Comparative analysis of the performance of various active queue management techniques to varying wireless network conditions

Okokpujie, Kennedy; Chukwu, Emmanuel C.; Shobayo, Olamilekan; Noma‐Osaghae, Etinosa; Okokpujie, Imhade P.; Odusami, Modupe

doi:10.11591/ijece.v9i1.pp359-368

Cited by 14 publications

(13 citation statements)

References 13 publications

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“…This review compare the algorithms discussed above with each other to analyze the improvement performance of these algorithms. In [55] the comparison have been hold based on varying the propagation delay and the link capacity. While in this study, the AQM schemas are briefly discussed and compared based on several metrics, as shown in Table 2.…”

Section: Comparison Between the Discussed Algorithmsmentioning

confidence: 99%

Survey on: A variety of AQM algorithm schemas and intelligent techniques developed for congestion control

Mahawish

Hassan

2021

IJEECS

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The congestion on the internet is the main issue that affects the performance of transition data over the network. An algorithm for congestion control is required to keep any network efficient and reliable for transfer traffic data of the users. Many Algorithms had been suggested over the years to improve the control of congestion that occurs in the network such as drop tail packets. Recently there are many algorithms have been developed to overcome the drawback of the drop tail procedure. One of the important algorithms developed is active queue management (AQM) that provides efficient congestion control by reducing drop packets, this technique considered as a base for many other congestion control algorithms schema. It works at the network core (router) for controlling the drop and marking of packets in the router's buffer before the congestion inception. In this study, a comprehensive survey is done on the AQM Algorithm schemas that proposed and modification these algorithms to achieve the best performance, the classification of AQM algorithms based on queue length, queue delay, or both. The advantages and limitations of each algorithm have been discussed. Also, debate the intelligent techniques procedure with AQM algorithm to achieve optimization in performance of algorithm operation. Finally, the comparison has been discussed among algorithms to find the weakness and powerful of each one based on different metrics.

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Section: Comparison Between the Discussed Algorithmsmentioning

confidence: 99%

Survey on: A variety of AQM algorithm schemas and intelligent techniques developed for congestion control

Mahawish

Hassan

2021

IJEECS

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“…Channel capacity is a parameter of the channel, which can reflect the maximum amount of information that can be transmitted by the channel. At present, the single-channel protocol is the main protocol in the practical application of wireless network, which simplifies the implementation of the protocol, reduces the difficulty of mutual discovery between nodes, and makes the networking more rapid and convenient [5]. However, when the amount of data is very large, many nodes will apply for communication on this channel at the same time, resulting in increased communication competition.…”

Section: Introductionmentioning

confidence: 99%

A Wireless Network Communication Capacity Control Technology Based on Fuzzy Wavelet Neural Network

Zheng¹,

Yun²

2021

Wireless Communications and Mobile Computing

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The communication capacity control of the computer wireless network is the basis for realizing the efficient communication of massive data. In order to study the communication capacity control technology of the computer wireless network, improve the control effect of a large amount of data communication, and calculate the capacity of the wireless network in real time, this paper uses the fuzzy wavelet neural network to predict the wireless network channel. After the interference-free channel is obtained, the load balancing strategy of the ant colony optimization algorithm is used to filter the channel, and the channel allocation sequence with the most balanced load distribution is obtained, and a priority selection list is generated. After discretizing the channels in the largest discretization selection list, the channel sequence is allocated to the pair of nodes with communication requests according to the greedy coloring algorithm, so as to realize the communication capacity control of the computer wireless network. The test results show that the technology can guarantee good communication performance in both static and dynamic networks and can effectively complete network communication of massive data, and the communication capacity control effect is good.

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“…Okokpujie, et al, [20], they determined the techniques of active queue management robustness to varying load, propagation delay in a wireless environment, and link capacity. The transmission control protocol queue management is contrlloled by four stanjdard were compared.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control of magnetic levitation system

Alkurawy

Mohammed

2020

IJECE

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In this work, we suggest a technique of controller design that applied to systems based on nonlinear. We inform the sufficient conditions for the stability of closed loop system. The asymptotic stability of equilibrium and the nonlinear controller can be applied to improvement the stability of Magnetic Levitation system(MagLev). The MagLev nonlinear nodel can be obtained by state equation based on Lagrange function and Model Predictive Control has been used for MagLev system.

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Comparative analysis of the performance of various active queue management techniques to varying wireless network conditions

Cited by 14 publications

References 13 publications

Survey on: A variety of AQM algorithm schemas and intelligent techniques developed for congestion control

Survey on: A variety of AQM algorithm schemas and intelligent techniques developed for congestion control

A Wireless Network Communication Capacity Control Technology Based on Fuzzy Wavelet Neural Network

Model predictive control of magnetic levitation system

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