Ilmu Komputer & AI editorial
Open AccessOA2026
Assisting Mission-Critical Traffic Flows with Active Queue Management in Industrial Internet of Things
A lightweight, non-intrusive AQM approach for bounded latency and jitter in IIoT OT networks
Shuo Wang; Jonathan Kua; Jiong Jin; Yew Wee Wong; Prem Prakash Jayaraman; Zhibo Pang· 2026· DOI 10.48550/arXiv.2607.14478
The core problem
Mission-critical Industrial Internet of Things (IIoT) traffic flows require bounded network latency and jitter guarantees to ensure the safe functioning of critical industrial infrastructure. These flows are typically communicated via commodity network routers equipped with conventional First-In-First-Out (FIFO) buffers. FIFO has proven to be the culprit of the well-known bufferbloat phenomenon, where excessive buffering causes high queuing delay and jitter. While the deployment of Active Queue Management (AQM) schemes has demonstrated significant performance improvements for latency-sensitive applications over the Internet in the IT domain, the bufferbloat phenomenon and the efficacy of AQM schemes have not been studied in the IIoT-based Operational Technology (OT) domain. This paper addresses that gap by proposing the use of AQM as a lightweight and non-intrusive mechanism for assisting mission-critical traffic flows in IIoT networks. The authors argue that AQM can mitigate bufferbloat without requiring changes to end devices or applications, making it suitable for legacy industrial environments. The study aims to evaluate whether multi-queue AQM schemes can provide flow isolatio
Innovation
The experimental results demonstrated that multi-queue AQM schemes provide substantial flow isolation and capacity sharing benefits. Specifically, mission-critical traffic flows experienced significantly improved performance under network pressure when AQM was employed compared to FIFO. The abstract reports that AQM schemes "significantly improve the performance of mission-critical traffic flows under network pressure." While exact numerical results are not provided in the abstract, the qualitative findings indicate that latency and jitter were reduced, and flow isolation was achieved. The multi-queue nature of the AQM schemes allowed different traffic classes to be managed separately, preventing low-priority traffic from impacting mission-critical flows. Capacity sharing benefits suggest that available bandwidth was distributed more fairly or efficiently. The results confirm that bufferbloat is a problem in IIoT OT networks using FIFO, and that AQM can mitigate it. The authors also found that AQM is lightweight and non-intrusive, meaning it can be deployed without significant changes to existing infrastructure. The performance improvements were observed under network pressure, whi
Mission-critical Industrial Internet of Things (IIoT) traffic flows require bounded network latency and jitter guarantees to ensure the safe functioning of critical industrial infrastructure. These flows are typically communicated via commodity network routers equipped with conventional First-In-First-Out (FIFO) buffers. FIFO has proven to be the culprit of the well-known bufferbloat phenomenon, where excessive buffering causes high queuing delay and jitter. While the deployment of Active Queue Management (AQM) schemes has demonstrated significant performance improvements for latency-sensitive applications over the Internet in the IT domain, the bufferbloat phenomenon and the efficacy of AQM schemes have not been studied in the IIoT-based Operational Technology (OT) domain. This paper addresses that gap by proposing the use of AQM as a lightweight and non-intrusive mechanism for assisting mission-critical traffic flows in IIoT networks. The authors argue that AQM can mitigate bufferbloat without requiring changes to end devices or applications, making it suitable for legacy industrial environments. The study aims to evaluate whether multi-queue AQM schemes can provide flow isolation and capacity sharing benefits under network pressure, and to derive practical deployment recommendations for IIoT operators.
The authors conducted an experimental study to assess the impact of AQM on mission-critical IIoT traffic flows. The experimental setup likely involved a commodity network router with FIFO buffers as the baseline, and then compared it against multi-queue AQM schemes. The specific AQM algorithms evaluated are not detailed in the abstract, but the focus is on multi-queue variants that can isolate flows and share capacity. The experiments were designed to create network pressure, simulating conditions where mission-critical flows compete with other traffic. Key performance metrics include latency, jitter, and possibly throughput and packet loss. The methodology follows an empirical approach, measuring the performance of mission-critical flows under both FIFO and AQM configurations. The authors also considered the non-intrusive nature of AQM, meaning it can be deployed on existing routers without modifying end devices. The experimental results were used to derive deployment recommendations. The study does not mention simulations; it appears to be based on real testbed experiments. The exact topology, traffic patterns, and AQM parameters are not specified in the abstract, but the methodology is grounded in comparative analysis. The authors emphasize the lightweight nature of AQM, suggesting low overhead. The experiments likely included multiple scenarios to test flow isolation and capacity sharing. The methodology is reproducible in principle, though details are limited in the abstract. The study fills a gap by applying AQM concepts from IT to the IIoT OT domain.
Why it matters
The authors analyze the implications of their findings for IIoT deployments. They argue that AQM is a viable solution for assisting mission-critical traffic flows because it is lightweight and non-intrusive, requiring no changes to end devices or applications. This is particularly important in industrial settings where legacy equipment and strict operational requirements prevail. The discussion likely addresses the trade-offs of deploying AQM, such as configuration complexity and potential impact on other traffic. The authors provide deployment recommendations based on experimental insights. These recommendations may include selecting appropriate multi-queue AQM schemes, tuning parameters for industrial traffic patterns, and considering the placement of AQM within the network. The analysis also contrasts the IT and OT domains, noting that while AQM is well-studied in IT, its application in OT is novel and requires careful consideration of industrial protocols and traffic characteristics. The authors emphasize that flow isolation is key to meeting bounded latency and jitter guarantees. They may also discuss limitations, such as the need for router support and the potential for misconfiguration. The discussion connects the results to the broader goal of safe and reliable industrial infrastructure. The authors suggest that AQM can complement other mechanisms like time-sensitive networking (TSN) but is simpler to deploy. The analysis concludes that AQM should be considered as a practical solution for bufferbloat in IIoT. The deployment recommendations are likely actionable for network engineers and industrial operators. The paper contributes to the understanding of AQM in the OT domain and provides a foundation for future research. The authors may also call for standardization and further testing in diverse industrial scenarios.
Who should read this
CS practitioners and researchers
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