Wireless Personal Communications

, Volume 109, Issue 2, pp 1395–1418 | Cite as

Analysis of Traffic Impact on Proposed Congestion Control Scheme in AODV

  • Navneet KaurEmail author
  • Rakesh Singhai


Congestion is a noteworthy problem in Mobile Ad hoc Network, which occurs when number of users increases and a node carries data more than its capacity. In this paper, a congestion control mechanism is suggested for the performance improvement of basic routing protocol AODV. The congestion status of a node is valuated based on parameters node queue length, channel utilization and residual node energy using fuzzy logic control. To strengthen the congestion control, an adaptive network coding mechanism is employed in case of high congestion to minimize transmissions amongst nodes. Recovery of data flow with neighbouring non-congested node is performed in case of medium congestion to avoid condition of high congestion and to reduce recovery time. Modifications are made to the AODV protocol in order to develop congestion control scheme without any additional overhead. The proposed methods are tested for different terrain areas with change in number of nodes and traffic load. The two variants of proposed protocol are simulated on Network simulator and impact of traffic on proposed schemes is investigated. The results are compared with basic AODV protocol. From simulation results, it is evident that adaptive network coding and congested link repairing method has improved scalability performance of routing protocol.


MANET Network coding Scalability Congestion control Congestion status packet CSP AODV 



  1. 1.
    Masri, E., Ali, A. S., Khoukhi, L., Hafid, A., & Gaiti, D. (2014). Neighborhood-aware and overhead-free congestion control for IEEE 802.11 wireless mesh networks. IEEE Transactions on Wireless Communications, 13(10), 5878–5892.CrossRefGoogle Scholar
  2. 2.
    Jyoti, J., Gupta, R., & Bandhopadhyay, T. K. (2014). Scalability enhancement of AODV using local link repairing. International Journal of Electronics, 101(9), 230–1243.Google Scholar
  3. 3.
    Tran, D. A., & Raghavendra, H. (2006) Routing with congestion awareness and adapltivity in mobile adhoc networks. IEEE Transactions on Parallel and Distributed System, 17(11), 1294–1305.CrossRefGoogle Scholar
  4. 4.
    Kim, J. Y., Tomar, G. S., Shrivastava, L., Bhadauria, S. S., & Lee, W. H. (2014). Load balanced congestion adaptive routing for mobile ad hoc networks. International Journal of Distributed Sensor Networks, 10(7), 532043.CrossRefGoogle Scholar
  5. 5.
    Ding, W., Tang, L., & Ji, S. (2016). Optimizing routing based on congestion control for wireless sensor networks. Wireless Networks, 22(3), 915–925.CrossRefGoogle Scholar
  6. 6.
    Li, S., Zhao, S., Wang, X., Zhang, K., & Li, L. (2014). Adaptive and secure load-balancing routing protocol for service-oriented wireless sensor networks. IEEE Systems Journal, 8(3), 858–867.CrossRefGoogle Scholar
  7. 7.
    Amuthan, A., Sreenath, N., Boobalan, P., & Muthuraj, K. (2017). Dynamic multi-stage tandem queue modeling-based congestion adaptive routing for MANET. Alexandria Engineering Journal, 57, 1467.CrossRefGoogle Scholar
  8. 8.
    Kavitha, N. S., & Malathi, P. (2017). Analysis of congestion control based on Engset loss formula-inspired queue model in wireless networks. Computers & Electrical Engineering, 64, 567.CrossRefGoogle Scholar
  9. 9.
    Emdadul, H. M., Tariq, F., Dooley, L., Allen, B., & Sun, Y. (2018). Efficient congestion minimisation by successive load shifting in multilayer wireless networks. Computers & Electrical Engineering, 68, 536–549.CrossRefGoogle Scholar
  10. 10.
    Wang, Y., Wang, W., Cui, Y., Shin, K. G., & Zhang, Z. (2018). Distributed packet forwarding and caching based on stochastic network utility maximization. IEEE/ACM Transactions on Networking, 26, 1264.CrossRefGoogle Scholar
  11. 11.
    Kaji, K., & Yoshihiro, T. (2018). Building detour paths to avoid local congestion in MANETs. Journal of Information Processing, 26, 116–123.CrossRefGoogle Scholar
  12. 12.
    Akhtar, N., Khan Khattak, M. A., Ullah, A., & Javed, M. Y. (2017). Efficient routing strategy for congestion avoidance in MANETs. In 2017 International conference on frontiers of information technology (FIT) (pp. 305–309). IEEE.Google Scholar
  13. 13.
    Wang, R., Tang, Y., & Yan, J. (2016). Congestion control mechanism for intermittently connected wireless network. Mobile Information Systems, 2016, 1–10.Google Scholar
  14. 14.
    Yang, X., Xu, S., & Li, Z. (2017). Consensus congestion control in multirouter networks based on multiagent system. Complexity, 2017, 1–10.MathSciNetzbMATHGoogle Scholar
  15. 15.
    Bassoli, R., Marques, H., Rodriguez, J., Shum, K. W., & Tafazolli, R. (2013). Network coding theory: A survey. IEEE Communications Surveys & Tutorials, 15(4), 1950–1978.CrossRefGoogle Scholar
  16. 16.
    Katti, S., Rahul, H., Hu, W., Katabi, D., Médard, M., & Crowcroft, J. (2008). XORs in the air: Practical wireless network coding. IEEE/ACM Transactions on Networking (ToN), 16(3), 497–510.CrossRefGoogle Scholar
  17. 17.
    Miao, L., Djouani, K., Kurien, A., & Noel, G. (2012). Network coding and competitive approach for gradient based routing in wireless sensor networks. Ad Hoc Networks, 10(6), 990–1008.CrossRefGoogle Scholar
  18. 18.
    Kafaie, S., Chen, Y., Dobre, O. A., & Ahmed, M. H. (2018). Joint inter-flow network coding and opportunistic routing in multi-hop wireless mesh networks: A comprehensive survey. IEEE Communications Surveys & Tutorial, 20(2), 1014–1035.CrossRefGoogle Scholar
  19. 19.
    Qu, Y., Dong, C., Guo, S., Tang, S., Wang, H., & Tian, C. (2017). Spectrum-aware network coded multicast in mobile cognitive radio ad hoc networks. IEEE Transactions on Vehicular Technology, 66(6), 5340–5350.CrossRefGoogle Scholar
  20. 20.
    Lin, K. C.-J., & Yang, D.-N. (2013). Multicast with intraflow network coding in multirate multichannel wireless mesh networks. IEEE Transactions on Vehicular Technology, 62(8), 3913–3927.CrossRefGoogle Scholar
  21. 21.
    Chen, Y.-H., Eric Hsiao-Kuang, W., & Chen, G.-H. (2017). Bandwidth-satisfied multicast by multiple trees and network coding in lossy MANETs. IEEE Systems Journal, 11(2), 1116–1127.CrossRefGoogle Scholar
  22. 22.
    Jin, J., Xu, H., & Li, B. (2010). Multicast scheduling with cooperation and network coding in cognitive radio networks. In INFOCOM, 2010 Proceedings IEEE (pp. 1–9). IEEE.Google Scholar
  23. 23.
    Chen, Y.-H., Eric Hsiao-Kuang, W., Lin, C.-H., & Chen, G.-H. (2018). Bandwidth-satisfied and coding-aware multicast protocol in MANETs. IEEE Transactions on Mobile Computing, 8, 1778–1790.CrossRefGoogle Scholar
  24. 24.
    Xie, X., & Wang, W. (2013). Detecting primary user emulation attacks in cognitive radio networks via physical layer network coding. Procedia Computer Science, 21, 430–435.CrossRefGoogle Scholar
  25. 25.
    Li, C., Yan, Y., & Zhang, B. (2018). Network coding aided collaborative real-time scalable video transmission in D2D communications. IEEE Transactions on Vehicular Technology, 67, 6203.CrossRefGoogle Scholar
  26. 26.
    Yan, Q., Li, M., Yang, Z., Lou, W., & Zhai, H. (2012). Throughput analysis of cooperative mobile content distribution in vehicular network using symbol level network coding. IEEE Journal on Selected Areas in Communications, 30(2), 484–492.CrossRefGoogle Scholar
  27. 27.
    Naseem, M., & Kumar, C. (2015). Congestion-aware Fibonacci sequence based multipath load balancing routing protocol for MANETs. Wireless Personal Communication, 84(4), 2955–2974.CrossRefGoogle Scholar
  28. 28.
    Varaprasad, G. (2012). Stable routing algorithm for mobile ad hoc networks using mobile agent. International Journal of Communication System, 27(1), 163–170.CrossRefGoogle Scholar
  29. 29.
    Sun, Y., Sun, J., Zhao, F., & Hu, Z. (2014). Delay constraint multipath routing for wireless multimedia ad hoc networks. International Journal of Communication System, 29(1), 210–225.CrossRefGoogle Scholar
  30. 30.
    De Rango, F., Guerriero, F., & Fazio, P. (2012). Link-stability and energy aware routing protocol in distributed wireless networks. IEEE Transactions on Parallel and Distributed Systems, 23(4), 713–726.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.UIT, RGPVBhopalIndia

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