A Modular KDN-Based Framework for IT/OT Autonomy in Industrial Systems

2025-09-27

AI Summary

To address the challenges of high heterogeneity, stringent real-time requirements, and insufficient adaptability of conventional static management approaches in IT/OT convergence, this paper proposes a Knowledge-Defined Networking (KDN)-driven modular autonomous management framework. The framework comprises four tightly integrated modules: telemetry sensing, knowledge construction, utility-optimized decision-making, and closed-loop control execution. It innovatively incorporates a graph-based state abstraction mechanism and a decoupled architecture, synergistically integrating graph neural network modeling, real-time closed-loop control theory, and programmable endpoint policy deployment. Experimental evaluation demonstrates that the proposed method significantly outperforms baseline approaches in decision latency, control effectiveness, and system stability—achieving measurable improvements in responsiveness, resilience, and operational efficiency of intelligent industrial networks.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The convergence of Information Technology (IT) and Operational Technology (OT) is a critical enabler for achieving autonomous and intelligent industrial systems. However, the increasing complexity, heterogeneity, and real-time demands of industrial environments render traditional rule-based or static management approaches insufficient. In this paper, we present a modular framework based on the Knowledge-Defined Networking (KDN) paradigm, enabling adaptive and autonomous control across IT-OT infrastructures. The proposed architecture is composed of four core modules: Telemetry Collector, Knowledge Builder, Decision Engine, and Control Enforcer. These modules operate in a closed control loop to continuously observe system behavior, extract contextual knowledge, evaluate control actions, and apply policy decisions across programmable industrial endpoints. A graph-based abstraction is used to represent system state, and a utility-optimization mechanism guides control decisions under dynamic conditions. The framework's performance is evaluated using three key metrics: decision latency, control effectiveness, and system stability, demonstrating its capability to enhance resilience, responsiveness, and operational efficiency in smart industrial networks.

Problem

• Enabling adaptive autonomous control across IT-OT infrastructures
• Addressing industrial complexity heterogeneity and real-time demands
• Overcoming limitations of traditional rule-based static management approaches

Innovation

• Modular KDN framework enables IT-OT autonomous control
• Four core modules operate in closed control loop
• Graph abstraction and utility optimization guide decisions