Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Recent Advances in Computer Science and Communications
  4. Fast Track Articles
  5. Fast Track Article
image of Comparative Analysis of Different Transport Layer Protocol Techniques Incognitive Network

Abstract

Most of the networks employ TCP protocol for transmission control in transport mechanism. Although it offers numerous services such as reliability, end-to-end delivery, secure transmission of data, and so on to applications functioning across the World Wide Web, TCP needs to have effective congestion management techniques in order to handle traffic with a lot of data. Still, TCP have poor performance during data transmission in the network. The network community continues conducting research to develop a method that should provide a fair and effective transmission bandwidth distribution. Numerous congestion control strategies have been developed based on previous research in this area. This work discusses, identifies, compares, and analyses the behaviour of a few network congestion control strategies to determine their benefits and limitations. The widely known network simulator ns2 is employed for the simulation. The performance metrics for QTCP, TCP new Reno, TCP- Hybla, L-TCP and RL-TCP are throughput, average delay, PDR, packet loss, average jitter, latency and fairness are considered. The RL-TCP exhibits superior performance in multiple measures when it is compared to alternative TCP protocols, as indicated by simulation results. These metrics encompass throughput, average delay, packet delivery ratio (PDR), packet loss, jitter, latency, and fairness. Furthermore, several TCP protocols, such as L-TCP, TCP-Hybla, QTCP, and TCP-New Reno, have undergone evaluation, uncovering disparities in their individual performance attributes. Nevertheless, the RL-TCP regularly demonstrates superior performance in all aspects.

© 2024 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/rascs/10.2174/0126662558301809240923075109
2024-10-09
2024-11-26

  • From This Site

    /content/journals/rascs/10.2174/0126662558301809240923075109
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Lorincz J. Klarin Z. Ožegović J. A comprehensive overview of TCP congestion control in 5G networks: Research challenges and future perspectives. Sensors 2021 21 13 4510 10.3390/s21134510 34209431
    [Google Scholar]
  2. Mishra A. Sun X. Jain A. Pande S. Joshi R. Leong B. "The great internet TCP congestion control census", Proc. ACM Meas. Anal. Comput. Syst. 2019 3 3 1 24 10.1145/3366693
    [Google Scholar]
  3. Kanagarathinam M.R. Singh S. Sandeep I. Kim H. Maheshwari M.K. Hwang J. Roy A. Saxena N. NexGen D-TCP: Next generation dynamic TCP congestion control algorithm. IEEE Access 2020 8 164482 164496 10.1109/ACCESS.2020.3022284
    [Google Scholar]
  4. Wei W. Xue K. Han J. Wei D.S.L. Hong P. Shared bottleneck-based congestion control and packet scheduling for multipath TCP. IEEE/ACM Trans. Netw. 2020 28 2 653 666 10.1109/TNET.2020.2970032
    [Google Scholar]
  5. Lubna T. Mahmud I. Cho Y.Z. D-LIA: Dynamic congestion control algorithm for MPTCP. ICT Express 2020 6 4 263 268 10.1016/j.icte.2020.03.005
    [Google Scholar]
  6. Prakash M. Abdrabou A. Zhuang W. An experimental study on multipath TCP congestion control with heterogeneous radio access technologies. IEEE Access 2019 7 25563 25574 10.1109/ACCESS.2019.2900290
    [Google Scholar]
  7. Lin J. Cui L. Zhang Y. Tso F.P. Guan Q. Extensive evaluation on the performance and behaviour of TCP congestion control protocols under varied network scenarios. Comput. Netw. 2019 163 106872 10.1016/j.comnet.2019.106872
    [Google Scholar]
  8. Pan W. Tan H. Li X. Li X. Improved RTT fairness of BBR congestion control algorithm based on adaptive congestion window. Electronics 2021 10 5 615 10.3390/electronics10050615
    [Google Scholar]
  9. Wang Y. Wang L. Dong X. An intelligent TCP congestion control method based on deep Q network. Future Internet 2021 13 10 261 10.3390/fi13100261
    [Google Scholar]
  10. Huang H. Sun Z. Wang X. End-to-end TCP congestion control for mobile applications. IEEE Access 2020 8 171628 171642 10.1109/ACCESS.2020.3024707
    [Google Scholar]
  11. Saedi T. El-Ocla H. TCP CERL+: Revisiting TCP congestion control in wireless networks with random loss. Wirel. Netw. 2021 27 1 423 440 10.1007/s11276‑020‑02459‑0
    [Google Scholar]
  12. Haile H. Grinnemo K.J. Ferlin S. Hurtig P. Brunstrom A. End-to-end congestion control approaches for high throughput and low delay in 4G/5G cellular networks. Comput. Netw. 2021 186 107692 10.1016/j.comnet.2020.107692
    [Google Scholar]
  13. ALBASRAWI S. T. A. Improve the performance of transport layer of tcp protocol in wireless sensor network. J. Theor. Appl. Inf. Technol. 2021 99 6
    [Google Scholar]
  14. Tuli E.A. Golam M. Kim D.S. Lee J.M. Performance enhancement of optimized link state routing protocol by parameter configuration for UANET. Drones 2022 6 1 22 10.3390/drones6010022
    [Google Scholar]
  15. Ghaleb S.A.M. Vasanthi V. Energy efficient multipath routing using multi-objective grey wolf optimizer based dynamic source routing algorithm for manet. Int. J. Adv. Sci. Technol. 2020 29 3 6096 6117
    [Google Scholar]
  16. Mishra A. Performance analysis of TCP Tahoe, Reno and New Reno for scalable IoT network clusters in QualNet® network simulator. Int. J. Comput. Sci. Eng. 2018 6 8 347 355 10.26438/ijcse/v6i8.347355
    [Google Scholar]
  17. Gambhava B. Bhensdadia C.K. Mathematical modelling of packet transmission during reclamation period in NewReno TCP and CTCP. Int. J. Internet Protoc. Technol. 2023 16 2 110 118 10.1504/IJIPT.2023.131296
    [Google Scholar]
  18. Chowdhury T. Alam M.J. Performance evaluation of TCP Vegas over TCP Reno and TCP NewReno over TCP Reno. JOIV : Int. J. Inf. Visual. 2019 3 3 275 282 10.30630/joiv.3.3.270
    [Google Scholar]
  19. Sacco A. Flocco M. Esposito F. Marchetto G. "Owl: Congestion control with partially invisible networks via reinforcement learning", IEEE INFOCOM 2021 - IEEE Conference on Computer Communications , Vancouver, BC, Canada, 10 May 2021, 1–10. 2021 10.1109/INFOCOM42981.2021.9488736
    [Google Scholar]
  20. Li W. Zhou F. Chowdhury K.R. Meleis W. QTCP: Adaptive congestion control with reinforcement learning. IEEE Trans. Netw. Sci. Eng. 2019 6 3 445 458 10.1109/TNSE.2018.2835758
    [Google Scholar]
  21. Li W. Zhang H. Gao S. Xue C. Wang X. Lu S. SmartCC: A reinforcement learning approach for multipath TCP congestion control in heterogeneous networks. IEEE J. Sel. Areas Comm. 2019 37 11 2621 2633 10.1109/JSAC.2019.2933761
    [Google Scholar]
  22. Zong L. Bai Y. Zhao C. Luo G. Zhang Z. Ma H. On enhancing TCP to deal with high latency and transmission errors in geostationary satellite network for 5G-IoT. Secur. Commun. Netw. 2020 2020 1 7 10.1155/2020/6693094
    [Google Scholar]
  23. Domański A. Domańska J. Czachórski T. Klamka J. Szyguła J. Marek D. Diffusion approximation model of TCP newreno congestion control mechanism. SN Comput. Sci. 2020 1 1 43 10.1007/s42979‑019‑0032‑x
    [Google Scholar]
  24. Le H.D. Trinh P.V. Pham T.V. Kolev D.R. Carrasco-Casado A. Kubo-Oka T. Toyoshima M. Pham A.T. Throughput analysis for TCP over the FSO-based satellite-assisted internet of vehicles. IEEE Trans. Vehicular Technol. 2022 71 2 1875 1890 10.1109/TVT.2021.3131746
    [Google Scholar]
  25. Yamazaki M. Yamamoto M. Fairness improvement of congestion control with reinforcement learning. J. Inf. Process. 2021 29 0 592 595 10.2197/ipsjjip.29.592
    [Google Scholar]
  26. Górski T. The 1+5 architectural views model in designing blockchain and IT system integration solutions. Symmetry 2021 13 11 2000 10.3390/sym13112000
    [Google Scholar]
  27. Górski T. UML profile for messaging patterns in service-oriented architecture, microservices, and internet of things. Appl. Sci. 2022 12 24 12790 10.3390/app122412790
    [Google Scholar]
  28. Pundir M. Sandhu J.K. Kumar A. Quality-of-service prediction techniques for wireless sensor networks. J. Phys. Conf. Ser. 2021 1950 1 012082 10.1088/1742‑6596/1950/1/012082
    [Google Scholar]
  29. Singhal A. Phogat M. Kumar D. Kumar A. Dahiya M. Shrivastava V.K. Study of deep learning techniques for medical image analysis: A review. Mater. Today Proc. 2022 56 209 214 10.1016/j.matpr.2022.01.071
    [Google Scholar]
  30. Rani S. Kumar A. Bagchi A. Yadav S. Kumar S. RPL based routing protocols for load balancing in iot network. J. Phys. Conf. Ser. 2021 1950 1 012073 10.1088/1742‑6596/1950/1/012073
    [Google Scholar]
  31. Shrivastava V.K. Kumar A. Shrivastava A. Tiwari A. Thiru K. Batra R. Study and trend prediction of covid-19 cases in india using deep learning techniques. J. Phys. Conf. Ser. 2021 1950 1 012084 10.1088/1742‑6596/1950/1/012084
    [Google Scholar]
  32. Rani S. Tripathi K. Kumar A. Machine learning aided malware detection for secure and smart manufacturing: A comprehensive analysis of the state of the art. Int J Interact Des Manuf 2023 10.1007/s12008‑023‑01578‑0
    [Google Scholar]
  33. Murali Krishna V.B. Duvvuri S.S.S.R.S. Yadlapati K. Pidikiti T. Sudheer P. Deployment and performance measurement of renewable energy based permanent magnet synchronous generator system. Meas. Sensors 2022 24 100478 100478 10.1016/j.measen.2022.100478
    [Google Scholar]
/content/journals/rascs/10.2174/0126662558301809240923075109
Loading
Comparative Analysis of Different Transport Layer Protocol Techniques Incognitive Network
Bentham Science Publishers ; https://doi.org/10.2174/0126662558301809240923075109
/content/journals/rascs/10.2174/0126662558301809240923075109
/content/journals/rascs/10.2174/0126662558301809240923075109
Loading

Data & Media loading...


  • Article Type:     Review Article
Keywords: PDR ; TCP new Reno ; RTT ; TCP- Hybla ; L-TCP ; RL-TCP ; PL ; QTCP ; Throughput

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test