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

Jafri, Syed Talib Abbas; Ahmed, Irfan; Ali, Sundus

doi:10.3390/electronics11182955

Cited by 8 publications

(7 citation statements)

References 43 publications

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“…A dynamic approach for calculating the average queue length, as proved by [46], utilizes exponential calculation and leads to improved results compared to the linear function used in RED. Similarly, the exponential packet-dropping mechanism in Aggressive RED (AgRED) [47] improved the results of RED, as proved in [48]. Modified RED (MD-RED) uses an adaptive selection of the dropping function based on the traffic load.…”

Section: The Related Workmentioning

confidence: 98%

“…The PHAQM [19] method solely utilizes delay as an indicator in nonadaptive mechanism. On the other hand, the adaptive methods, including ARED [30], DRED [31], BLUE [32], GREEN [33], SRED [34], ATRED [39], CRED [40], AAQMRD [41], LTRED [42], WQDAQM [43], BetaRED [44] AC-RED [45], and AgRED [47] do not consider queue and traffic statuses explicitly, instead, focusing on adaptively calculating or estimating certain parameters to stabilize the performance while mitigating high packet dropping in high congested networks.…”

Section: Theoretical Comparisonmentioning

confidence: 99%

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A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

Alshahrani,

Abu-Shareha,

Shambour

et al. 2023

CMC

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Active queue management (AQM) methods manage the queued packets at the router buffer, prevent buffer congestion, and stabilize the network performance. The bursty nature of the traffic passing by the network routers and the slake behavior of the existing AQM methods leads to unnecessary packet dropping. This paper proposes a fully adaptive active queue management (AAQM) method to maintain stable network performance, avoid congestion and packet loss, and eliminate unnecessary packet dropping. The proposed AAQM method is based on load and queue length indicators and uses an adaptive mechanism to adjust the dropping probability based on the buffer status. The proposed AAQM method adapts to single and multiclass traffic models. Extensive simulation results over two types of traffic showed that the proposed method achieved the best results compared to the existing methods, including Random Early Detection (RED), BLUE, Effective RED (ERED), Fuzzy RED (FRED), Fuzzy Gentle RED (FGRED), and Fuzzy BLUE (FBLUE). The proposed and compared methods achieved similar results with low or moderate traffic load. However, under high traffic load, the proposed AAQM method achieved the best rate of zero loss, similar to BLUE, compared to 0.01 for RED, 0.27 for ERED, 0.04 for FRED, 0.12 for FGRED, and 0.44 for FBLUE. For throughput, the proposed AAQM method achieved the highest rate of 0.54, surpassing the BLUE method's throughput of 0.43. For delay, the proposed AAQM method achieved the second-best delay of 28.51, while the BLUE method achieved the best delay of 13.18; however, the BLUE results are insufficient because of the low throughput. Consequently, the proposed AAQM method outperformed the compared methods with its superior throughput and acceptable delay.

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Section: The Related Workmentioning

confidence: 98%

Section: Theoretical Comparisonmentioning

confidence: 99%

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

Alshahrani,

Abu-Shareha,

Shambour

et al. 2023

CMC

View full text Add to dashboard Cite

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“…TD has two main problemsfull queue problem and lock out problem. Thus, TD causes system instability and unfair bandwidth sharing [20]. So, a method to find out the congestion beforehand is needed.…”

Section: Random Early Detectionmentioning

confidence: 99%

Review of random early detection optimization for congestion control in vehicular ad hoc networks

Ali,

Kadir,

Abidin

et al. 2023

IJEECS

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<span>The research area of “optimization of random early detection for congestion control in vehicular ad hoc networks (VANETS)” is emerging as a prominent area of research in internet, mobile and communication networks. The paper would draw the attention of development of VANETS in a wider context of internet and communications. It touches upon the problem of congestion that needs to be controlled or avoided for smooth performance of the network. It studies further to come up with methods of control or optimize. An update on these methods will be included. The concept of random early detection (RED) is included and studied in such a way the parameters are identified for defining the optimization problem for congestion control problem (CCP) in VANETS. Current trends of optimization of RED in CCP of VANETS will be reviewed, hence an outlook is created for the problem to be investigated with a final conclusion on the potential way forward for the research work.</span>

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“…Obviously, the packet delivery time is of importance in all sorts of networks. Therefore, AQM is recommended by researchers and engineers for wired networks [1,2], wireless sensor networks [3][4][5], LTE and 5G networks [6,7], mobile ad hoc networks [8,9], satellite networks [10,11], and others.…”

Section: Introductionmentioning

confidence: 99%

Level-Crossing Characteristics of an Actively Managed Buffer

Chydzinski

2024

JSAN

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In this paper, we examine a buffer with active management that rejects packets basing on the buffer occupancy. Specifically, we derive several metrics characterizing how effectively the algorithm can prevent the queue of packets from becoming too long and how well it assists in flushing the buffer quickly when necessary. First, we compute the probability that the size of the queue is kept below a predefined level L. Second, we calculate the distribution of the amount of time needed to cross level L, the buffer overflow probability, and the average time to buffer overflow. Third, we derive the distribution of the amount of time required to flush the buffer and its average value. A general modeling framework is used in derivations, with a general service time distribution, general rejection function, and a powerful model of the arrival process. The obtained formulas enable, among other things, the solving of many design problems, e.g., those connected with the design of wireless sensor nodes using the N-policy. Several numerical results are provided, including examples of design problems and other calculations.

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

Cited by 8 publications

References 43 publications

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

Review of random early detection optimization for congestion control in vehicular ad hoc networks

Level-Crossing Characteristics of an Actively Managed Buffer

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