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Available Bandwidth Estimation Using Collision Probability, Idle Period Synchronization and Random Waiting Time in MANETs: Cognitive Agent Based Approach

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Abstract

In Mobile Ad hoc NETworks (MANETs), each node estimates available bandwidth (AB) before transmission of real-time multimedia data, as the channel is shared. The existing AB estimation techniques lack accuracy and add overhead. Accuracy of these AB estimation techniques could be enhanced by adopting some intelligent techniques. Intelligent software agents such as cognitive agent (CA) have significant potential to solve the challenges of estimating AB in MANETs. Intelligence is provided similar to the logical thinking like human for decision making in CA. The predominant CA architecture is belief desire intention model, which performs the task on behalf of human user as an assistant to him/her. In this paper, we propose CA-based available bandwidth estimation using collision probability, Idle period synchronization and random waiting time (BECIT) in MANETs. The proposed BECIT technique uses CA at each node to create mobile agents for collecting and distributing the statistics (such as idle period, packet loss, and AB, etc.) over the network. The collected network statistics are used by CA to estimate AB using our previous work distributed lagrange interpolation based AB estimation (DLI-ABE). The DLI-ABE comprises of statistical models for idle period synchronization by differentiating their states as BUSY, IDLE, and SENSE BUSY; collision probability using distributed Lagrange Interpolation polynomial; and random waiting time that consumes AB such as request-to-send, clear-to-send commands in IEEE 802.11 standard. Estimation of end-to-end AB on a multi-hop path uses each node AB. The simulation results show that BECIT performs approximately 30 % more accurate than the non-agent based AB estimation scheme. Memory overhead is essential to store knowledge base for agents whereas the computational overhead is due to Lagrange Interpolation polynomial at each node in the proposed scheme.

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References

  1. Hanzo, L., Haas, H., Imre, S., O’Brien, D., Rupp, M., & Gyongyosi, L. (2012). Wireless myths, realities, and futures: From 3G/4G to optical and quantum wireless. Proceedings of the IEEE Special Centennial Issue, 100, 853–888.

    Google Scholar 

  2. Siva Ram Murthy, C., & Manoj, B. S. (2004). Ad hoc wireless networks, architectures and protocol. Pearson: Prentice-Hall.

    Google Scholar 

  3. Alzate, M. A.., Pea, N..M., Labrador, & Miguel, A. (2008). Capacity, Bandwidth and available bandwidth concepts for wireless ad hoc networks. In Proceedings of the international conference on military communications (pp. 1–7) San Diego, CA.

  4. Zhao, H., Garcia-Palacios, E., Wang, S., Wei, J., & Ma, D. (2013). Evaluating the impact of network density, hidden nodes and capture effect for throughput guarantee in multi-hop wireless networks. Journals of Ad Hoc Networks, 11(1), 54–69.

    Article  Google Scholar 

  5. Jennings, N. R. (2001). An Agent-based approach for building complex software systems. Communications of the ACM, 44, 35–41.

    Article  Google Scholar 

  6. Uhrmacher, A. M., & Weyns, D. (2009). Multi-agent systems simulation and applications. Boca Raton: CRC Press, Taylor and Francis Group.

    Google Scholar 

  7. Manvi, S., & Venkataram, P. (2004). Applications of agent technology in communications: A review. Computer Communications, 27(15), 1493–1508.

    Article  Google Scholar 

  8. Padgham, L., & Winikoff, M. (2004). Developing intelligent agent systems: A practical guide. London: Wiley.

    Book  Google Scholar 

  9. Chaudhari, S. S., & Biradar, R. C. (2014). Collision probability based available bandwidth estimation in mobile ad hoc networks. In Proceedings of the international conference on the applications of digital information and web technologies (pp. 244–249). Chennai, India.

  10. Chaudhari, S. S., & Biradar, R. C. (2015) Survey of bandwidth estimation techniques in communication networks. Wireless Personal Communication, US: Springer. doi:10.1007/s11277-015-2459-2.

  11. Turrubiartes, M., Torres, D., Angulo, M., & Munoz, D. (2005). Analysis of IP network path capacity estimation using a variable packet size method. 15th international conference on electronics, communications and computers (pp. 177–182).

  12. Xiao, Y., Chen, S., Li, X., & Li, Y. (2007). A new available measurement method based on self-loading periodic streams. Wireless communications, networking and mobile computing (pp. 1904–1907).

  13. Jain, M. (2003). End-to-end available bandwidth: Measurement methodology, dynamics, and relation with TCP throughput. IEEE/ACM Transactions on Networking, 11(4), 537–549.

    Article  Google Scholar 

  14. Melander, B., Bjorkman, M., & Gunningberg, P. (2000). A new end-to-end probing and analysis method for estimating bandwidth bottlenecks. IEEE Global Telecommunications Conference, 1, 415–420.

    Article  Google Scholar 

  15. Obara, H., Koseki, S., & Selin, P. (2012). Packet train pair: A fast and efficient technique for measuring available bandwidth in the internet. SICE annual conference (SICE) (pp. 1833–1836).

  16. Zhiguo, H., Zhang, D., Zhu, A., Chen, Z., & Zhou, H. (2012). SLDRT: A measurement technique for available bandwidth on multi-hop path with bursty cross traffic. Computer Networks, 56(14), 3247–3260.

    Article  Google Scholar 

  17. Thouin, F., Coates, M., & Rabbat, M. (2011). Large scale probabilistic available bandwidth estimation. Computer Networks, 55(9), 2065–2078.

    Article  Google Scholar 

  18. Ningning, H., & Steenkiste, P. (2003). Evaluation and characterization of available bandwidth probing techniques. IEEE Journal on Selected Areas in Communications, 21(6), 879–894.

    Article  Google Scholar 

  19. Li, M., & Chang, C. R. (2009). A two-way available bandwidth estimation scheme for multimedia streaming networks adopting scalable video coding. IEEE Sarnoff symposium (pp. 1–6).

  20. Li, M., Wu, Y. L ., & Chang, C. R. (2013). Available bandwidth estimation for the network paths with multiple tight links and bursty traffic. Journal of Network and Computer Applications, 36(1), 353–367.

    Article  Google Scholar 

  21. Dey, B. K., Manjunath, D., & Chakraborty, S. (2011). Estimating network link characteristics using packet-pair dispersion: A discrete-time queueing theoretic analysis. Computer Networks, 55(5), 1052–1068.

    Article  MATH  Google Scholar 

  22. Park, K. J, Lim, H., Hou, J. C., & Choi, C. H. (2009). Feedback-assisted robust estimation of available bandwidth. Computer Networks, 53(7), 896–912.

    Article  MATH  Google Scholar 

  23. Tunali, T., & Anar, K. (2006). Adaptive available bandwidth estimation for internet video streaming. Signal Processing: Image Communication, 21(3), 217–234.

    Google Scholar 

  24. Bergfeldt, E., Ekelin, S., & Karlsson, J. M. (2009). Real-time available-bandwidth estimation using filtering and change detection. Computer Networks, 53(15), 2617–2645.

    Article  MATH  Google Scholar 

  25. Sedighizad, M., Seyfe, B., & Navaie, K. (2012). MR-BART: Multi-rate available bandwidth estimation in real-time. Journal of Network and Computer Applications, 35(2), 731–742.

    Article  Google Scholar 

  26. Nam, S. Y., Kim, S., & Park, W. (2012). Analysis of minimal backlogging-based available bandwidth estimation mechanism. Journal Computer Communications, 35(4), 431–443.

    Article  Google Scholar 

  27. Calafate, C. T., Manzoni, P., & Malumbres, M. P. (2005). Supporting soft real-time services in MANETs using distributed admission control and ieee 802.11e technology. 10th IEEE symposium on computers and communications (pp. 217–222).

  28. Hoang, V. D., Shao, Z., & Fujise, M. (2006). Efficient load balancing in MANETS to improve network performance. 6th international conference on ITS telecommunications proceedings (pp. 753–756).

  29. Hoang, V. D., Shao, Z., & Fujise, M. (2006). A new solution to estimate the available bandwidth in MANETs. IEEE 63rd vehicular technology conference vol. 2, pp. 653–657.

  30. Alzate, M. A., Pagn, J. C., Pea, N. M., & Labrador, M. A. (2008). End-to-end bandwidth and available bandwidth estimation in multi-hop IEEE 802.11b ad hoc networks. 42nd annual conference on information sciences and systems (pp. 659–664).

  31. Ergin, M. A., Gruteser, M., Luo, L., Raychaudhuri, D., & Liu, H. (2008). Available bandwidth estimation and admission control for QoS routing in wireless mesh networks. Computer Communications, 31(7), 1301–1317.

    Article  Google Scholar 

  32. de Renesse, R., Ghassemian, M., Friderikos, V., & Aghvami, A. H. (2004). QoS enabled routing in mobile Ad-hoc networks. In Proceedings of the international conference on 3G mobile communication technologies (pp. 678–682).

  33. Chaudet, C., & Lassous, I. G. (2002). BRuIT: Bandwidth reservation under interferences influence. Proceedings of the European Wireless.

  34. Yang, Y., & Kravets, R. (2005). Contention aware admission control for Ad-hoc networks. IEEE Transaction on Mobile Computing, 4(4), 363–377.

    Article  Google Scholar 

  35. de Renesse, R., Ghassemian, M., Friderikos, V., & Aghvami, A. H. (2005). Adaptive admission control for Ad-hoc and sensor networks providing quality of service. Technical report, King College London.

  36. Tursunova, S., Inoyatov, K., & Kim, Y. T. (2010). Cognitive passive estimation of available bandwidth (cPEAB) in overlapped IEEE 802.11 WiFi WLANs. Proceedings of the IEEE network operations and management symposium (pp. 448–454).

  37. sarr, C., Chaudet, C., Chelius, G., & Lassous, I. G. (2008). Bandwidth estimation for IEEE 802.11-based Ad-hoc networks. IEEE Transaction on Mobile Computing, 7(10), 1228–1241.

    Article  Google Scholar 

  38. Park, H. J., & Roh, B. H. (2010). Accurate passive bandwidth estimation (APBE) in IEEE 802.11 wireless LANs. Proceedings of the international conference on ubiquitous information technologies and applications (pp. 1–4).

  39. Zhao, H., Garcia-Palacios, E., Wei, J., & Xi, Y. (2009). Accurate available bandwidth estimation in IEEE 802.11-based Ad-hoc networks. Computer Communications, 32(6), 1050–1057.

    Article  Google Scholar 

  40. Yan, Z., Dapeng, W., Bin, W., Muqing, W., & Chunxiu, X. (2008). A novel call admission control routing mechanism for 802.11e based multi-hop MANET. In Proceedings of the international conference on wireless communications, networking and mobile computing (pp. 1–4), Dalian.

  41. Peng, Y., & Yan, Z. (2012). Available bandwidth estimating method in IEEE802.11e based mobile Ad-hoc network. In Proceedings of the international conference on fuzzy systems and knowledge discovery (pp. 2138–2142), Sichuan.

  42. Zhao, H., Garcia-Palacios, E., Wang, S., Wei, J., & Ma, D. (2013). Evaluating the impact of network density, hidden nodes and capture effect for throughput guarantee in multi-hop wireless networks. Ad Hoc Networks, 11(1), 54–69.

    Article  Google Scholar 

  43. Yuan, Z., Venkataraman, H., & Muntean, G. (2012). A novel bandwidth estimation algorithm for IEEE 802.11 TCP data transmissions. IEEE wireless communications and networking conference workshops (pp. 377–382).

  44. Liebeherr, J., Fidler, M., & Valaee, S. (2010). A system-theoretic approach to bandwidth estimation. IEEE/ACM Transactions on Networking, 18(4), 1040–1053.

    Article  Google Scholar 

  45. Yuan, Z. (2012). MBE: Model-based available bandwidth estimation for IEEE 802.11 data communications. IEEE Transactions on Vehicular Technology, 61(5), 2158–2171.

    Article  Google Scholar 

  46. Hei, X., Bensaou, B., & Tsang, D. H. K. (2006). Model-based end-to-end available bandwidth inference using queueing analysis. Computer Networks, 50(12), 1916–1937.

    Article  MATH  Google Scholar 

  47. Chobanyan, A., Mutka, M., Mandrekar, V., & Xi, N. (2008). End-to-end available bandwidth as a random autocorrelated QoS-relevant time-series. Computer Networks, 52(6), 1220–1237.

    Article  MATH  Google Scholar 

  48. Vieira, F. H. T., & Lee, L. L. (2009). Adaptive wavelet-based multifractal model applied to the effective bandwidth estimation of network traffic flows. IET Communications, 3(6), 906–919.

    Article  Google Scholar 

  49. Rao, A. S., & Georgeff, M. P. (1995). BDI-agents: From theory to practice. Proceedings of the international conference on multi-agent systems, San Francisco.

  50. Anderson, R., Bothell, D., Byrne, M., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of mind. Psychological Review, 111(4), 1036–1060.

    Article  Google Scholar 

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Acknowledgments

The authors wish to thank Visvesvaraya Technological University (VTU), Karnataka, INDIA, for funding the part of the project under VTU Research Scheme (Grant No. VTU/Aca./2011-12/A-9/753, Dated: 5 May 2012.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Reva Institute of Technology and Management, Visvesvaraya Technological University Research Center, Bangalore, 560 064, India

    Shilpa Shashikant Chaudhari

  2. School of Electronics and Communication Engineering, REVA University, Bangalore, 560 064, India

    Rajashekhar C. Biradar

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  1. Shilpa Shashikant Chaudhari
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Correspondence to Shilpa Shashikant Chaudhari.

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Chaudhari, S.S., Biradar, R.C. Available Bandwidth Estimation Using Collision Probability, Idle Period Synchronization and Random Waiting Time in MANETs: Cognitive Agent Based Approach. Wireless Pers Commun 85, 597–621 (2015). https://doi.org/10.1007/s11277-015-2797-0

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