Skip to main content

Fuzzy Expert System-Based Node Trust Estimation in Wireless Sensor Networks

  • Conference paper
  • First Online:
Inventive Computation and Information Technologies

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 173))

Abstract

Wireless sensor networks are used in most recent real-time systems globally. In WSN, many researchers feel they are arriving at the destination in all aspects of security and trust. Because trust is the significant factor to implement a secure network by transmitting a packet in a trusted way. Most of the models evaluate the present trust value of the nodes and do not predict the upcoming changes in the trust factor of the nodes. This paper provides a new trust estimation model to evaluate the trust value of the node with a fuzzy expert system that predicts the changes that may be going to occur in the future based on the inference mechanism. This proposed work also concentrates on energy efficiency to optimize the node energy level even though the nodes are undergone trustworthy data transmission. Further, the results obtained from the experiments show that the proposed model outperforms the existing in both parameters as trust and energy efficiency.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Forghani A, Rahmani AM (2008) Multi state fault tolerant topology control algorithm for wireless sensor networks. future generation communication and networking. In: FGCN ‘08. Second ınternational conference, pp 433–436

    Google Scholar 

  2. Munir SA, Wen Bin Y, Biao R, Man M (2007) Fuzzy logic based congestion estimation for QoS in wireless sensor network. In: Wireless communications and networking conference, WCNC.IEEE, pp 4336–4346.

    Google Scholar 

  3. Akkaya K, Younis M (2003) An Energy-Aware QoS Routing Protocol for Wireless Sensor Networks. Distributed Computing Systems Workshops, Proceedings. 23rd International Conference. 710–715

    Google Scholar 

  4. Sun YL, Han Z, Liu KJR (2008) Defense of trust management vulnerabilities in distributed networks. Commun Mag 46(4):112–119

    Article  Google Scholar 

  5. Sathiyavathi V, Reshma R, Parvin SS, SaiRamesh L, Ayyasamy A (2019) Dynamic trust based secure multipath routing for mobile Ad-Hoc networks. In: Intelligent communication technologies and virtual mobile networks. Springer, Cham, pp 618–625

    Google Scholar 

  6. Selvakumar K, Ramesh LS, Kannan A (2016) Fuzzy Based node trust estimation in wireless sensor networks. Asian J Inf Technol 15(5):951–954

    Google Scholar 

  7. Thangaramya K, Logambigai R, SaiRamesh L, Kulothungan K, Ganapathy AKS (2017) An energy efficient clustering approach using spectral graph theory in wireless sensor networks. In: 2017 Second ınternational conference on recent trends and challenges in computational models (ICRTCCM). IEEE, pp 126–129

    Google Scholar 

  8. Poolsappasit N, Madria S (2011) A secure data aggregation based trust management approach for dealing with untrustworthy motes in sensor networks. In: Proceedings of the 40th ınternational conference on parallel processing (ICPP ’11), pp 138–147

    Google Scholar 

  9. Feng RJ, Che SY, Wang X (2012) A credible cluster-head election algorithm based on fuzzy logic in wireless sensor networks. J Comput Inf Syst 8(15):6241–6248

    Google Scholar 

  10. Selvakumar K, Karuppiah M, SaiRamesh L, Islam SH, Hassan MM, Fortino G, Choo KKR (2019) Intelligent temporal classification and fuzzy rough set-based feature selection algorithm for intrusion detection system in WSNs. Inf Sci 497:77–90

    Article  Google Scholar 

  11. Raj JS (2019) QoS optimization of energy efficient routing in IoT wireless sensor networks. J ISMAC 1(01):12–23

    Article  Google Scholar 

  12. Claycomb WR, Shin D (2011) A novel node level security policy framework for wireless sensor networks. J Netw Comput Appl 34(1):418–428

    Article  Google Scholar 

  13. Selvakumar K, Sairamesh L, Kannan A (2017) An intelligent energy aware secured algorithm for routing in wireless sensor networks. Wireless Pers Commun 96(3):4781–4798

    Article  Google Scholar 

  14. Feng R, Xu X, Zhou X, Wan J (2011) A trust evaluation algorithm for wireless sensor networks based on node behaviors and D-S evidence theory. Sensors 11(2):1345–1360

    Article  Google Scholar 

  15. Ganeriwal S, Balzano LK, Srivastava MB (2008) Reputation-based framework for high integrity sensor networks. ACM Trans Sens Netw 4(3):1–37

    Article  Google Scholar 

  16. Kamalanathan S, Lakshmanan SR, Arputharaj K (2017) Fuzzy-clustering-based intelligent and secured energy-aware routing. In: Handbook of research on fuzzy and rough set theory in organizational decision making. IGI Global, pp 24–37

    Google Scholar 

  17. Shaikh RA, Jameel H, d’Auriol BJ, Lee H, Lee S, Song YJ (2009) Group-based trust management scheme for clustered wireless sensor networks. IEEE Trans Parallel Distrib Syst 20(11):1698–1712

    Article  Google Scholar 

  18. Selvakumar K, Sairamesh L, Kannan A (2019) Wise intrusion detection system using fuzzy rough set-based feature extraction and classification algorithms. Int J Oper Res 35(1):87–107

    Article  Google Scholar 

  19. Chang EJ, Hussain FK, Dillon TS (2005) Fuzzy nature of trust and dynamic trust modeling in service-oriented environments. In: Proceedings of workshop on secure web services, pp 75–83

    Google Scholar 

  20. Guo S, Yang O (2007) Energy-aware multicasting in wireless ad hoc networks: a survey and discussion. Comput Commun 30(9):2129–2148

    Article  Google Scholar 

  21. Beth T, Borcherding M, Klein B (1994) Valuation of trust in an open network. In: Proceedings of ESORICS, pp 3–18

    Google Scholar 

  22. Josang A (2001) A logic for uncertain probabilities. Int J Uncertainty Fuzziness Knowl Based Syst 9(3):279–311

    Article  MathSciNet  Google Scholar 

  23. Darney PE, Jacob IJ (2019) Performance enhancements of cognitive radio networks using the improved fuzzy logic. J Soft Comput Paradigm (JSCP) 1(02):57–68

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to L. Sai Ramesh .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Selvakumar, K., Sai Ramesh, L. (2021). Fuzzy Expert System-Based Node Trust Estimation in Wireless Sensor Networks. In: Smys, S., Balas, V.E., Kamel, K.A., Lafata, P. (eds) Inventive Computation and Information Technologies. Lecture Notes in Networks and Systems, vol 173. Springer, Singapore. https://doi.org/10.1007/978-981-33-4305-4_33

Download citation

Publish with us

Policies and ethics