Skip to main content
Log in

Theoretical estimation of border effect on epoch length distributions in wireless networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Mobility models are often used in simulation and analysis. An epoch mobility model can describe many mobile behaviors with epoch length distributions. Here an epoch is a random time interval, during which a node keeps moving at a constant velocity. Since wireless networks such as Mobile Ad Hoc Networks are often deployed or tested in a bordered space, borders may force a node to change its motion. This motion change may severely twist epoch length distributions by truncating the original epoch lengths. Many epoch length distribution models in the literature are originally established for an open space without taking into account this effect. Using this kind of models in a bordered environment inevitably causes a mismatch between the assumption and the actual situation. This mismatch may further lead to misinterpretations of the observed results, and it is necessary to study this effect on the epoch length distribution and to find a method to accurately describe the mobile behavior in bordered spaces. This paper just studies these methods for exponential and uniform epoch distributions in circles.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. As mentioned earlier, the border can influence the node distribution and the probability where a node starts its motion [4, 5, 10]. The uniform assumption here is mainly used to simplify the H(l) derivation.

  2. Refer to http://mathworld.wolfram.com/IncompleteGammaFunction.html for more detail on \(\Upgamma[0,z]\).

References

  1. Fu, S., Hou, Z., & Yang, G. (2009). An indoor navigation system for autonomous mobile robot using wireless sensor network. In International conference on networking, sensing and control (ICNSC), Okayama, Japan, Mar. pp. 227–232.

  2. Park, S., & Hashimoto, S. (2009). Autonomous mobile robot navigation using passive rfid in indoor environment. IEEE Transactions on Industrial Electronics, 7, 2366–2373.

    Google Scholar 

  3. Braunl, T. (1999). Research relevance of mobile robot competitions. IEEE Robotics & Automation Magazine, 6(4), 32–37.

    Article  Google Scholar 

  4. Durvy, M., Dousse, O., & Thiran, P. (2008). Border effects, fairness, and phase transition in large wireless networks. In Proceedings of IEEE INFOCOM, Phoenix, AZ, USA, Apr. 601–609.

  5. Jin, Y., Wang, L., Jo, J., Kim, Y., Yang, M., & Jiang, Y. (2009). Eeccr: An energy-efficient -coverage and -connectivity routing algorithm under border effects in heterogeneous sensor networks. IEEE Transactions on Vehicular Technology, 58(3), 1429–1442.

    Article  Google Scholar 

  6. Royer, E., Melliar-Smith, P., & Moser, L. (2001). An analysis of the optimum node density for ad hoc mobile networks. In Proceedings of IEEE international conference on communications (ICC), Vol. 3, Helsinki, Finland, Jun. pp. 857–861.

  7. Bettstetter, C., & Krause, O. (2001). On border effects in modeling and simulation of wireless ad hoc networks. In Proceedings of IEEE conference one nobile & wireless communication network (MWCN), Recife, Brazil.

  8. Yen, L., & Yu, C. (2004). Link probability, network coverage, and related properties of wireless ad hoc networks. In Proceedings of IEEE international conference on mobile ad-hoc & sensor systems, Fort Lauderdale, Florida USA, Oct. pp. 525–527.

  9. Bettstetter, C., & Zangl, J. (2002). How to achieve a connected ad hoc network with homogeneous range assignment: an analytical study with consideration of border effects. In Proceedings of IEEE conference on mobile & wireless communication network (MWCN), Stockholm. Sweden, Sep. pp. 125–129.

  10. Roy, R. (2011). Swarm group mobility model. In R. Roy (Ed.), Handbook of mobile ad hoc networks for mobility models (ch. 34, pp. 65–124), New York: Springer Science+Business Media

  11. Díaz, J., Mitsche, D., & Santi, P. (2011). Theoretical aspects of graph models for manets. In S. Nikoletseas & J. Rolim (Eds.), Theoretical aspects of distributed computing in sensor networks (ch. 6, pp. 161–190). Berlin: Springer.

  12. Clementi, A., Monti, A., & Silvestri, R. (2011). Modelling mobility: A discrete revolution. Ad Hoc Networks, 9(6), 998–1014.

    Article  Google Scholar 

  13. Kuo, J., & Liao, W. (2005). Modeling the behavior of flooding on target location discovery in mobile ad hoc networks. In Proceedings of IEEE international conference on communications (ICC), Seoul, Korea, pp. 16–20.

  14. Wu, Y., Ho, T., Liao, W., & Tsao, C. (2005). Epoch, length of the random waypoint model in mobile ad hoc networks. IEEE Communications Letters, 9(11), 1003–1005.

    Article  Google Scholar 

  15. Zhao, C., & Sichitiu, M. L. (2011). Contact time in random walk and random waypoint: Dichotomy in tail distribution. Ad Hoc Networks, 9(2), 152–163.

    Article  Google Scholar 

  16. Hu, Y., & Johnson, D. B. (2000). Caching strategies in on-demand routing protocols for wireless ad hoc networks. In Proceedings of annual ACM/IEEE international conference on mobile computing & network. (MobiCom), Boston, USA, pp. 231–242.

  17. Jiang, S. M., Liu, Y. D., Jiang, Y. M., & Yin, Q. H. (2004). Provisioning of adaptability to variable topologies for routing schemes in manets. IEEE Journal of Selected Areas in Communications, 22(7), 1347–1356.

    Article  Google Scholar 

  18. Liang, B., & Haas, Z. (1999). Predictive distance-based mobility management for pcs networks. In Proceedings of IEEE INFOCOM, Vol. 3, New York City, NY, USA, Mar. pp. 1377–1384.

  19. Nagshineh, M., & Schwartz, M. (1996). Distributed call admission control in mobile/wireless networks. IEEE Journal of Selected Areas Communications, 14(4), 711–716.

    Article  Google Scholar 

  20. Brust, M., Ribeiro, C., & Filho, J. (2009). Border effects in the simulation of ad hoc and sensor networks. In Proceedings of international conference on computer modelling & simulation (UKSIM), Cambridge, UK, Mar. pp. 180–185.

  21. Johnson, D., & Maltz, D. (1996). Dynamic source routing in ad hoc wireless networks. In T. Imielinski & H. Korth (Eds.), Mobile computing (ch. 5, pp. 153–181). Boston: Kluwer Academic Publishers.

  22. Jiang, S. M., Li, B., Luo, X. Y., & Tsang, D. H. K. (2001). A modified distributed call admission control scheme and its performance. ACM Wireless Networks (WINET), 2, 127–138.

  23. Ross, S. (1996). Stochastic process, 2nd ed. New York: Wiley.

    Google Scholar 

  24. Arnow, B. J. (1994). On laplace’s extension of the buffon needle problem. The College Mathematics Journal, 25(1), 40–43.

    Article  Google Scholar 

  25. Solomon, H. (1978). Geometric probability. Philadelphia: SIAM.

    Book  MATH  Google Scholar 

  26. Ren, D. L. (1994). Topics in integral geometry. Singapore: World Scientific.

    MATH  Google Scholar 

  27. Xu, Y., & Shi, Y. (2009). Note on buffon’s problem with a long needle. Applied Mathematical Sciences, 3(24), 1189–1192.

    MathSciNet  Google Scholar 

  28. Penrose, M. (2003). Random geometric graphs, 2nd ed. Oxford: Oxford Uinversity Press.

    Book  Google Scholar 

  29. Miles, R. (1980). A survey of geometrical probability in the plane, with emphasis on stochastic image modeling. Computer Graphics and Image Processing, 12(1), 1–24.

    Article  MathSciNet  Google Scholar 

  30. Santalo, L. A. (2004). Integral geometry and geometric probability, 2nd ed. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  31. Neuts, M., & Purdue, P. (1971). Buffon in the round. Mathematics Magazine, 44(2), 81–89.

    Article  MATH  MathSciNet  Google Scholar 

  32. Polyanin, A., & Manzhirov, A. (1998). Handbook of integral equations. Boca Raton: CRC Press.

    Book  MATH  Google Scholar 

  33. Jardosh, A., Belding-Royer, E., Almeroth, K., & Suri, S. (2005). Real-world environment models for mobile network evaluation. IEEE Journal of Selected Areas Communications, 23(3), 622–632.

    Article  Google Scholar 

Download references

Acknowledgments

The author would like to take this opportunity to sincerely thank he anonymous Reviewers and the Editors for their valuable and constructive comments to the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shengming Jiang.

Additional information

Part of this work was published in the International Workshop on Performance Evaluation of Wireless Networks in conjunction with the Fifth International Conference on Mobile Ad-Hoc and Sensor Networks (MSN), December, 2009, China.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jiang, S. Theoretical estimation of border effect on epoch length distributions in wireless networks. Wireless Netw 20, 705–718 (2014). https://doi.org/10.1007/s11276-013-0633-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-013-0633-6

Keywords

Navigation