Skip to main content

Improved Heterogeneous Human Walk Mobility Model with Hub and Gateway Identification

  • Conference paper
Distributed Computing and Networking (ICDCN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8314))

Included in the following conference series:

Abstract

Heterogeneous Human Walk (HHW) model [1] mimics human mobility and is based on two important properties of social network: overlapping community structure and heterogeneous popularity. But, it does not produce heterogeneous local popularities of nodes in a community as observed in real mobility traces. Further, it does not consider Levy walk nature of human mobility which has significant impact on performance of protocols. We propose Improved Heterogeneous Human Walk (IHHW) model that correctly produces heterogeneous local popularities and also incorporates Levy walk nature of human mobility within overlapping community structure. As popular nodes are very useful for data dissemination, we also propose theoretical methods to identify popular nodes within community (hubs) and in entire network (gateways) from overlapping community structure itself. These nodes can act as hubs/gateways till overlapping community structure does not change. Our methods eliminate the need to identify and change these nodes dynamically when network is operational.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Yang, S., Yang, X., Zhang, C., Spyrou, E.: Using social network theory for modeling human mobility. IEEE Network 24(5), 6–13 (2010)

    Article  Google Scholar 

  2. Hui, P., Crowcroft, J., Yoneki, E.: Bubble rap: Social-based forwarding in delay-tolerant networks. IEEE Transactions on Mobile Computing 10(11), 1576–1589 (2011)

    Article  Google Scholar 

  3. Chaintreau, A., Hui, P., Crowcroft, J., Diot, C., Gass, R., Scott, J.: Impact of human mobility on opportunistic forwarding algorithms. IEEE Transactions on Mobile Computing 6(6), 606–620 (2007)

    Article  Google Scholar 

  4. Boldrini, C., Conti, M., Passarella, A.: Impact of social mobility on routing protocols for opportunistic networks. In: IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2007, pp. 1–6. IEEE (2007)

    Google Scholar 

  5. Hui, P., Chaintreau, A., Scott, J., Gass, R., Crowcroft, J., Diot, C.: Pocket switched networks and human mobility in conference environments. In: Proceedings of the 2005 ACM SIGCOMM Workshop on Delay-Tolerant Networking, pp. 244–251. ACM (2005)

    Google Scholar 

  6. Eagle, N., Pentland, A.S.: Reality mining: sensing complex social systems. Personal and Ubiquitous Computing 10(4), 255–268 (2006)

    Article  Google Scholar 

  7. Rhee, I., Shin, M., Hong, S., Lee, K., Kim, S.J., Chong, S.: On the levy-walk nature of human mobility. IEEE/ACM Transactions on Networking (TON) 19(3), 630–643 (2011)

    Article  Google Scholar 

  8. Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)

    Article  Google Scholar 

  9. Hyytiä, E., Koskinen, H., Lassila, P., Penttinen, A., Roszik, J., Virtamo, J.: Random waypoint model in wireless networks. In: Networks and Algorithms: Complexity in Physics and Computer Science, Helsinki (2005)

    Google Scholar 

  10. Groenevelt, R., Altman, E., Nain, P.: Relaying in mobile ad hoc networks: the brownian motion mobility model. Wireless Networks 12(5), 561–571 (2006)

    Article  Google Scholar 

  11. Hsu, W.J., Spyropoulos, T., Psounis, K., Helmy, A.: Modeling spatial and temporal dependencies of user mobility in wireless mobile networks. IEEE/ACM Transactions on Networking 17(5), 1564–1577 (2009)

    Article  Google Scholar 

  12. Mei, A., Stefa, J.: Swim: A simple model to generate small mobile worlds. In: INFOCOM 2009, pp. 2106–2113. IEEE (2009)

    Google Scholar 

  13. Lee, K., Hong, S., Kim, S.J., Rhee, I., Chong, S.: Slaw: A new mobility model for human walks. In: INFOCOM 2009, pp. 855–863. IEEE (2009)

    Google Scholar 

  14. Newman, M.E.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  15. Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)

    Article  Google Scholar 

  16. Musolesi, M., Mascolo, C.: Designing mobility models based on social network theory. ACM SIGMOBILE Mobile Computing and Communications Review 11(3), 59–70 (2007)

    Article  Google Scholar 

  17. Boldrini, C., Passarella, A.: Hcmm: Modelling spatial and temporal properties of human mobility driven by users social relationships. Computer Communications 33(9), 1056–1074 (2010)

    Article  Google Scholar 

  18. Ekman, F., Keränen, A., Karvo, J., Ott, J.: Working day movement model. In: Proceedings of the 1st ACM SIGMOBILE Workshop on Mobility Models, pp. 33–40. ACM (2008)

    Google Scholar 

  19. Watts, D.J.: Small worlds: the dynamics of networks between order and randomness. Princeton university press (1999)

    Google Scholar 

  20. Grinstead, C.C.M., Snell, J.L.: Introduction to probability. American Mathematical Soc. (1997)

    Google Scholar 

  21. Keränen, A., Ott, J., Kärkkäinen, T.: The one simulator for dtn protocol evaluation. In: Proceedings of the 2nd International Conference on Simulation Tools and Techniques, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), p. 55 (2009)

    Google Scholar 

  22. Pirie, W.: Spearman rank correlation coefficient. In: Encyclopedia of Statistical Sciences (1988)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Narmawala, Z., Srivastava, S. (2014). Improved Heterogeneous Human Walk Mobility Model with Hub and Gateway Identification. In: Chatterjee, M., Cao, Jn., Kothapalli, K., Rajsbaum, S. (eds) Distributed Computing and Networking. ICDCN 2014. Lecture Notes in Computer Science, vol 8314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45249-9_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-45249-9_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45248-2

  • Online ISBN: 978-3-642-45249-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics