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Cross-layer based congestion detection and routing protocol using fuzzy logic for MANET

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An Erratum to this article was published on 26 April 2016

Abstract

Mobile Ad hoc Networks (MANET) is made up of mobile devices which form an infrastructure-less network. In MANET, the devices can change its location and configure itself at any time. Hence, there is a possibility for the network to be prone to abnormal network events due to certain factors like error in the links, overflow in the buffers, layers, and so forth. So, in this paper, a cross-layer based congestion detection and routing protocol is proposed using Fuzzy logic. In this protocol, whenever a network event occurs, the kind of event occurring is recognized in order to handle it accordingly. Next, the alternate routes for data transmission are determined by applying the concept of fuzzy logic on some of the critical factors. Based on the fuzzy inference rules, appropriate routes are selected and data messages are transmitted successfully.

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References

  1. Jehan, M., & G. Radhamani, Scalable TCP: Better throughput in TCP congestion control algorithms on MANETs. International Journal of Advanced Computer Science and Applications Special Issue on Wireless and Mobile Networks.

  2. Geetika, M., Gour, M., & Chourasia, U. K. (2014). A survey on congestion control in MANET. International Journal of Computer Science and Information Technologies 5 (2).

  3. Srinivas, K., & Chari, A. A. (2011). Cross layer congestion control in MANETs and current state of art. International Journal of Computer Applications, 29(6), 28–35.

    Article  Google Scholar 

  4. Sheeja, S., & Pujeri, R. V. (2013). Cross layer based congestion control scheme for mobile ad hoc networks. International Journal of Computer Applications, 67(9), 60–67.

    Article  Google Scholar 

  5. Senthilkumaran T & Sankaranarayanan V (2011) Early congestion detection and optimal control routing in manet. European Journal of Scientific Research, 63(1): 15–31, ISSN: 1450-216X.

  6. Nidhi, S., Gupta, N., & Parveen, N. (2014). Survey of cross layer based TCP congestion control techniques in MANET. International Journal of Emerging Technology and Advanced Engineering, 4(3), ISSN: 2250-2459

  7. Sreenivasa, B. C., BhanuPrakash, G. C., & Ramakrishnan, K. V. (2011). A survey on congestion control techniques in AD-HOC network. Elixir Adoc Networks, 32, 2061–2067.

    Google Scholar 

  8. Appaji, V. V., & Sreedhar, Dr. (2012). CPCRT: Crosslayered and power conserved routing topology for congestion control in mobile ad hoc networks. IOSR Journal of Computer Engineering (IOSRJCE), 3(5): 17–25, ISSN: 2278-0661.

  9. Natarajan, E., & Devi, L. (2014). Cross layer based energy aware routing and congestion control algorithm in MANET. International Journal of Computer Science and Mobile Computing, 3(10), 700–709.

    Google Scholar 

  10. Thilagavathe, V., & Duraiswamy, Dr. K. (2011). Cross layer based congestion control technique for reliable and energy aware routing in MANET. International Journal of Computer Applications, 36(12): 1–6, ISSN: 0975–8887.

  11. Lin, X., & Shroff, N. B. (2006). The impact of imperfect scheduling on cross-layer congestion control in wireless networks. IEEE/ACM Transactions onNetworking, 14(2), 302–315.

    Article  Google Scholar 

  12. Sunitha, D., Nagaraju, A., & Narsimha, G. (2014). A cross-layer approach for congestion control in multi hop mobile ad hoc networks. In Proceedings of international conference on computing for sustainable global development (INDIACom), pp. 54–60. IEEE.

  13. Chang, H.-P., Kan, H.-W., & Ho, M.-H. (2012). Adaptive TCP congestion control and routing schemes using cross-layer information for mobile ad hoc networks. Computer Communications, 35(4), 454–474.

    Article  Google Scholar 

  14. Zhao, W., Huang, X., Shi, K., & Zhang, L. (2013). TSBCC: Time series-based congestion control algorithm for wireless network. Journal of Networks, 8(5), 1058–1064.

    Google Scholar 

  15. Tabash, I. K., Ahmad, N., & Beg, S. (2011). A congestion window control mechanism based on fuzzy logic to improve tcp performance in manets. In Proceedings of international conference on computational intelligence and communication networks (CICN), IEEE (pp. 21–26).

  16. Douga, Y., & Bourenane, M. (2013). A cross layer solution to improve TCP performances in Ad Hoc wireless networks. In Proceedings of international conference on smart communications in network technologies (SaCoNeT), vol. 1, pp. 1–5. IEEE.

  17. Rath, H. K., Rajan, M. A. & Balamuralidhar, P. (2011). Monotonic signed graph approach for cross-layer congestion control in wireless ad-hoc networks. In GLOBECOM workshops (GC Wkshps) (pp. 309–314). IEEE.

  18. Pushpender & Garg, S. (2014). New routing technique based on fuzzy and network dependent for wireless mesh networks. International Journal of Modern Electronics and Communication Engineering (IJMECE), 2(1), ISSN: 2321-2152.

  19. Anuradha, M. & Anandha Mala G. S. (2014). Multi-objective cross-layer based multipath routing protocol in manet. Journal of Theoretical and Applied Information Technology, 68(3), 531–540.

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    MathSciNet  Google Scholar 

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

  23. 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 

  24. 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  MATH  Google Scholar 

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

    Article  Google Scholar 

  26. Busch, C., et al. (2012). approximating congestion + dilation in networks via “quality of routing” games. IEEE Transaction Computers, 61(9), 1270–1283.

    Article  MathSciNet  Google Scholar 

  27. 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 

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

    MATH  Google Scholar 

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

    MathSciNet  Google Scholar 

  30. 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 

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

    Article  Google Scholar 

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

    Google Scholar 

  33. 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 

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

    Book  MATH  Google Scholar 

  35. 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  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  37. 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 

  38. 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).

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

  40. Vasilakos, A. et al. (2003). Optimizing QoS routing in hierarchical ATM networks using computational intelligence techniques. In Systems, man, and cybernetics, part C: applications and reviews, IEEE, 2003

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

  42. Acampora, G., et al. (2010). Interoperable and adaptive fuzzy services for ambient intelligence applications. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 5(2), 8.

    Google Scholar 

  43. Reza Rahimi, M., et al. (2014). Mobile cloud computing: A survey, state of art and future directions. MONET, 19(2), 133–143.

    Google Scholar 

  44. Xiang, L., et al. (2011) Compressed data aggregation for energy efficient wireless sensor networks. SECON 46–54.

  45. Yang, M., et al. (2015). Software-defined and virtualized future mobile and wireless networks: A survey. ACM/Springer Mobile Networks and Applications, 20(1), 4–18.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Easwari Engineering College, Chennai, India

    M. Anuradha & G. S. Anandha Mala

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  1. M. Anuradha
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  2. G. S. Anandha Mala
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Correspondence to M. Anuradha.

Additional information

An erratum to this article is available at http://dx.doi.org/10.1007/s11276-016-1260-9.

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Anuradha, M., Anandha Mala, G.S. Cross-layer based congestion detection and routing protocol using fuzzy logic for MANET. Wireless Netw 23, 1373–1385 (2017). https://doi.org/10.1007/s11276-016-1211-5

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