Simulation model of ACO, FLC and PID controller for TCP/AQM wireless networks by using MATLAB/Simulink

Awad, Buraq Abdulhadi; Oudah, Manal K.; Enaya, Yaser Ali; Shneen, Salam Waley

doi:10.11591/ijece.v13i3.pp2677-2685

Cited by 1 publication

(3 citation statements)

References 27 publications

(28 reference statements)

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“…Existing AQM methods are broadly categorized into two main types: crisp-based AQM and fuzzy-based AQM. Fuzzy-based methods expand upon the crisp approach by incorporating the fuzzy inference process, as illustrated in Figure 2 [10]. of these AQM methods, which harms the network performance with the bursty nature of the traffic.…”

Section: The Literature Reviewmentioning

confidence: 99%

“…In the proposed method, all three measures were given equal weights of 0.33 to find the optimal solution that suited the burstiness nature of the network. The hard constraint in Equation (10) ensured that the sum of the weights given to all the performances equals 1.…”

Section: 𝐷𝑝 = 𝑓(𝑏 𝑏 𝑏 )mentioning

confidence: 99%

“…The early AQM methods calculate the value of the dropping probability (Dp) based on the value of the status variables using a set of rules wrapped in case-based reasoning; these methods can be denoted as crisp-based AQM methods. These methods were extended with the fuzzy inference process to ease the parameterization problem and reduce packet loss [10]. For the fuzzy-based methods, the fuzzy system is formed using input variables mapped from the status variables in the crisp-based AQM methods while the output variable represents the Dp.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy-Based Active Queue Management Using Precise Fuzzy Modeling and Genetic Algorithm

Abu-Shareha,

Alsaaidah,

Alshahrani

et al. 2023

Symmetry

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Active Queue Management (AQM) methods significantly impact the network performance, as they manage the router queue and facilitate the traffic flow through the network. This paper presents a novel fuzzy-based AQM method developed with a computationally efficient precise fuzzy modeling optimized using the Genetic Algorithm. The proposed method focuses on the concept of symmetry as a means to achieve a more balanced and equitable distribution of the resources and avoid bandwidth wasting resulting from unnecessary packet dropping. The proposed method calculates the dropping probability of each packet using a precise fuzzy model that was created and tuned in advance and based on the previous dropping probability value and the queue length. The tuning process is implemented as an optimization problem formulated for the b0, b1, and b2 variables of the precise rules with an objective function that maximizes the performance results in terms of loss, dropping, and delay. To prove the efficiency of the developed method, the simulation was not limited to the common Bernoulli process simulation; instead, the Markov-modulated Bernoulli process was used to mimic the burstiness nature of the traffic. The simulation is conducted on a machine operated with 64-bit Windows 10 with an Intel Core i7 2.0 GHz processor and 16 GB of RAM. The simulation used Java programming language in Apache NetBeans Integrated Development Environment (IDE) 11.2. The results showed that the proposed method outperformed the existing methods in terms of computational complexity, packet loss, dropping, and delay. As such, in low congested networks, the proposed method maintained no packet loss and dropped 22% of the packets with an average delay of 7.57, compared to the best method, LRED, which dropped 21% of the packets with a delay of 10.74, and FCRED, which dropped 21% of the packets with a delay of 16.54. In highly congested networks, the proposed method also maintained no packet loss and dropped 48% of the packets, with an average delay of 16.23, compared to the best method LRED, which dropped 47% of the packets with a delay of 28.04, and FCRED, which dropped 46% of the packets with a delay of 40.23.

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Section: The Literature Reviewmentioning

confidence: 99%

Section: 𝐷𝑝 = 𝑓(𝑏 𝑏 𝑏 )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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