A Priority-Based Self-Adaptive Random Early Detection Algorithm in IoT Gateways
DOI: https://doi.org/10.33003/jobasr-2023-v1i1-19
Abdulrahman Nasiru Sada.
Oyenike Mary Olanrewaju.
Yusuf Surajo.
Abstract
Effective algorithms for queue management are crucial in place of guaranteeing
maximum efficiency in gateway routers since network traffic continues to expand
dramatically. An online researcher has suggested the Active Queue Management
(AQM) strategy regarding the upcoming generation of gateway switches. The
common active queue scheme remains (RED) Random Early Detection. Random
early detection is susceptible to parameterization issues and lacks a selfadaptation mechanism. Several RED variants have been formed; nevertheless,
variations in traffic load have an adverse effect on all of them. Due to the fact that
each has a static drop pattern, to address the RED and its variation schemes, the
SARED system, or the design of self-adaptive random early detection was
created. But in order to prevent congestion, during the time when the queue length
surpasses a present maximum threshold limit, SARED aggressively removes
packets. This causes networks having a lot of traffic situations the average is
expected to increase queue delay, so in those cases, SARED should be less
aggressive. This paper develops a priority-based queuing congestion control
method for IoT gateways to manage network congestion. Our method (prioritybased algorithms) performs substantially better with regard to throughput, delay,
and packet loss than the present methods of SARED. The outcomes of the
conducted simulation experiments have shown that in scenarios with heavy traffic
loads, priority-based self-adaptive random early detection (PSARED) has greatly
decreased average queuing delay by 3%, minimized average throughput by 1%,
and decreased the rate of packet loss by 10% in contrast to SARED.
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