scholarly journals Grey wolf optimizer based fuzzy-PI active queue management design for network congestion avoidance

2020 ◽  
Vol 18 (1) ◽  
pp. 199 ◽  
Author(s):  
Sana Sabah Sabry ◽  
Nada Mahdi Kaittan
Keyword(s):  
Fuzzy Logic ◽  
Communication Networks ◽  
Control Strategy ◽  
Queue Length ◽  
Active Queue Management ◽  
Linguistic Knowledge ◽  
Grey Wolf Optimizer ◽  
Queue Management ◽  
Grey Wolf ◽  
Research Attention

<p>Congestion is one of the most important issues in communication networks which has attracted much research attention. To ensure a stable TCP network, we can use active queue management (AQM for early congestion detection and router queue length regulation. In this study, it was proposed to use the Grey Wolf Optimizer (GWO) algorithm in designing a fuzzy proportional integral (fuzzy-PI) controller as a novel AQM for internet routers congestion control and for achieving a low steady-state error and fast response. The suggested Fuzzy logic-based network traffic control strategy permit us to deploy linguistic knowledge for depicting the dynamics of probability marking functions and ensures a more accurate use of multiple inputs to depict the   the network’s state. The possibility of incorporating human knowledge into such a control strategy using Fuzzy logic control methodology was demonstrated. The postulated controller was compared to proportion integral (PI) through several MATLAB simulation scenarios. The results indicated the stability of the postulated controller and its ability to attain a faster response in a dynamic network with varying network load and target queue length.</p>

scholarly journals A Fuzzy Logic Based Network Congestion Control Using Active Queue Management Techniques

2010 ◽  
Vol 2 (2) ◽  
pp. 273-284 ◽  
Author(s):  
I. K. Tabash ◽  
M. A. Mamun ◽  
A. Negi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Probability Function ◽  
Active Queue Management ◽  
Buffer Size ◽  
Queue Management ◽  
Passive Devices ◽  
Better Regulation ◽  
Ip Routers ◽  
Intelligent Approach

Conventional IP routers are passive devices that accept packets and perform the routing function on any input. Usually the tail-drop (TD) strategy is used where the input which exceeds the buffer capacity are simply dropped. In active queue management (AQM) methods routers manage their buffers by dropping packets selectively. We study one of the AQM methods called as random exponential marking (REM). We propose an intelligent approach to AQM based on fuzzy logic controller (FLC) to drop packets dynamically, keep the buffer size around desired level and also prevent buffer overflow. Our proposed approach is based on REM algorithm, which drops the packets by drop probability function. In our proposal we replace the drop probability function by a FLC to drop the packets, stabilize the buffer around the desired size and reduce delay. Simulation results show a better regulation of the buffer.  Keywords: Random exponential marking; Active queue management; Fuzzy logic controller; Pro-active queue management. © 2010 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved. DOI: 10.3329/jsr.v2i2.2786               J. Sci. Res. 2 (2), 273-284 (2010) 

Dynamic simultaneous adaptation of parameters in the grey wolf optimizer using fuzzy logic

2017 ◽  
Author(s):  
Luis Rodriguez ◽  
Oscar Castillo ◽  
Mario Garcia ◽  
Jose Soria ◽  
Fevrier Valdez ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Grey Wolf Optimizer ◽  
Grey Wolf

scholarly journals Fuzzy-Logic Based Active Queue Management Using Performance Metrics Mapping into Multi-Congestion Indicators

2021 ◽  
Vol 21 (2) ◽  
pp. 29-44
Author(s):  
Mosleh M. Abualhaj ◽  
Mayy M. Al-Tahrawi ◽  
Abdelrahman H. Hussein ◽  
Sumaya N. Al-Khatib
Keyword(s):  
Fuzzy Logic ◽  
Network Performance ◽  
Performance Metrics ◽  
Early Stage ◽  
Active Queue Management ◽  
Traffic Load ◽  
Performance Criteria ◽  
Queue Management ◽  
Method Show

Abstract The congestion problem at the router buffer leads to serious consequences on network performance. Active Queue Management (AQM) has been developed to react to any possible congestion at the router buffer at an early stage. The limitation of the existing fuzzy-based AQM is the utilization of indicators that do not address all the performance criteria and quality of services required. In this paper, a new method for active queue management is proposed based on using the fuzzy logic and multiple performance indicators that are extracted from the network performance metrics. These indicators are queue length, delta queue and expected loss. The simulation of the proposed method show that in high traffic load, the proposed method preserves packet loss, drop packet only when it is necessary and produce a satisfactory delay that outperformed the state-of-the-art AQM methods.

Design of filter coefficients for optimal factored truncated cascade FIR filter using optimization algorithm

2021 ◽  
pp. 1-18
Author(s):  
K. Srivatsan
Keyword(s):  
Communication Networks ◽  
Optimization Algorithm ◽  
Absolute Error ◽  
Fir Filter ◽  
Least Square ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Hardware Complexity ◽  
Number Of Components ◽  
Selection Of

Signal filtering acts as one of the basic requirement of communication networks for the removal of unwanted features from the signal. The design of appropriate digital filter requires the selection of optimal filter coefficients for the generation of desired frequency response with reduced hardware complexity. This paper proposes a hybrid optimization algorithm named as Brain Storm- Grey Wolf Optimizer (BSGWO) algorithm for the selection of filter coefficients in the design of factored truncated cascade FIR filter. The proposed algorithm is the hybridization of the optimization algorithms, namely Brain Storm Optimization (BSO) and Grey Wolf Optimizer (GWO). The input signal is interpolated initially for the formation of an intermediate signal using the FIR filter. Then, the factored truncated cascade filter is developed for the interpolation of the signal. After designing the filter coefficients, the optimal selection of the filter coefficients is performed using the proposed BSGWO algorithm. The original filter is developed with the use of the least square estimation and the new filter is developed using the proposed algorithm that tunes the filter coefficients. The performance of the proposed system is analyzed using the metrics, such as fitness, Mean Absolute Error (MAE), magnitude, and the number of components. The proposed method produces minimum fitness, MAE, magnitude and number of components of 0.05, 0.0155, − 96.0  dB and 3372, respectively that shows the effectiveness of the proposed method.

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