CORTEXA
← Browse
arxivcs.NI2026-07-16

Assisting Mission-Critical Traffic Flows with Active Queue Management in Industrial Internet of Things

Shuo Wang, Jonathan Kua, Jiong Jin, Yew Wee Wong, Prem Prakash Jayaraman, Zhibo Pang

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 with conventional First-In-First-Out (FIFO) buffers. FIFO has proven to be the culprit of the well-known bufferbloat phenomenon, and the deployment of Active Queue Management (AQM) schemes have demonstrated significant performance improvements for latency-sensitive applications over the Internet in the IT domain. However, the bufferbloat phenomenon and the efficacy of AQM schemes have not been studied in IIoT-based OT domain. In this paper, we propose the use of AQM as a lightweight and non-intrusive mechanism for assisting mission-critical traffic flows in IIoT networks. Our experimental results demonstrated that multi-queue AQM schemes provide substantial flow isolation and capacity sharing benefits, and significantly improve the performance of mission-critical traffic flows under network pressure. We further provide deployment recommendations based on our experimental insights.

View free PDFSource page

Related papers

arxivcs.NIcs.AI2026-07-24

A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocation

Fin Gentzen, Marla Grunewald, Iulisloi Zacarias, Mounir Bensalem, Admela Jukan

Large Language Models (LLMs) are increasingly deployed as autonomous agents, transitioning from static conversational interfaces to dynamic systems capable of complex reasoning, tool execution, and decision-making. However, the operational reliability of these agentic AI systems…

View free PDFSource page
arxivcs.NIcs.MAeess.SY2026-07-24

Predictive Lightweight MARL for Resilient Coverage in Sparse-Signaling Aerial Networks

Chuan-Chi Lai, Ang-Hsun Tsai

This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Awa…

View free PDFSource page
arxivq-fin.MFcs.CEcs.ITcs.NI2026-07-24

Neilson's Weak vs. Strong Loss Aversion: A Characterization and a Generalized CPT-Utility Function

Symeon Vaidanis, Marios Kountouris

In multi-objective and multi-criteria decision-making under risk, especially in settings involving individual behavior, risk-aware analysis based on subjective evaluation has become increasingly important. Moving beyond risk-neutral modeling and the constraints of Expected Utilit…

View free PDFSource page