TBCS: A Trust Based Clustering Scheme for Secure Communication in Flying Ad-Hoc Networks | Wireless Personal Communications Skip to main content
Log in

TBCS: A Trust Based Clustering Scheme for Secure Communication in Flying Ad-Hoc Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In Flying Ad hoc Networks (FANETs), coordination and cooperation among nodes are important for efficient data transmission. Cooperation among the nodes hinges on the node behavior and the behavior of the node can be quantified using the concept of trust. Trust helps in segregation of non-cooperative and malicious network nodes, thus increasing the reliability of information exchanged among nodes. In this paper, a Trust Based Clustering Scheme (TBCS) has been proposed for FANETs. TBCS use a multi-criteria fuzzy method for the classification based on the node’s behavior in the fuzzy and complex environment. The proposed scheme makes use of Takagi–Sugeno–Kang fuzzy inference method. The reward and punishment mechanism has been introduced to convert the node’s behavior into trust, and to segregate malicious and misbehaving nodes in the FANET. Furthermore, a secure Cluster Head has been selected based on calculated trust values that is responsible for communication with ground control station and inter-cluster communication. TBCS is compared with existing trust models and the experiment results revealed that the proposed TBCS model has high accuracy, better performance, and adaptability in FANETs.

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

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

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

Similar content being viewed by others

References

  1. Müller, M. (2012). Flying ad-hoc networks (p. 53). Ulm: Institute of Media Informatics Ulm University.

    Google Scholar 

  2. Singh, K., & Verma, A.K. (2015). Experimental analysis of AODV, DSDV and OLSR routing protocol for flying adhoc networks (FANETs). In 2015 IEEE international conference on electrical, computer and communication technologies (ICECCT), (pp. 1–4). IEEE.

  3. Bekmezci, I., Sahingoz, O. K., & Temel, Ş. (2013). Flying ad-hoc networks (FANETs): A survey. Ad Hoc Networks, 11(3), 1254–1270.

    Article  Google Scholar 

  4. Rosati, S., Krużelecki, K., Heitz, G., Floreano, D., & Rimoldi, B. (2016). Dynamic routing for flying ad hoc networks. IEEE Transactions on Vehicular Technology, 65(3), 1690–1700.

    Article  Google Scholar 

  5. Singh, K., & Verma, A.K. (2019). Flying adhoc networks concept and challenges. In Advanced methodologies and technologies in network architecture, mobile computing, and data analytics (pp. 903–911). IGI Global.

  6. Frew, E. W., & Brown, T. X. (2008). ‘Networking issues for small unmanned aircraft systems’. Journal of Intelligent Robotic Systems, 54(1), 21–37.

    Google Scholar 

  7. Rebahi, Y., Mujica-V, V.E., & Sisalem, D. (2005). A reputation-based trust mechanism for ad hoc networks. In 10th IEEE symposium on computers and communications (ISCC’05) (pp. 37–42). IEEE.

  8. Singh, K., & Verma, A. K. (2018). A fuzzy-based trust model for flying ad hoc networks (FANETs). International Journal of Communication Systems, 31(6), e3517.

    Article  Google Scholar 

  9. Buttyán, L., & Hubaux, J. P. (2003). Stimulating cooperation in self-organizing mobile ad hoc networks. Mobile Networks and Applications, 8(5), 579–592.

    Article  Google Scholar 

  10. Wang, X., Gao, X.Z., & Ovaska, S.J. (2008). A hybrid optimization method for fuzzy classification systems. In 2008 eighth international conference on hybrid intelligent systems.

  11. Cho, J. H., Swami, A., & Chen, R. (2012). Modeling and analysis of trust management with trust chain optimization in mobile ad hoc networks. Journal of Network and Computer Applications, 35(3), 1001–1012.

    Article  Google Scholar 

  12. Movahedi, Z., Hosseini, Z., Bayan, F., & Pujolle, G. (2016). Trust-distortion resistant trust management frameworks on mobile ad hoc networks: A survey. IEEE Communications Surveys & Tutorials, 18, 1287–1309.

    Article  Google Scholar 

  13. Blaze, M., Feigenbaum, J., & Lacy, J. (1996). Decentralized trust management. In Proceedings 1996 IEEE symposium on security and privacy (pp. 164–173). IEEE.

  14. Devanagavi, G. D., Nalini, N., & Biradar, R. C. (2016). Secured routing in wireless sensor networks using fault-free and trusted nodes. International Journal of Communication Systems, 29(1), 170–193.

    Article  Google Scholar 

  15. Guha, R., Kumar, R., Raghavan, P., & Tomkins, A. (2004). Propagation of trust and distrust. In Proceedings of the 13th international conference on World Wide Web (pp. 403–412). ACM.

  16. Wang, Y., Chandrasekhar, S., Singhal, M., & Ma, J. (2016). A limited-trust capacity model for mitigating threats of internal malicious services in cloud computing. Cluster Computing, 19(2), 647–662.

    Article  Google Scholar 

  17. Chen, D., Chang, G., Sun, D., Li, J., Jia, J., & Wang, X. (2011). TRM-IoT: A trust management model based on fuzzy reputation for internet of things. Computer Science and Information Systems, 8(4), 1207–1228.

    Article  Google Scholar 

  18. Li, J., Li, R., & Kato, J. (2008). Future trust management framework for mobile ad hoc networks. IEEE Communications Magazine, 46(4), 108–114.

    Article  Google Scholar 

  19. Bella, G., Costantino, G., & Riccobene, S. (2008). “Managing reputation over MANETS,” In Proceedings 4th international conference on information assurance and security (IAS), pp. 255–260.

  20. Fang, W., Zhang, C., Shi, Z., Zhao, Q., & Shan, L. (2016). BTRES: Beta-based trust and reputation evaluation system for wireless sensor networks. Journal of Network and Computer Applications, 59, 88–94.

    Article  Google Scholar 

  21. Bao, F., & Chen, I.R. (2012). Dynamic trust management for internet of things applications. In Proceedings of the 2012 international workshop on Self-aware internet of things (pp. 1–6). ACM.

  22. Pouyan, A. A., & Yadollahzadeh Tabari, M. (2017). FPN-SAODV: Using fuzzy petri nets for securing AODV routing protocol in mobile ad hoc network. International Journal of Communication Systems, 30(1), e2935.

    Article  Google Scholar 

  23. Luo, J., Liu, X., & Fan, M. (2009). A trust model based on fuzzy recommendation for mobile ad-hoc networks. Computer Networks, 53(14), 2396–2407.

    Article  Google Scholar 

  24. Ullah, Z., Khan, M.S., Ahmed, I., Javaid, N., & Khan, M.I. (2016). Fuzzy-based trust model for detection of selfish nodes in MANETs. In 2016 IEEE 30th international conference on advanced information networking and applications (AINA) (pp. 965–972). IEEE.

  25. Xia, H., Jia, Z., Ju, L., Li, X., & Zhu, Y. (2011). A subjective trust management model with multiple decision factors for MANET based on AHP and fuzzy logic rules. In 2011 IEEE/ACM international conference on green computing and communications (GreenCom) (pp. 124–130). IEEE.

  26. Chen, R., & Guo, J. (2014). Dynamic hierarchical trust management of mobile groups and its application to misbehaving node detection. In 2014 IEEE 28th international conference on advanced information networking and applications (pp. 49–56). IEEE.

  27. Chatterjee, Pushpita, Ghosh, U., Sengupta, I., & Ghosh, S. K. (2014). A trust enhanced secure clustering framework for wireless ad hoc networks. Wireless Networks, 20(7), 1669–1684.

    Article  Google Scholar 

  28. Singh, K., & Verma, A. K. (2018). FCTM: A novel fuzzy classification trust model for enhancing reliability in flying ad hoc networks (FANETs). Ad Hoc & Sensor Wireless Networks, 40(1–2), 23–47.

    Google Scholar 

  29. Marshall, D. M., Barnhart, R. K., Shappee, E., & Most, M. T. (Eds.). (2015). Introduction to unmanned aircraft systems. Boca Raton: CRC Press.

    Google Scholar 

  30. Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2–3), 191–203.

    Article  Google Scholar 

  31. Wang, Y. (2008). Enhancing node cooperation in mobile wireless ad hoc networks with selish nodes. Retrived from https://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1605&context=gradschool_diss.

  32. Sugeno, M. (1985). Industrial applications of fuzzy control. Amsterdam: Elsevier.

    MATH  Google Scholar 

  33. Rappaport, T. S. (2002). Wireless communications-principles and practice, (the book end). Microwave Journal, 45(12), 128–129.

    Google Scholar 

  34. Sklar, B. (2001). Digital communications (Vol. 2). NJ: Prentice Hall.

    MATH  Google Scholar 

Download references

Acknowledgements

This research work is supported by The MIETY, Government of INDIA under the VISVESVERYA Ph.D. Scheme for Electronics and IT with Grant Ref. No. PHD-MLA/4(33)/2014-15.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kuldeep Singh.

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

Singh, K., Verma, A.K. TBCS: A Trust Based Clustering Scheme for Secure Communication in Flying Ad-Hoc Networks. Wireless Pers Commun 114, 3173–3196 (2020). https://doi.org/10.1007/s11277-020-07523-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07523-8

Keywords

Navigation