Technology Category
Planning, Routing, and Scheduling: Model-Based ReasoningCognitive Modeling & Cognitive Systems: Conceptual Inference and ReasoningSearch and Optimization: Sampling/Simulation-based Search
Dual Mind World Model Inspired Network Digital Twin for Access Scheduling
🤖 AI Summary
This work addresses the challenge of intelligent access scheduling in industrial IoT and real-time cyber-physical systems under dynamic traffic, deadline constraints, and interference. Inspired by dual-process cognitive mechanisms, the authors propose a digital twin–based scheduling framework that integrates short-term predictive planning with symbolic model-based reasoning. For the first time, this approach synergistically combines data-driven learning and symbolic inference within a network digital twin, enabling proactive imagination of network states and adaptive decision-making. Evaluation on a configurable simulation platform demonstrates that, under bursty traffic, strong interference, and deadline-sensitive conditions, the proposed method significantly outperforms conventional heuristic and reinforcement learning approaches—achieving higher scheduling efficiency and lower overhead while simultaneously enhancing interpretability and sample efficiency.
Technology Category
Application Category
📝 Abstract
Emerging networked systems such as industrial IoT and real-time cyber-physical infrastructures demand intelligent scheduling strategies capable of adapting to dynamic traffic, deadlines, and interference constraints. In this work, we present a novel Digital Twin-enabled scheduling framework inspired by Dual Mind World Model (DMWM) architecture, for learning-informed and imagination-driven network control. Unlike conventional rule-based or purely data-driven policies, the proposed DMWM combines short-horizon predictive planning with symbolic model-based rollout, enabling the scheduler to anticipate future network states and adjust transmission decisions accordingly. We implement the framework in a configurable simulation testbed and benchmark its performance against traditional heuristics and reinforcement learning baselines under varied traffic conditions. Our results show that DMWM achieves superior performance in bursty, interference-limited, and deadline-sensitive environments, while maintaining interpretability and sample efficiency. The proposed design bridges the gap between network-level reasoning and low-overhead learning, marking a step toward scalable and adaptive NDT-based network optimization.
Problem
Research questions and friction points this paper is trying to address.
access scheduling
networked systems
dynamic traffic
interference constraints
deadline-sensitive
Innovation
Methods, ideas, or system contributions that make the work stand out.
Digital Twin
Dual Mind World Model
Network Scheduling
Model-based Planning
Sample Efficiency
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