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Abstract

In a mixed wireless and wired network environment, packet loss can be caused by various types of due to network congestion as well as wireless errors. However, existing TCP decides that all packet loss is network congestion, which causes congestion control to degrade TCP performance. This project distinguishes network congestions and wireless errors through a deep learning algorithm when a packet loss occurs. In case of loss due to network congestion, congestion control is performed in the same way as existing TCP. In case of loss due to errors, we propose an algorithm that can improve the wireless TCP performance by only retransmitting lost packets without reducing the congestion window. Through the simulation, we show that the proposed algorithm improves the TCP performance by reflecting the result of the pre-learned deep learning algorithm, compared to the "TCP Westwood" or "TCP Veno" proposed for improving the wireless TCP performance by discriminating the congestion and wireless error. In addition, improve the network life time using fixed window protocol and sliding window protocol, each node monitors the forwarding behavior of its neighboring nodes in sliding window mechanism. The node listen the next-hop node with the packets sent at regular intervals. The project quantitatively computes the system configuration parameters for guaranteed performance in terms of average false positive rate, average detection delay, and missed detection ratio under a detection delay constraint.

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How to Cite
K.P.Uvaraja, K.N.Muthu Rathinam, S.Santhosh, S.Shneh, & R.Shyamala. (2019). Network congestion control monitoring system using FWP and SWP with multi hop model . International Journal of Intellectual Advancements and Research in Engineering Computations, 7(1), 1276–1287. Retrieved from https://ijiarec.com/ijiarec/article/view/1120