Skip to main content

Advertisement

Log in

Fuzzy-Based Secure Authentication and Clustering Algorithm for Improving the Energy Efficiency in Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless Sensor Networks are categorized by the improved energy consumption parameters. The nodes are deployed in challenging surroundings and they are not communicated for a long time with lacking of energy level. These kinds of nodes will be the victim of security attacks, the conciliation nodes are guided all the data packets send to the base station of the network. In this paper proposes, The Fuzzy-based Secured Authentication and Clustering (FSAC) Algorithm observes the different kinds of the data packets transmitted within the sensors to avoid the attacks. The FSAC is implemented to use the proficient routing path to diminish the energy consumption. This method finds the adjacent transmitting node to improve the proficient path setup for the data packet routing using the fuzzy logic method. The simulation results specify that the proposed method increases the energy up to 12% compared to the related methods.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Djenouri, D., & Bagaa, M. (2017). Energy-aware constrained relay node deployment for sustainable wireless sensor networks. IEEE Transactions on Sustainable Computing,2(1), 30–42.

    Article  Google Scholar 

  2. Manjeshwar, A., & Agrawal, D. P. (2001). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In Null (p. 30189a). IEEE.

  3. Getu, T. M., Ajib, W., & Yeste-Ojeda, O. A. (2017). Tensor-based efficient multi-interferer RFI excision algorithms for SIMO systems. IEEE Transactions on Communications,65(7), 3037–3052.

    Article  Google Scholar 

  4. Wang, Z., Zhang, L., Zheng, Z., & Wang, J. (2018). Energy balancing RPL protocol with multipath for wireless sensor networks. Peer-to-Peer Networking and Applications,11(5), 1085–1100.

    Article  Google Scholar 

  5. Nguyen, T. G., So-In, C., Nguyen, N. G., & Phoemphon, S. (2017). A novel energy-efficient clustering protocol with area coverage awareness for wireless sensor networks. Peer-to-Peer Networking and Applications,10(3), 519–536.

    Article  Google Scholar 

  6. Kumar, D. (2013). Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks. IET Wireless Sensor Systems,4(1), 9–16.

    Google Scholar 

  7. Zakariayi, S., & Babaie, S. (2019). DEHCIC: A distributed energy-aware hexagon based clustering algorithm to improve coverage in wireless sensor networks. Peer-to-Peer Networking and Applications, 12(4), 689–704.

    Article  Google Scholar 

  8. Gholami, M., & Panahi, A. (2014). Enhancing nodes lifetime optimum protocol for dissemination of information in WSN. International Journal of Computers Communications & Control,9(3), 276–283.

    Article  Google Scholar 

  9. Lee, H., Jang, M., & Chang, J. W. (2014). A new energy-efficient cluster-based routing protocol using a representative path in wireless sensor networks. International Journal of Distributed Sensor Networks,10(7), 527928.

    Article  Google Scholar 

  10. Farooq, M. O., Dogar, A. B., & Shah, G. A. (2010). MR-LEACH: Multi-hop routing with low energy adaptive clustering hierarchy. In 2010 fourth international conference on sensor technologies and applications (SENSORCOMM) (pp. 262–268). IEEE.

  11. Khelifi, M., & Djabelkhir, A. (2012). LMEEC: Layered multi-hop energy efficient cluster-based routing protocol for wireless sensor networks. Preprint arXiv:1201.0725.

  12. Yoon, M., Kim, Y. K., & Chang, J. W. (2013). An energy-efficient routing protocol using message success rate in wireless sensor networks. JoC,4(1), 15–22.

    Google Scholar 

  13. Saini, P., & Sharma, A. K. (2010). Energy efficient scheme for clustering protocol prolonging the lifetime of heterogeneous wireless sensor networks. International Journal of Computer Applications,6(2), 30–36.

    Article  Google Scholar 

  14. Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power-efficient gathering in sensor information systems. In Aerospace conference proceedings, 2002. IEEE (Vol. 3, pp. 3–3). IEEE.

  15. Nikolidakis, S. A., Kandris, D., Vergados, D. D., & Douligeris, C. (2013). Energy efficient routing in wireless sensor networks through balanced clustering. Algorithms,6(1), 29–42.

    Article  MathSciNet  Google Scholar 

  16. Saravanan, T., Saritha, G., & Srinivsan, V. (2014). A analysis of flat routing protocols in sensor N/W. Middle-East Journal of Scientific Research,20(12), 2566–2570.

    Google Scholar 

  17. Tam, N. T., Hai, D. T., Son, L. H., & Vinh, L. T. (2018). Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization. Wireless Networks,1–14, 1477–1490.

    Article  Google Scholar 

  18. Ha, Y. G., Kim, H., & Byun, Y. C. (2012). Energy-efficient fire monitoring over cluster-based wireless sensor networks. International Journal of Distributed Sensor Networks,8(2), 460754.

    Article  Google Scholar 

  19. Yong, Z., & Pei, Q. (2012). A energy-efficient clustering routing algorithm based on distance and residual energy for wireless sensor networks. Procedia Engineering,29, 1882–1888.

    Article  Google Scholar 

  20. Sarkar, A., & Murugan, T. S. (2017). Cluster head selection for energy efficient and delay-less routing in wireless sensor network. Wireless Networks,1–18, 303–320.

    Google Scholar 

  21. Mao, X., Tang, S., & Li, X.-Y. (2011). Energy efficient opportunistic routing in wireless sensor networks. IEEE Transactions and Parallel and Distributed System,15(2), 551–591.

    Google Scholar 

  22. Zhan, G., & Shi, W. (2011). Design and Implementation of TARF: A trust aware routing framework for wireless sensor networks. IEEE Transactions on Dependable and Secure Computing,9(2), 184–197.

    Article  Google Scholar 

  23. Li, Q., & Gao, G. (2012). Mitigating routing misbehavior in disruption tolerant networks. IEEE Transactions on Information Forensics and Security,7(2), 664–675.

    Article  MathSciNet  Google Scholar 

  24. Lee, J.-S., & Cheng, W.-L. (2012). Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication. IEEE Sensors Journal,12(9), 2891–2897.

    Article  Google Scholar 

  25. Ahvar, E., Pourmoslemi, A., & Piran, M. J. (2011). FEAR: A fuzzy-based energy-aware routing protocol for wireless sensor networks. arXiv preprint arXiv:1108.2777.

  26. Selvakumar, K., Sairamesh, L., & Kannan, A. (2017). An intelligent energy aware secured algorithm for routing in wireless sensor networks. Wireless Personal Communications, 96(3), 4781–4798.

    Article  Google Scholar 

  27. AlMomani, I. M., & Saadeh, M. K. (2011). FEAR: Fuzzy-based energy aware routing protocol for wireless sensor network. Int’I of Communications, Network and System Sciences,4, 403–415.

    Article  Google Scholar 

  28. Athmani, S., Bilami, A., & Boubiche, D. E. (2017). EDAK: An efficient dynamic authentication and key management mechanism for heterogeneous WSNs. Future Generation Computer Systems,92, 789–799.

    Article  Google Scholar 

  29. Gaber, T., Abdelwahab, S., Elhoseny, M., & Hassanien, A. E. (2018). Trust-based secure clustering in WSN-based intelligent transportation systems. Computer Networks,146, 151–158.

    Article  Google Scholar 

  30. Zhou, J. (2013). Efficient and secure routing protocol based on encryption and authentication for wireless sensor networks. International Journal of Distributed Sensor Networks,2013, 17.

    Google Scholar 

  31. Zhu, S., Setia, S., & Hahidua, S. (2003). LEAP: Efficient security mechanisms for large-scale distributed sensor networks. In Proceedings of the 10th ACM conference on computer and communications security (pp. 62–72), ACM.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to C. Sureshkumar.

Ethics declarations

Conflict of interest

The authors declare that they do not have any conflict of interest.

Human and Animal Rights

This research does not involve any human or animal participation. All authors have checked and agreed the submission.

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

Sureshkumar, C., Sabena, S. Fuzzy-Based Secure Authentication and Clustering Algorithm for Improving the Energy Efficiency in Wireless Sensor Networks. Wireless Pers Commun 112, 1517–1536 (2020). https://doi.org/10.1007/s11277-020-07113-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07113-8

Keywords

Navigation