevent attacks in VANET. IEEE Access, 6, 66.

    Google Scholar 

  9. Al-Turjman, F., Zahmatkesh, H., & Shahroze, R. (2019). An overview of security and privacy in smart cities’ IoT communications. Transactions on Emerging Telecommunications Technologies, 66, e3677.

    Google Scholar 

  10. Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805.

    Article  Google Scholar 

  11. Guan, Z., Li, J., Wu, L., Zhang, Y., Wu, J., & Du, X. (2017). Achieving efficient and secure data acquisition for cloud-supported Internet of Things in smart grid. IEEE Internet of Things Journal, 4(6), 1934–1944.

    Article  Google Scholar 

  12. Wang, C., Shen, J., Liu, Q., Ren, Y., & Li, T. (2018). A novel security scheme based on instant encrypted transmission for Internet of Things. Security and Communication Networks, 6, 66.

    Google Scholar 

  13. Appavoo, P., Chan, M., Bhojan, A., et al. (2016). Efficient and privacy-preserving access to sensor data for Internet of Things (IoT) based services. IEEE International Conference on Communication Systems and Networks, 66, 1–8.

    Google Scholar 

  14. Henze, M., Hermerschmidt, L., Kerpen, D., et al. (2016). A comprehensive approach to privacy in the cloud-based Internet of Things. Future Generation Computer Systems, 56, 701–718.

    Article  Google Scholar 

  15. González-Manzano, L., de Fuentes, J., Pastrana, S., et al. (2016). PAgIoT-Privacy-preserving aggregation protocol for Internet of Things. Journal of Network and Computer Applications, 71, 59–71.

    Article  Google Scholar 

  16. Lai, C., Li, H., Liang, X., et al. (2014). CPAL: A conditional privacy-preserving authentication with access linkability for roaming service. IEEE Internet of Things Journal, 1(1), 46–57.

    Article  Google Scholar 

  17. Premnath, S., & Haas, Z. (2015). Security and privacy in the internet-of-things under time-and-budget-limited adversary mode. IEEE Wireless Communications Letters, 4(3), 277–280.

    Article  Google Scholar 

  18. Maram, B., Gnanasekar, J. M., Manogaran, G., & Balaanand, M. (2018). Intelligent security algorithm for UNICODE data privacy and security in IOT. Service Oriented Computing and Applications, 6, 66.

    Google Scholar 

  19. George, G., & Thampi, S. M. (2018). A graph-based security framework for securing industrial IoT networks from vulnerability exploitations. IEEE Access, 6, 43586–43601.

    Article  Google Scholar 

  20. Aman, M. N., Basheer, M. H., & Sikdar, B. (2019). Data provenance for IoT with light weight authentication and privacy preservation. IEEE Internet of Things Journal, 6(6), 10441–10457.

    Article  Google Scholar 

  21. Kounoudes, A. D., & Kapitsaki, G. M. (2020). A mapping of IoT user-centric privacy preserving approaches to the GDPR. Internet of Things, 11, 66.

    Article  Google Scholar 

  22. Guan, Z., Zhang, Y., Wu, L., Wu, J., Li, J., Ma, Y., & Hu, J. (2019). APPA: An anonymous and privacy preserving data aggregation scheme for fogenhanced IoT. Journal of Network and Computer Applications, 125, 82–92.

    Article  Google Scholar 

  23. Sun, G., Chang, V., Ramachandran, M., Sun, Z., Li, G., Yu, H., & Liao, D. (2017). Efficient location privacy algorithm for Internet of Things (IoT) services and applications. Journal of Network and Computer Applications, 89, 3–13.

    Article  Google Scholar 

  24. Hamza, R., Yan, Z., Muhammad, K., Bellavista, P., & Titouna, F. (2020). A privacy-preserving cryptosystem for IoT E-healthcare. Information Sciences, 527, 493–510.

    Article  MathSciNet  Google Scholar 

  25. Deebak, B. D., Al-Turjman, F., Aloqaily, M., & Alfandi, O. (2019). An authentic-based privacy preservation protocol for smart e-Healthcare systems in IoT. IEEE Access, 7, 135632–135649.

    Article  Google Scholar 

  26. Yu, M., Zhou, M., & Su, W. (2009). A secure routing protocol against byzantine attacks for MANETs in adversarial environments. IEEE Transactions on Vehicular Technology, 58(1), 449–460.

    Article  Google Scholar 

  27. Defrawy, K. E., & Tsudik, G. (2011). Privacy-preserving location-based on-demand routing in MANETs. IEEE Journal on Selected Areas in Communications, 29(10), 1926–1934.

    Article  Google Scholar 

  28. Hurley-Smith, D., Wetherall, J., & Adekunle, A. (2017). SUPERMAN: Security using pre-existing routing for mobile ad hoc networks. IEEE Transactions on Mobile Computing, 16(10), 2927–2940.

    Article  Google Scholar 

  29. Dhurandher, S. K., Obaidat, M. S., Verma, K., Gupta, P., & Dhurandher, P. (2011). FACES: Friend-based ad hoc routing using challenges to establish security in MANETs systems. IEEE Systems Journal, 5(2), 176–188.

    Article  Google Scholar 

  30. Li, T., Chen, W., Tang, Y., & Yan, H. (2018). A homomorphic network coding signature scheme for multiple sources and its application in IoT. Security and Communication Networks, 6, 66.

    Google Scholar 

  31. Challa, S., Wazid, M., Das, A. K., Kumar, N., Reddy, A. G., Yoon, E. J., & Yoo, K. Y. (2017). Secure signature-based authenticated key establishment scheme for future IoT applications. IEEE Access, 5, 3028–3043.

    Article  Google Scholar 

  32. Gomes, G. F., da Cunha, S. S., Jr., & Ancelotti, A. C., Jr. (2019). A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates. Engineering with Computers, 35, 619–626.

  33. Shadravan, S., Naji, H. R., & Bardsiri, V. K. (2019). The sailfish optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence, 80, 20–34.

    Article  Google Scholar 

  34. Pedersen, M. E. H., & Chipperfield, A. J. (2010). Simplifying particle swarm optimization. Applied Soft Computing, 10(2), 618–628.

    Article  Google Scholar 

  35. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.

    Article  Google Scholar 

  36. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, India

    Satyanarayana Pamarthi & R. Narmadha

Authors
  1. Satyanarayana Pamarthi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Narmadha
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Satyanarayana Pamarthi and Narmadha Ramakrishnan designed the model and computational framework. Both carried out the implementation. Satyanarayana Pamarthi performed the calculations and wrote the manuscript with all the inputs. Satyanarayana Pamarthi and Narmadha Ramakrishnan discussed the results and contributed to the final manuscript.

Corresponding author

Correspondence to Satyanarayana Pamarthi.

Ethics declarations

Conflict of interest

Not applicable.

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

Pamarthi, S., Narmadha, R. Adaptive Key Management-Based Cryptographic Algorithm for Privacy Preservation in Wireless Mobile Adhoc Networks for IoT Applications. Wireless Pers Commun 124, 349–376 (2022). https://doi.org/10.1007/s11277-021-09360-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-09360-9

Keywords

Search

Navigation