Skip to main content
SpringerLink
Log in

TCP-Gvegas with prediction and adaptation in multi-hop ad hoc networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

TCP Vegas performance can be improved since its rate-based congestion control mechanism could proactively avoid possible congestion and packet losses in multi-hop ad hoc networks. Nevertheless, Vegas cannot make full advantage of available bandwidth to transmit packets since incorrect bandwidth estimates may occur due to frequent topology changes caused by node mobility. This paper proposes an improved TCP Vegas based on the grey prediction theory, named TCP-Gvegas, for multi-hop ad hoc networks, which has the capability of prediction and self-adaption, as well as three enhanced aspects in the phase of congestion avoidance. The lower layers’ parameters are considered in the throughput model to improve the accuracy of theoretical throughput. The prediction of future throughput based on grey prediction is used to promote the online control. The optimal exploration method based on Q-Learning and Round Trip Time quantizer are applied to search for the more reasonable changing size of congestion window. Besides, the convergence analysis of grey prediction by using the Lyapunov’s second method proves that a shorter input data length of prediction implies a faster convergence rate. The simulation results show that the TCP-Gvegas achieves a substantially higher throughput and lower delay than Vegas in multi-hop ad hoc networks.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Cai, Y., Jiang, S., Guan, Q., et al. (2013). Decoupling congestion control from TCP (semi-TCP) for multi-hop wireless networks. EURASIP Journal on Wireless Communications and Networking, 1, 1–14.

    Google Scholar 

  2. Al-Jubari, A. M., Othman, M., Ali, B. M., et al. (2013). An adaptive delayed acknowledgment strategy to improve TCP performance in multi-hop wireless networks. Wireless Personal Communications, 69(1), 307–333.

    Article  Google Scholar 

  3. Majeed, A., Abu-Ghazaleh, N. B., Razak, S., et al. (2012). Analysis of TCP performance on multi-hop wireless networks: A cross layer approach. Ad Hoc Networks, 10(3), 586–603.

    Article  Google Scholar 

  4. Brakmo, L. S., O’Malley, S. W., & Peterson, L. L. (1994). TCP Vegas: New techniques for congestion detection and avoidance. In Proceedings of the conference on communications architectures, protocols and applications (ACM SIGCOMM) (pp. 24–35).

  5. Ding, L., Wang, X., Xn, Y., et al. (2008). Improve throughput of TCP-Vegas in multi-hop ad hoc networks. Computer Communications, 31(10), 2581–2588.

    Article  Google Scholar 

  6. Cheng, R. S., Deng, D. J., Chao, H. (2011). Congestion control with dynamic threshold adaptation and cross-layer response for TCP over IEEE 802.11 wireless networks. In Proceedings of the international conference on wireless information networks and systems (WINSYS) (pp. 95–100).

  7. Yuan, Z., Venkataraman, H. M., Muntean, G. M. (2012). A novel bandwidth estimation algorithm for IEEE 802.11 TCP data transmissions. 2012 Wireless Vehicular Communications and Networks (WCNC) (pp. 377–382).

  8. Guan, Q., Yu, F. R., Jiang, S., et al. (2010). Prediction-based topology control and routing in cognitive radio mobile ad hoc networks. IEEE Transactions on Vehicular Technology, 59(9), 4443–4452.

    Article  Google Scholar 

  9. Ghaffari, A. (2015). Congestion control mechanisms in wireless sensor networks: A survey. Journal of Network and Computer Applications, 52, 101–115.

    Article  Google Scholar 

  10. Samios, C. B., & Vernon, M. K. (2003). Modeling the throughput of TCP Vegas. ACM SIGMETRICS Performance Evaluation Review, 31(1), 71–81.

    Article  Google Scholar 

  11. Parvez, N., Mahanti, A., & Williamson, C. (2010). An analytic throughput model of TCP Newreno. IEEE/ACM Transactions on Networking, 18(2), 448–461.

    Article  Google Scholar 

  12. Xie, H., Boukerche, A., De Grande, R., et al. (2015). Towards a distributed TCP improvement through individual contention control in wireless networks. IEEE International Conference on Communications (ICC), 2015, 5602–5607.

    Google Scholar 

  13. Fu, B., Xiao, Y., Deng, H., et al. (2014). A survey of cross-layer designs in wireless networks. IEEE Communications Surveys and Tutorials, 16(1), 110–126.

    Article  Google Scholar 

  14. Al Islam, A. B. M. A., & Raghunathan, V. (2015). ITCP: An intelligent TCP with neural network based end-to-end congestion control for ad-hoc multi-hop wireless mesh networks. Wireless Networks, 21(2), 581–610.

    Article  Google Scholar 

  15. Badarla, V., & Murthy, C. S. R. (2011). Learning-TCP: A stochastic approach for efficient update in TCP congestion window in ad hoc wireless networks. Journal of Parallel and Distributed Computing, 71(6), 863–878.

    Article  Google Scholar 

  16. Xie, H., Pazzi, R. W., & Boukerche, A. (2012). A novel cross layer TCP optimization protocol over wireless networks by Markov Decision Process. IEEE Global Communications Conference (GLOBECOM), 2012, 5723–5728.

    Google Scholar 

  17. Xie, H., & Boukerche, A. (2015). TCP-CC: Cross-layer TCP pacing protocol by contention control on wireless networks. Wireless Networks, 21(4), 1061–1078.

    Article  Google Scholar 

  18. Mezzavilla, M., Quer, G., & Zorzi, M. (2014). On the effects of cognitive mobility prediction in wireless multi-hop ad hoc networks. IEEE International Conference on Communications (ICC), 2014, 1638–1644.

    Google Scholar 

  19. Wang, J., Dong, P., Chen, J., et al. (2013). Adaptive explicit congestion control based on bandwidth estimation for high bandwidth-delay product networks. Computer Communications, 36(10), 1235–1244.

    Article  Google Scholar 

  20. Liu, S., Forrest, J., & Yang, Y. (2012). A brief introduction to grey systems theory. Grey Systems: Theory and Application, 2(2), 89–104.

    Article  Google Scholar 

  21. Xie, N., & Liu, S. (2009). Discrete grey forecasting model and its optimization. Applied Mathematical Modelling, 33(2), 1173–1186.

    Article  MathSciNet  MATH  Google Scholar 

  22. Kayacan, E., Ulutas, B., & Kaynak, O. (2010). Grey system theory-based models in time series prediction. Expert Systems with Applications, 37(2), 1784–1789.

    Article  Google Scholar 

  23. Chen, C. I., & Huang, S. J. (2013). The necessary and sufficient condition for GM(1,1) grey prediction model. Applied Mathematics and Computation, 219(11), 6152–6162.

    Article  MathSciNet  MATH  Google Scholar 

  24. Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237–285.

    Google Scholar 

  25. Marco, M., Lorenza, G., Michele, R., et al. (2015). Distributed Q-learning for energy harvesting heterogeneous networks. IEEE International Conference on Communications (ICC), 2015, 2006–2011.

    Google Scholar 

  26. Alonso-Zárate, J., Crespo, C., Skianis, C., et al. (2012). Distributed point coordination function for IEEE 802.11 wireless ad hoc networks. Ad Hoc Networks, 10(3), 536–551.

    Article  Google Scholar 

  27. Paunonen, L., & Zwart, H. (2013). A Lyapunov approach to strong stability of semigroups. System and Control Letters, 62(8), 673–678.

    Article  MathSciNet  MATH  Google Scholar 

  28. Yau, K. L. A., Komisarczuk, P., & Teal, P. D. (2012). Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues. Journal of Network and Computer Applications, 35(1), 253–267.

    Article  Google Scholar 

  29. Shiang, H. P., & Van der Schaar, M. (2010). Online learning in autonomic multi-hop wireless networks for transmitting mission-critical applications. IEEE Journal on Selected Areas in Communications, 28(5), 728–741.

    Article  Google Scholar 

  30. Xu, X., Liu, C., & Hu, D. (2011). Continuous-action reinforcement learning with fast policy search and adaptive basis function selection. Soft Computing, 15(6), 1055–1070.

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by National Nature Science Foundation of China under Grant 61379005, State Administration of Science, Technology and Industry for National Defence under Grant B3120133002, China Academy of Engineering  Physics(CAEP) project under Grant 2015A0403002, and the Robot Technology Used for Special Environment Key Laboratory of Sichuan Province under Grant 13zxtk07. The authors would like to thank the editor and anonymous reviewers for their thorough review and valuable comments and suggestions to improve the quality of the paper.

Author information

Authors and Affiliations

  1. School of Information, South West University of Science and Technology, Mianyang, China

    Hong Jiang, Ying Luo, QiuYun Zhang & Chun Wu

  2. Institute of Computer Application, China Academy of Engineering Physics, Mianyang, China

    MingYong Yin

Authors
  1. Hong Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ying Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. QiuYun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. MingYong Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Jiang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, H., Luo, Y., Zhang, Q. et al. TCP-Gvegas with prediction and adaptation in multi-hop ad hoc networks. Wireless Netw 23, 1535–1548 (2017). https://doi.org/10.1007/s11276-016-1242-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-016-1242-y

Keywords

Search

Navigation