A Review on Machine Learning based Loss Discrimination Algorithm for Wireless TCP Congestion Control

Authors

  • Pallavi Bhomle  M.Tech Scholar, Department of Computer Science & Engineering, JD College Of Engineering and Management, Nagpur, Maharashtra, India
  • Dr. Shrikant V. Sonekar  Assistant Professor, Department of Computer Science & Engineering, JD College Of Engineering And Management, Nagpur, Maharashtra, India

Keywords:

Wireless TCP, Congestion Control, Machine Learning, TCP Congestion Control, LDA.

Abstract

In the incorporated condition of wired and wireless networks, different issues can bring about bundle misfortunes, for example, congestion and wireless parcel misfortune. The current TCP senders treat the parcel misfortunes as the loss of bundles in the transmission line which are brought about by network congestion, coming about to the TCP execution corruption. In this paper, the network congestion and wireless parcel misfortune are segregated dependent on machine learning algorithms with one concealed layer. In the event that the evaluated outcome is chosen as bundle misfortune from congestion, at that point congestion window and ssthresh is diminished to half, in any case, those qualities are kept in the event that it is chosen as a wireless mistake. The proposed algorithm utilizes essential TCP NewReno, yet if there should arise an occurrence of bundle misfortune, it adjusts congestion control plans dependent on the pre-learned machine-learning algorithm. The reproduction results show the improved TCP execution as contrasted and different existing TCP algorithms.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-08-30

Issue

Volume 7, Issue 4, July-August-2020

Section

Research Articles

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How to Cite

[1]
Pallavi Bhomle, Dr. Shrikant V. Sonekar "A Review on Machine Learning based Loss Discrimination Algorithm for Wireless TCP Congestion Control" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 4, pp.33-38, July-August-2020.