Advertisement

Num Ant Factor Based Comprehensive Investigations over Linguistic Trust and Reputations Model in Mobile Sensor Networks

  • Vinod Kumar VermaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11005)

Abstract

Trust is the prime concern for the evaluations of mobile sensor network-based applications. Trust in terms of human intractable levels is being expected form nowadays mobile sensor networks. In this paper, a linguistic trust and reputation model has been investigated in an exhaustive manner. The performance parameters like accuracy, path length, and energy consumption have been evaluated. Moreover, satisfaction factor has been investigated with the inference power of the fuzzy sets. Num ant factor has been considered as the major factor for this investigational analysis. The effects of num ant factor on the operations of the mobile sensor networks system have been observed. Simulations have been performed to validate the results.

Keywords

Trust Reputation LFTM Num ant Accuracy Path length Energy Satisfaction 

References

  1. 1.
    Tang, W., Chen, Z.: Research of subjective trust management model based on the fuzzy set theory. Chin. J. Softw. 14(8), 1401–1408 (2003)zbMATHGoogle Scholar
  2. 2.
    Ramchurn, S.D., Sierra, C., Godo, L., Jennings, N.R.: A computational trust model for multi-agent interactions based on confidence and reputation. In: Proceedings of 6th International Workshop of Deception, Fraud and Trust in Agent Societies, pp. 69–75 (2003)Google Scholar
  3. 3.
    Shuqin, Z., Dongxin, L., Yongtian, Y.: A fuzzy set based trust and reputation model in P2P networks. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 211–217. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-28651-6_31CrossRefGoogle Scholar
  4. 4.
    Gómez Mármol, F., Marín-Blázquez, J.G., Martínez Pérez, G.: A linguistic fuzzy logic enhancement for distributed networks. In: Third IEEE International Symposium on Trust, Security and Privacy for Emerging Applications, TSP 2010, Bradford, UK, pp. 838–845 (2010). ISBN 978-1-4244-7547-6Google Scholar
  5. 5.
    Gómez Mármol, F., Marín-Blázquez, J.G., Martínez Pérez, G.: LFTM, linguistic fuzzy trust mechanism for distributed networks. Concurr. Comput.: Pract. Exp. 24(17), 2007–2027 (2012)CrossRefGoogle Scholar
  6. 6.
    Gómez Mármol, F., Martínez Pérez, G.: Trust and reputation models comparison. Internet Res. 21(2), 138–153 (2011)CrossRefGoogle Scholar
  7. 7.
    Verma, V.K., Singh, S., Pathak, N.P.: Towards comparative evaluation of trust and reputation models over static, dynamic and oscillating wireless sensor networks. Wirel. Netw. 23, 335 (2017).  https://doi.org/10.1007/s11276-015-1144-4CrossRefGoogle Scholar
  8. 8.
    Verma, V.K., Singh, S., Pathak, N.P.: Collusion based realization of trust and reputation models in extreme fraudulent environment over static and dynamic wireless sensor networks. Int. J. Distrib. Sens. Netw. (2014).  https://doi.org/10.1155/2014/672968. Advanced Convergence Technologies and Practices for Wireless Ad Hoc and Sensor NetworksCrossRefGoogle Scholar
  9. 9.
    Verma, V.K.: Pheromone and path length factor-based trustworthiness estimations in heterogeneous wireless sensor networks. Sens. J. IEEE 17, 215–220 (2017). ISSN 1530-437XCrossRefGoogle Scholar
  10. 10.
    Gomez Mármol, F., Martínez Perez, G.: TRMSim-WSN, trust and reputation models simulator for wireless sensor networks. In: Proceedings of the IEEE International Conference on Communications, IEEE ICC 2009, Communication and Information Systems Security Symposium, Dresden, Germany, June 2009Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Sant Longowal Institute of Engineering and Technology, Deemed to be UniversityLongowalIndia

Personalised recommendations