Evaluation of Packet Transfer Delivery Timeouts During Packet Transmission Using the DA-ARQ (Delay-Aware Automatic Repeat Request) Methodology

Abstract

DA-ARQ is adaptive and dynamic; it can change the rate of retransmission in dependence on receiver feedback and network state. This might result in additional reliable retransmissions with increased efficiency and reduced latency. DA-ARQ adjusts its timeout duration and retransmission rate utilizing feedback mechanisms like ACKs and NACKs. This allows it to take into account the receiver's response and alter the retransmission rate based on the network's current status. DA-ARQ can reduce latency by reducing the time required to retransmit dropped packets. This may result in the quick retransmission of dropped packets by varying the retransmission rate based on network conditions. DA-ARQ can increase reliability by minimizing the number of lost packets and the impact of packet loss on network efficiency. This takes place by adjusting the network environment's retransmission rate, which may result in greater effectiveness and dependability of retransmissions. DA-ARQ can enhance network performance by altering the retransmission rate based on network conditions. This can increase reliability, reduce latency, and reduce packet loss, all of which will enhance overall network performance. The DA-ARQ (Dynamic Adaptive Automatic Repeat Request) packet timeout analysis method is employed to maximize the rate at which lost or damaged packets are transmitted again in a networked environment. It changes the timeout value and resend rate based on feedback received from the receiver and the status of the network. DA-ARQ (Delay-Aware Automatic Repeat Request) is a network communication technology that ensures reliable data transfer. It entails retransmitting lost or damaged packets to ensure they are received successfully. Machine learning techniques may be incorporated into DA-ARQ to improve networking packet analysis. To train the model, several methods, such as decision trees, random forests, or SVM, can be used. The algorithm discovers patterns and correlations between various variables in addition to the probability of successful packet delivery. By integrating DA-ARQ with machine learning, network packet transfer analysis may be enhanced, resulting in increased data transmission accuracy and effectiveness.