Performance Analysis of A-Based Hop Selection Technique in Opportunistic Networks Through Movement Mobility Models

  • Pragya Kuchhal
  • Satbir Jain
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 27)


In recent times, it has been observed that opportunistic networks (OPPNET) are attracted by many researchers. The reason behind it is that no end-to-end connectivity exists and still the messages are communicated from the source to destination in this network. This is because, this network uses the node’s mobility for routing and hence selection of mobility models are considered very crucial while evaluating the movement of the nodes in any routing protocol. Appropriate selection of a mobility model is important for evaluating the performance of the protocol and secondly, to substantiate its applicability in the real-world scenario. This chapter analyzes the impact that three specific mobility models (namely SPMBM, RWP, and RTT) have on the performance of the existing A*OR protocol for OppNets using average hop count, delivered messages, number of message dropped, overhead ratio, and average latency as performance metrics. A suitable environment is set up using ONE simulator for simulating the AOR protocol over different movement models. Experimental results show significant variations in the performance of AOR for the SPMBM, RWP, and RTT. In terms of delivery predictability, the SPMBMM model is 80% better than RWP and 90% than RTT. The average latency of the SPMBMM model is 16% better than RWP and 59% than RTT when TTL varies in the network.


  1. 1.
    C.-M. Huang, K.-C. Lan, C.-Z, Tsai, A Survey of opportunistic networks, in Proceedings of the 22nd International Conference on Advanced Information Networking and Applications – Workshops (IEEE, Piscataway, 2008), pp. 1672–1677Google Scholar
  2. 2.
    M. Demmer, J. Ott, S. Perreault, Delay tolerant networking TCP convergence layer protocol. Experimental RFC 7242, 2014Google Scholar
  3. 3.
    Y. Cao, Z. Sun, Routing in delay/disruption tolerant networks: a taxonomy, survey and challenges. IEEE Commun. Surv. Tutorials 15(2), 654–677 (2013)CrossRefGoogle Scholar
  4. 4.
    J. Yick, B. Mukherjee, D. Ghosal, Wireless sensor network survey. Comput. Netw. 52(12), 2292–2330 (2008)CrossRefGoogle Scholar
  5. 5.
    K. Fall, S. Farrell, DTN: an architectural retrospective. IEEE J. Sel. Areas Commun. 26(5), 828–836 (2008)CrossRefGoogle Scholar
  6. 6.
    M. Musolesi, C. Mascolo, Designing mobility models based on social network theory. ACM SIGMOBILE Mobile Comput. Commun. Rev. 11(3), 59–70 (2007)CrossRefGoogle Scholar
  7. 7.
    T. Camp, J. Boleng, V. Davies, A survey of mobility models for ad hoc network research. Wirel. Commun. Mob. Comput. 2(5), 483–502 (2002)CrossRefGoogle Scholar
  8. 8.
    C. Song, Z. Qu, N. Blumm, A.Barabasi, Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010). MathSciNetCrossRefGoogle Scholar
  9. 9.
    S. Batabyal, P. Bhaumik, Mobility models, traces and impact of mobility on opportunistic routing algorithms: a survey. IEEE Commun. Surv. Tutorials 17(3), 1679–1707 (2015). CrossRefGoogle Scholar
  10. 10.
    P. Kuchhal, S.K. Dhurandher, S.J. Borah, I. Woungang, S. Jain, S. Gupta, A* search based next hop selection for routing in opportunistic networks. Int. J. Space-Based Situat. Comput. 7(3), 177–186 (2017)CrossRefGoogle Scholar
  11. 11.
    M. Kim, D. Kotz, S. Kim, Extracting a mobility model from real user traces, in Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications (IEEE, Piscataway, 2006), pp. 1–13Google Scholar
  12. 12.
    A. Keranen, Keranen A. Opportunistic Network Environment Simulator. Special Assignment Report, Helsinki University of Technology, Department of Communications and Networking, 2008Google Scholar
  13. 13.
    A. Keranen, J. Ott, Increasing Reality for DTN Protocol Simulations. Special Technical Report, Helsinki University of Technology, Networking Laboratory, 2007Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pragya Kuchhal
    • 1
  • Satbir Jain
    • 1
  1. 1.Netaji Subhas Institute of TechnologyUniversity of DelhiNew DelhiIndia

Personalised recommendations