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Multipath routing in mobile ad hoc network with probabilistic splitting of traffic

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Abstract

Multipath routing is a burning issue in mobile ad hoc network due to its various advantages over single path routing. Some of these advantages are load balancing, bandwidth aggregation, and fault tolerance. Multipath routing means multiple paths exist between source and destination pair. Many works discussed in section 2 addressed queuing delays, but none of them suggested queuing delay for multiple path deliveries of data in mobile ad hoc network context. In this paper, we have designed a mathematical model to compute delay and throughput for multipath. Our model follow the network of M/M/1 queues, and we have applied Burke’s theorem to calculate the queuing delay of the packet in mobile network scenario. This model can be used to estimate delay and throughput of an individual path. Further, through the analysis the best path for data delivery out of available multiple paths as well as the multipath path can be used simultaneously for data delivery to the destination. Simulation result shows that splitted traffic multiple paths outperform splitted traffic. Therefore, our model is useful for design and analysis of ad hoc network. The simulation work has been carried out in Qualnet simulator.

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References

  1. Mueller, S., Tsang, R. P., & Ghosal, D. (2003). Multipath routing in mobile ad hoc networks: Issues and challenges. In Proceedings of 11th international symposium modeling, analysis and simulation of computer and telecomm. Systems tutorials (MASCOTS’03), 2003 (pp. 209–234).

  2. Burke, P. J. (1956). The output of a queuing system. Operations Research, 4(6), 699–704.

    Article  MathSciNet  Google Scholar 

  3. Stallings, W. Queuing analysis. Last Accessed September, 2014, http://www.cosc.brocku.ca/Offerings/3P96/notes/QueuingAnalysis.pdf.

  4. Chee-Hock, N., & Boon-Hee, S. (2008). Queueing modelling fundamentals: With applications in communication networks, 2nd edn. Hoboken: Wiley. ISBN 978-0-470-51957-8.

  5. Jackson, R. R. P. (1954). Queueing systems with phase type service. Operational Research Quarterly, 5, 109–120. doi:10.2307/3007088.

    Article  Google Scholar 

  6. Cho, W., Kim, D., Kim, T., & Kim, T. (2011). Time delay on-demand multipath routing protocol in mobile ad-hoc networks. In Third international conference on ubiquitous and future networks (ICUFN), June, 2011 (pp. 55–60).

  7. Han, Y., & Makowski, A. (2006). Resequencing delays under multipath routing—Asymptotics in a simple queueing model. In Proceedings of 25th IEEE international conference on computer communications, Barcelona, 2006 (pp. 1–12).

  8. Bisnik, N., & Abouzeid, A. A. (2006). Queuing delay and achievable throughput in random access wireless ad hoc networks. 3rd annual IEEE communications society on sensor and ad hoc communications and networks, 28 September 2006, SECON’06 (Vol. 3, pp. 874–880). doi:10.1109/SAHCN.2006.288575.

  9. Li, P., Fang, Y., Li, J., & Huang, X. (2012). Smooth trade-offs between throughput and delay in mobile ad hoc networks. IEEE Transactions on Mobile Computing, 11(3), 427–438.

    Article  Google Scholar 

  10. Das, D., & Abouzeid, A. A. (2014). Delay analysis of multihop cognitive radio networks using network of virtual priority queues. In IEEE wireless communications and networking conference (WCNC), (to appear), Istanbul, Turkey, April 7–9, 2014.

  11. Qiu, T., Xia, F., Feng, L., Wu, G., & Jin, B. (2011). Queueing theory-based path delay analysis of wireless sensor networks. International Journal on Advances in Electrical and Computer Engineering, 11(2), 3–8.

    Article  Google Scholar 

  12. Ping, L., & Peiyan, Y. (2010). An approach to calculate queue delay in mobile ad hoc networks. In Proceeding of IEEE international conference of information science and management engineering (ISME), Xi’an, China, 2010 (Vol. 2, pp. 190–192).

  13. Zeng, Y., et al. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.

    Article  Google Scholar 

  14. Wang, X., et al. (2012). A survey of green mobile networks: Opportunities and challenges. MONET, 17(1), 4–20.

    Google Scholar 

  15. Li, P., et al. (2012). CodePipe: An opportunistic feeding and routing protocol for reliable multicast with pipelined network coding. INFOCOM, 2012, 100–108.

    Google Scholar 

  16. Li, P., et al. (2014). Reliable multicast with pipelined network coding using opportunistic feeding and routing. IEEE Transactions on Parallel and Distributed Systems, 25(12), 3264–3273.

    Article  Google Scholar 

  17. Song, Y., et al. (2014). A biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Transactions on Network and Service Management, 11(3), 417–430.

    Article  Google Scholar 

  18. Liu, L., et al. (2015). Physarum optimization: A biology-inspired algorithm for the steiner tree problem in networks. IEEE Transactions on Computers, 64(3), 819–832.

    MathSciNet  Google Scholar 

  19. Liu, Y., et al. (2010). Multi-layer clustering routing algorithm for wireless vehicular sensor networks. IET Communications, 4(7), 810–816.

    Article  Google Scholar 

  20. Busch, C., et al. (2012). Approximating congestion + dilation in networks via “Quality of Routing” games. IEEE Transactions on Computers, 61(9), 1270–1283.

    Article  MathSciNet  Google Scholar 

  21. Meng, T., et al. (2015). Spatial reusability-aware routing in multi-hop wireless networks. IEEE TMC. doi:10.1109/TC.2015.2417543.

    Google Scholar 

  22. Dvir, A., et al. (2011). Backpressure-based routing protocol for DTNs. ACM SIGCOMM Computer Communication Review, 41(4), 405–406.

    MathSciNet  Google Scholar 

  23. Yen, Y.-S., et al. (2011). Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs. Mathematical and Computer Modelling, 53(11–12), 2238–2250.

    Article  Google Scholar 

  24. Spyropoulos, T., et al. (2010). Routing for disruption tolerant networks: Taxonomy and design. Wireless Networks, 16(8), 2349–2370.

    Article  Google Scholar 

  25. Vasilakos, A., et al. (2012). Delay tolerant networks: Protocols and applications. Boca Raton: CRC Press.

    Google Scholar 

  26. Youssef, M., et al. (2014). Routing metrics of cognitive radio networks: A survey. IEEE Communications Surveys and Tutorials, 16(1), 92–109.

    Article  Google Scholar 

  27. Woungang, I., et al. (2013). Routing in opportunistic networks. Berlin: Springer.

    Book  MATH  Google Scholar 

  28. Zhang, X. M., et al. (2015). Interference-based topology control algorithm for delay-constrained mobile Ad hoc networks. IEEE Transactions on Mobile Computing, 14(4), 742–754.

    Article  Google Scholar 

  29. Duarte, P. B. F., et al. (2012). On the partially overlapped channel assignment on wireless mesh network backbone: A game theoretic approach. IEEE Journal on Selected Areas in Communications, 30(1), 119–127.

    Article  Google Scholar 

  30. Attar, A., et al. (2012). A survey of security challenges in cognitive radio networks: Solutions and future research directions. Proceedings of the IEEE, 100(12), 3172–3186.

    Article  Google Scholar 

  31. Marwaha, S., et al. (2004). Evolutionary fuzzy multi-objective routing for wireless mobile ad hoc networks. Evolutionary computation, 2004, CEC2004. Congress on 2, 1964–1971.

  32. Vasilakos, A., et al. (2003). Optimizing QoS routing in hierarchical ATM networks using computational intelligence techniques. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE, 33, 297.

    Article  Google Scholar 

  33. Quan, W., et al. (2014). TB2F: Tree-bitmap and bloom-filter for a scalable and efficient name lookup in content-centric networking. IFIP networking, 2014.

  34. Vasilakos, A. V., et al. (2015). Information centric network: Research challenges and opportunities. Journal of Network and Computer Applications, 52, 1–10.

    Article  Google Scholar 

  35. Yao, Y., et al. (2013). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. In MASS (pp. 182–190).

  36. Mir, N. F. (2006). Computer and communication networks. Chapter 11, Section 11.6, Networks of Queues.

  37. Jain, R. (1991). The art of computer systems performance analysis: Techniques for experimental design, measurement, simulation, and modelling. New York, NY: Wiley-Interscience. ISBN 0471503361.

    MATH  Google Scholar 

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Correspondence to Indrani Das.

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Das, I., Lobiyal, D.K. & Katti, C.P. Multipath routing in mobile ad hoc network with probabilistic splitting of traffic. Wireless Netw 22, 2287–2298 (2016). https://doi.org/10.1007/s11276-015-1093-y

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