Predicting the position of adjacent nodes with QoS in mobile ad hoc networks

  • C. Chandru Vignesh
  • S. Karthik


The Mobile Ad Hoc Networks are a self-regulatory set of autonomous nodes which perform communication to all the other nodes within their communication ranges. The nodes which are not in straightforward range make use of in between nodes to perform communication with one another. In mobile ad hoc network, each and every autonomous node holds displacements and shifts based on the precise positions within the network. Hence the verification of node position is crucial in mobile ad hoc networks and it is mainly a great dispute during the existence of opponents focusing on damaging the system. The intention is to design a standard termed as Adjacent Node Location Confirmation (ANLC) for confirming the location of its transmitting adjacent nodes for interchanging the messages and confirms the location of the nodes in transmission within the network. Initially, the method focuses on finding the nodes based on which the transmission connection is set up or it is within the fixed distance. The distance is estimated based on message interchanges among the confirmer and its adjacent nodes in transmission. Soon after the estimation of distances the confirmer authenticates the location of nodes in transmission within the network based on straight balanced, traverse balanced and multi-lateration analysis. The analysis is performed based on QoS of the transmitting node choice for minimizing the delays and acquiring improved throughput. The performance of the designed scheme is estimated based on network throughput and delays.


ANLC Self-regulatory QoS Adjacent nodes Throughput and Delays NV 


  1. 1.
    Calandriello G, Papadimitratos P, Lioy A, Hubaux JP (2011) On the performance of secure vehicular communication systems. IEEE Trans Dependable Secure Comput 8(6):898–912CrossRefGoogle Scholar
  2. 2.
    Capkun S, Hubaux JP (2006) Secure positioning in wireless networks. IEEE J Sel Areas Commun 24(2):221–232CrossRefGoogle Scholar
  3. 3.
    Capkun S, Rasmussen K, Cagalj M, Srivastava M (2008) Secure location verification with hidden and mobile base stations. IEEE Trans Mob Comput 7(4):470–483CrossRefGoogle Scholar
  4. 4.
    Chen YH, Wu H, Lin CH and Chen GH (2017) Bandwidth-satisfied and coding-aware multicast protocol in MANETs. In: IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp 1–1.
  5. 5.
    Chiang J, Haas J, Hu Y (2009) Secure and precise location verification using distance bounding and simultaneous multilateration. ACM WiSec, Zurich, pp 181–192Google Scholar
  6. 6.
    Ekici E, Vural S, McNair J, Al-Abri D (2008) Secure probabilistic location verification in randomly deployed wireless sensor networks. Elsevier Ad Hoc Netw 6(2):195–209CrossRefGoogle Scholar
  7. 7.
    Harri J, Fiore M, Filali F and Bonnet C (2009) Vehicular mobility simulation with VanetMobiSim. Trans Soc Model Simul 4(87):275–300.
  8. 8.
    Hu YC, Perrig A, Johnson DB (2003) Packet leashes: a defense against wormhole attacks in wireless networks. IEEE Infocom 3:1976–1986Google Scholar
  9. 9.
    Hwang J, He T, Kim Y (2007) Detecting phantom nodes in wireless sensor networks. 26th IEEE International Conference on Computer Communications, Anchorage, AK, pp 2391–2395.
  10. 10.
    Imaizumi N, Kobayashi K, Utsu K et al (2016) A study on effective flooding over MANET based on exchange of neighbor information. J Supercomput Springer 72:1237.
  11. 11.
    Kandari S, Pandey MK (2016) Impact of residual life estimator battery model on QoS issues in MANET. Wirel Pers Commun Springer 86:601.
  12. 12.
    Kumar S, Dutta K (2016) Securing mobile ad hoc networks: challenges and solutions. Int J Handheld Comput Res 7(1):26–76. CrossRefGoogle Scholar
  13. 13.
    Lazos L, Poovendran R (2006) HiRLoc: high-resolution robust localization for wireless sensor networks. IEEE JSAC 24(2):233–246Google Scholar
  14. 14.
    Maheshwari R, Gao J, Das SR (2007) Detecting wormhole attacks in wireless networks using connectivity information. IEEE INFOCOM 2007 – 26th IEEE International Conference on Computer Communications, Anchorage, AK, pp 107–115.
  15. 15.
    Niu D, Rui L, Huang H, Qiu X (2017) A service recovery method based on trust evaluation in mobile social network. Multimed Tools Appl Springer 76:3255.
  16. 16.
    Papadimitratos P, Poturalski M, Schaller P, Lafourcade P, Basin D, Capkun S, Hubaux JP (2008) Secure neighborhood discovery: a fundamental element for mobile ad hoc networks. IEEE Commun Mag 46(2):132–139CrossRefGoogle Scholar
  17. 17.
    Poovendran R, Lazos L (2007) A graph theoretic framework for preventing the wormhole attack. Wirel Netw 13:27–59CrossRefGoogle Scholar
  18. 18.
    Poturalski M, Papadimitratos P and Hubaux JP (2008) Secure neighbor discovery in wireless networks: formal investigation of possibility. ACM ASIACCS, pp 189–200Google Scholar
  19. 19.
    Rath M, Pattanayak BK (2018) Information and communication technology for intelligent systems (ICTIS 2017) – volume 2, pp 579–586.
  20. 20.
    Song J-H, Vincent W, Wong S, Victor C, Leung M (2008) Secure location verification for vehicular Ad-Hoc networks. IEEE Global Telecommunications Conference, New Orleans, LO, pp 1–5.
  21. 21.
    Vignesh CC, Christopher Paul A, and Karthik S (2013) Enhancing Clock Skew by measuring Least Square Fit method to identifying unauthorized nodes in Wireless Ad-Hoc NetworksGoogle Scholar
  22. 22.
    Wang W, Yang B, Takahashi O, Jiang X, Shen S (2018) On the packet delivery delay study for three-dimensional mobile ad hoc networks. Ad Hoc Netw 69:38–48, ISSN 1570-8705. CrossRefGoogle Scholar
  23. 23.
    Zhong S, Jadliwala M, Upadhyaya S, Qiao C (2008) Towards a theory of robust localization against malicious beacon nodes. IEEE Infocom, PhoenixCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSNS College of TechnologyCoimbatoreIndia

Personalised recommendations