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Energy Conscious Packet Transmission in Wireless Networks Using Trust Based Mechanism: A Cognitive Approach

  • Anshu BhasinEmail author
  • Sandeep Singh
  • Anshul Kalia
Chapter
  • 27 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1132)

Abstract

The self-motivated nature of wireless ad-hoc networks deters the possibility of a centralized solution. Also, no specific node can act as a centralized point due to energy and processing constraints. Constraint of non-centralization demands efficient and effective transmission of data between nodes by sharing information whenever needed without any disruption. This co-operation is a prodigious challenge due to the presence of covetous and malicious nodes in the network. Hence, an asserted need of some lightweight trust based mechanism in differentiating among reliable and unreliable nodes arises. This mechanism enhances security and improve co-operation in nodes. Energy efficiency remains central to the above segregation. Many trust-based methods are proposed which use packet delivered ratio as the major parameter for direct trust calculation. This work presents investigation of other related parameters like routing overhead, energy level etc. which can increase the effectiveness of trust based mechanisms for early detection of malicious nodes along with packet delivered ratio. Furthermore, an ameliorated energy optimization model is proposed for wireless network.

Keywords

Wireless ad-hoc networks Routing Attacks Energy MANET 

References

  1. 1.
    Merlin, R.T., Ravi, R.: Novel trust based energy aware routing mechanism for mitigation of black hole attacks in MANET. Wirel. Pers. Commun. 104, 1599–1636 (2019)CrossRefGoogle Scholar
  2. 2.
    Mayti, M., Khatoun, R., Begriche, Y., Khoukhi, L., Gaiti, D.: A stochastic approach for packet dropping attacks detection in mobile ad hoc networks. Comput. Netw. 121, 53–64 (2017)CrossRefGoogle Scholar
  3. 3.
    Airehrour, D., Gutierrez, J.A., Ray, S.K.: SecTrust-RPL: a secure trust-aware RPL routing protocol for Internet of Things. Future Gener. Comput. Syst. (2018).  https://doi.org/10.1016/j.future.2018.03.021CrossRefGoogle Scholar
  4. 4.
    Khan, F.A., Imran, M., Abbas, H., Durad, M.H.: A detection and prevention system against collaborative attacks in mobile ad hoc networks. Future Gener. Comput. Syst. 68, 416–427 (2017)CrossRefGoogle Scholar
  5. 5.
    Wei, Z., Tang, H., Yu, F.R., Wang, M., Mason, P.: Security enhancements for mobile ad hoc networks with trust management using uncertain reasoning. IEEE Trans. Veh. Technol. 63, 4647–4658 (2014)CrossRefGoogle Scholar
  6. 6.
    Dorri, A., Vaseghi, S., Gharib, O.: DEBH: detecting and eliminating black holes in mobile ad-hoc network. Wirel. Netw. 24, 2943–2955 (2017)CrossRefGoogle Scholar
  7. 7.
    Badiwal, S., Kulshrestha, A., Garg, N.: Analysis of black hole attack in MANET using AODV routing protocol. Int. J. Comput. Appl. 168(8), 27–33 (2017)Google Scholar
  8. 8.
    Paliwal, G., Taterh, S.: Impact of dense network in MANET routing protocols AODV and DSDV comparative analysis through NS3. Springer, Heidelberg (2018). Chap. 30Google Scholar
  9. 9.
    Li, X., Jia, Z., Zhang, P., Zhang, R., Wang, H.: Trust based on-demand multipath routing in mobile ad hoc networks. IET Inf. Secur. 4, 212–232 (2010)CrossRefGoogle Scholar
  10. 10.
    Tseng, F.H., Chiang, H.P., Chao, H.C.: Black hole along with other attacks in MANET: a survey. J. Inf. Process Syst. 14, 56–78 (2018)Google Scholar
  11. 11.
    Yaseen, Q.M., Aldwairi, M.: An enhanced AODV protocol for avoiding black holes in MANET. Procedia Comput. Sci. 134, 371–376 (2018)CrossRefGoogle Scholar
  12. 12.
    Babu, M.R., Usha, G.: A novel honeypot based detection and isolation approach (NHBADI) to detect and isolate black hole attacks in MANET. Wirel. Pers. Commun. 90, 831–845 (2016)CrossRefGoogle Scholar
  13. 13.
    Nadeem, A., Howarth, M.P.: A survey of manet intrusion detection & prevention approaches for network layer attacks. IEEE Commun. Surv. Tutor. 15(4), 2027–2045 (2013)CrossRefGoogle Scholar
  14. 14.
    Sood, M., Rani, P.: Removal of black hole attack using AODV protocol in MANET. Int. J. Eng. Manag. Res. (IJEMR) 7(3), 72–75 (2017)Google Scholar
  15. 15.
    Khanna, N.: Mitigation of collaborative black hole attack using TRACEROUTE mechanism with enhancement in AODV routing protocol. IJFGCN 9(1), 157–166 (2016)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Kaur, P., Kaur, D., Mahajan, R.: Simulation based comparative study of routing protocols under wormhole attack in MANET. Wirel. Pers. Commun. 96(1), 47–63 (2017)CrossRefGoogle Scholar
  17. 17.
    Singh, P., Paprzycki, M., Bhargava, B., Chhabra, J., Kaushal, N., Kumar, Y.(eds.): Futuristic trends in network and communication technologies, FTNCT 2018. Communications in Computer and Information Science, vol. 958. Springer, Singapore (2018)Google Scholar
  18. 18.
    Chander, D., Kumar, R.: Performance analysis of CBR and VBR applications on different multicast routing protocols over MANET. In: FTNCT 2018. Communications in Computer and Information Science, vol. 958, pp. 396–411. Springer, Singapore (2019)Google Scholar
  19. 19.
    Selvi, M., Thangaramya, K., Ganapathy, S., Kulothungan, K., Khannah Nehemiah, H., Kannan, A.: An energy aware trust based secure routing algorithm for effective communication in wireless sensor networks. Wirel. Pers. Commun.  https://doi.org/10.1007/s11277-019-06155-xCrossRefGoogle Scholar
  20. 20.
    Salman, M.S., Zhu, N., He, J., Zardari, Z.A., Memon, M.Q., Hussain, M.I.: An efficient trust-based scheme for secure and quality of service routing in MANETs. Future Internet 10, 16 (2018).  https://doi.org/10.3390/fi10020016CrossRefGoogle Scholar
  21. 21.
    Zahedi, A., Parma, F.: An energy-aware trust-based routing algorithm using gravitational search approach in wireless sensor networks. Peer-to-Peer Netw. Appl. (2018).  https://doi.org/10.1007/s12083-018-0654-0CrossRefGoogle Scholar
  22. 22.
    Cai, R.J., Li, X.J., Chong, P.H.J.: An evolutionary self-cooperative trust scheme against routing disruptions in MANETs. IEEE (2018).  https://doi.org/10.1109/TMC.2018.2828814CrossRefGoogle Scholar
  23. 23.
    Liang, W., Long, J., Weng, T.H., Chen, X., Li, K.C., Zomaya, A.Y.: TBRS: a trust based recommendation scheme for vehicular CPS network. Future Gener. Comput. Syst. (2018).  https://doi.org/10.1016/j.future.2018.09.002CrossRefGoogle Scholar
  24. 24.
    Beghriche, A., Bilami, A.: A fuzzy trust-based routing model for mitigating the misbehaving nodes in mobile ad hoc networks. Int. J. Intell. Comput. Cybern. (2018).  https://doi.org/10.1108/IJICC-04-2017-0038CrossRefGoogle Scholar
  25. 25.
    Shanthi, K., Murugan, D., Kumar, T.: Trust-based intrusion detection with secure key management integrated into MANET. Inf. Secur. J. Glob. Perspect. 27(4), 183–191 (2018).  https://doi.org/10.1080/19393555.2018.1505007CrossRefGoogle Scholar
  26. 26.
    Anusha, K., Sathiyamoorthy, E.: A new trust-based mechanism for detecting intrusions in MANET. Inf. Secur. J. Glob. Perspect. 26(4), 153–165 (2017).  https://doi.org/10.1080/19393555.2017.1328544CrossRefGoogle Scholar
  27. 27.
    Sethuraman, P., Kannan, N.: Refined trust energy-ad hoc on demand distance vector (ReTE-AODV) routing algorithm for secured routing in MANET. Wirel. Netw. (2016).  https://doi.org/10.1007/s11276-016-1284-1CrossRefGoogle Scholar
  28. 28.
    Xia, H., Yu, J., Tian, C., Pan, Z., Sha, E.: Light-weight trust-enhanced on-demand multi-path routing in mobile Ad Hoc networks. J. Netw. Comput. Appl. (2015).  https://doi.org/10.1016/j.jnca.2015.12.005CrossRefGoogle Scholar
  29. 29.
    Zhao, D., Zhen, M., Zhang, D.: A distributed and adaptive trust evaluation algorithm for MANET. ACM (2016). http://dx.doi.org/10.1145/2988272.2990297
  30. 30.
    Shaikh, R.A., Jameel, H., d’Auriol, B.J., Lee, H., Lee, S., Song, Y.-J.: Group-based trust management scheme for clustered wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 20, 1698–1712 (2009)CrossRefGoogle Scholar
  31. 31.
    Yan, Z., Zhang, P., Vasilakos, A.V.: A survey on trust management for Internet of Things. J. Netw. Comput. Appl. 42, 120–134 (2014)CrossRefGoogle Scholar
  32. 32.
    Khanna, N., Sachdeva, M.: Study of trust based mechanism and its component model in MANET: current research state, issues, and future recommendation. Int. J. Commun. Syst. e4012 (2019).  https://doi.org/10.1002/dac.4012CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.IKG Punjab Technical UniversityKapurthalaIndia

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