Towards Congestion Control Approach Based on Weighted Random Early Detection and Type-2 Fuzzy Logic System

Alhassan, Maha Salaheldeen Elbadawy; Hagras, Hani

doi:10.1109/ceec.2018.8674190

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…The results show better performance in high and moderate traffic scenarios in terms of packet loss and delays, but compared to E_RED, they exhibit poor performance in some other network situations. Maha Salaheldeen used a type-2 fuzzy logic system with weighted random early detection (WRED) to optimize packet drop rates in [30]. The proposed scheme uses fuzzy logic for clustering the packet dropping in three groups (low, medium, and high) and uses maximum membership for each group in the type-2 fuzzy logic system.…”

Section: Related Workmentioning

confidence: 99%

Queue-Buffer Optimization Based on Aggressive Random Early Detection in Massive NB-IoT MANET for 5G Applications

2022

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Elements in massive narrowband Internet of Things (NB-IoT) for 5G networks suffer severely from packet drops due to queue overflow. Active Queue Management (AQM) techniques help in maintaining queue length by dropping packets early, based on certain defined parameters. In this paper, we have proposed an AQM technique, called Aggressive Random Early Detection (AgRED) which, in comparison to previously used Random Early Detection (RED) and exponential RED technique, improves the overall end-to-end delay, throughput, and packet delivery ratio of the massive NB-IoT 5G network while using UDP. This improvement has been achieved due to a sigmoid function used by the AgRED technique, which aggressively and randomly drops the incoming packets preventing them from filling the queue. Because of the incorporation of the AgRED technique, the queue at different nodes will remain available throughout the operation of the network and the probability of delivering the packets will increase. We have analyzed and compared the performance of our proposed AgRED technique and have found that the performance gain for the proposed technique is higher than other techniques (RED and exponential RED) and passive queue management techniques (drop-tail and drop-head). The improvement in results is most significant in congested network deployment scenarios and provides improvements in massive Machine Type Communication, while also supporting ultra-low latency and reliable communication for 5G applications.

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Section: Related Workmentioning

confidence: 99%

Queue-Buffer Optimization Based on Aggressive Random Early Detection in Massive NB-IoT MANET for 5G Applications

2022

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“…Castillo et al (2013) report that T2FLS are essentially "fuzzy fuzzy" sets that generalize type-1 fuzzy sets and systems to work with greater uncertainty. Alhassan & Hagras (2018) report that type-2 fuzzy set is reduced to a type-1 fuzzy set if there is no uncertainty that is analogous to the probability of decreasing to determinism when unpredictability disappears. The membership function of the general type-2 fuzzy set is three-dimensional and includes the footprint of uncertainty (FOU).…”

Section: Introductionmentioning

confidence: 99%

Investment Decision Support Based on Interval Type-2 Fuzzy Expert System

Janková

Jana

Dostál

2021

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The decision-making process on investing in financial markets is a very complex and difficult task, mainly due to the chaotic behavior and high uncertainty in the development of the prices of investment instruments. For this reason, financial markets are increasingly using means of artificial intelligence, namely fuzzy logic, which is able to capture the nonlinear behavior.Fuzzy logic provides a way to draw definitive conclusions from vague, ambiguous, or inaccurate information.However, there are some drawbacks associated with type-1 fuzzy logic, so the type-2 fuzzy logic comes forward, which can work with greater uncertainty. Type-2 fuzzy logic works with a new third dimension fuzzy set that provides additional degrees of freedom and allows to model and process numerical and linguistic uncertainties directly. The paper applies type-2 fuzzy logic to the stock market with the aim to create a simple and understandable model for deciding on investing in investment instruments, which is important for investors in this area. The proposed type-2 fuzzy model uses return, risk, dividend and total expense ratio of ETF as input variables. The created system is able to generate aggregated models from a certain number of language rules, which allows the investor to understand the created financial model. Using type-2 fuzzy logic can lead to more realistic and accurate results than type-1 fuzzy logic.

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Towards Congestion Control Approach Based on Weighted Random Early Detection and Type-2 Fuzzy Logic System

Cited by 2 publications

References 31 publications

Queue-Buffer Optimization Based on Aggressive Random Early Detection in Massive NB-IoT MANET for 5G Applications

Queue-Buffer Optimization Based on Aggressive Random Early Detection in Massive NB-IoT MANET for 5G Applications

Investment Decision Support Based on Interval Type-2 Fuzzy Expert System

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