Abstract
today The topic of contention is the quality of computer network service. Efficient data handling is important. Each router offers a buffer space where packets can be held before being sent until they are processed. The theory of packet congestion control is TCP (Transmission Control Protocol) .(AQM) is a mechanism proposed for gateways to improve congestion management. AQM (active queue management) is a crucial to minimize packet loss in TCP/IP networks and improving network efficiency. This paper offers a hybrid intelligent (PID) with type1 fuzzy logic controller which is designed to decrease network congestion. As a router (AQM), social spider optimization (SSO) is used to tune the control parameters used to reduce queue size error. The designed controller based on SSO algorithm provides a good tracking performance for AQM desired value.The observational findings indicated a high-performance increase in the proposed process. In MATLAB(R2020a), a simulation of a linearized TCP/AQM model is presented. NS2 is used to perform verification for the fuzzy logic controllers for the nonlinear TCP/AQM model under a more practical simulation and to analyze and examine network behavior in different scenarios. Index Terms—AQM controller, congestion control, fuzzy logic control, PID Controller; SSO.
Highlights
One of the most widely used technology is computer networking that can be extended to various areas of life
Spiders have complete freedom of movement on the internet. They cannot, leave the site because off-web positions are insufficient solutions to the optimization problemWhen a spider travels to a new spot, it sends out a pulse that can be felt across the web
Router1 configures the technique of active queue management (AQM) and drop Tail is used in other gateways
Summary
One of the most widely used technology is computer networking that can be extended to various areas of life. Kim Quoc et al, 2015, A paradigm integrating the fuzzy controller with the neural network (FNN) has been proposed This increases the operating performance of the processes of active queue management at network nodes[8]. In 2018, A technique called ANB-AQM adaptive neural (AQM) is suggested,To contend with the nonlinearity of network networks, this strategy the parameters are fine-tuned self-learning-based This helps to estimate the potential value likelihood of packet drop by observing the prior experience of queue length error [12]. Based on a modeling principle of multi-agent systems, a new congestion control The neural networks framework is proposed to explore a class of multiple (TCP/AQM) network finite time consensus. One of the conventional PID control approaches of fuzzy logic for wireless networks and congestion is discussed in this research Diverse configurations can be managed.
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