Digital Twins & ZeroConf AI: Structuring Automated Intelligent Pipelines for Industrial Applications
- URL: http://arxiv.org/abs/2602.04385v1
- Date: Wed, 04 Feb 2026 10:11:06 GMT
- Title: Digital Twins & ZeroConf AI: Structuring Automated Intelligent Pipelines for Industrial Applications
- Authors: Marco Picone, Fabio Turazza, Matteo Martinelli, Marco Mamei,
- Abstract summary: This work proposes a modular and interoperable solution that enables seamless AI pipeline integration into Cyber-Physical Systems.<n>We introduce the concept of Zero configuration (ZeroConf) AI pipelines, where DTs orchestrate data management and intelligent augmentation.<n>The approach is demonstrated in a MicroFactory scenario, showing support for concurrent ML models and dynamic data processing.
- Score: 3.534869097377701
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The increasing complexity of Cyber-Physical Systems (CPS), particularly in the industrial domain, has amplified the challenges associated with the effective integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. Fragmentation across IoT and IIoT technologies, manifested through diverse communication protocols, data formats and device capabilities, creates a substantial gap between low-level physical layers and high-level intelligent functionalities. Recently, Digital Twin (DT) technology has emerged as a promising solution, offering structured, interoperable and semantically rich digital representations of physical assets. Current approaches are often siloed and tightly coupled, limiting scalability and reuse of AI functionalities. This work proposes a modular and interoperable solution that enables seamless AI pipeline integration into CPS by minimizing configuration and decoupling the roles of DTs and AI components. We introduce the concept of Zero Configuration (ZeroConf) AI pipelines, where DTs orchestrate data management and intelligent augmentation. The approach is demonstrated in a MicroFactory scenario, showing support for concurrent ML models and dynamic data processing, effectively accelerating the deployment of intelligent services in complex industrial settings.
Related papers
- Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directions [40.35481906711933]
Federated split learning (FedSL) has emerged as a promising paradigm for enabling collaborative intelligence in industrial Internet of Things (IoT) systems.<n>We present a comprehensive study of FedSL frameworks tailored for resource-constrained robots in industrial scenarios.
arXiv Detail & Related papers (2025-10-07T09:24:29Z) - The AI_INFN Platform: Artificial Intelligence Development in the Cloud [0.0]
The INFN initiative AI_INFN (Artificial Intelligence at INFN) seeks to promote the use of ML methods across various INFN research scenarios.<n>We will present preliminary benchmarks, functional tests, and case studies, demonstrating both performance and integration outcomes.
arXiv Detail & Related papers (2025-09-26T09:40:51Z) - A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks [49.90474228895655]
Cloud-edge-terminal collaborative intelligence (CETCI) is a fundamental paradigm within the artificial intelligence of things (AIoT) community.<n>CETCI has made significant progress with emerging AIoT applications, moving beyond isolated layer optimization to deployable collaborative intelligence systems.<n>This survey describes foundational architectures, enabling technologies, and scenarios of CETCI paradigms, offering a tutorial-style review for CISAIOT beginners.
arXiv Detail & Related papers (2025-08-26T08:38:01Z) - Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities [54.12860202362483]
This article comprehensively reviews recent developments and research on deep learning-based (DL-based) techniques for integrated sensing and communication (ISAC) systems.<n>ISAC is regarded as a key enabler for 6G and beyond networks, as many emerging applications, such as vehicular networks and industrial robotics, necessitate both sensing and communication capabilities.<n>As an alternative to conventional techniques, DL-based techniques offer efficient and near-optimal solutions with reduced computational complexity.
arXiv Detail & Related papers (2025-08-23T22:27:51Z) - PANAMA: A Network-Aware MARL Framework for Multi-Agent Path Finding in Digital Twin Ecosystems [0.0]
We introduce PANAMA, a novel algorithm with Priority Asymmetry for Network Multi-agent Reinforcement Learning (MARL) based multi-agent path finding (MAPF)<n>Our approach demonstrates superior pathfinding performance in accuracy, speed, and scalability compared to existing benchmarks.<n>PanAMA bridges the gap between network-aware decision-making and robust multi-agent coordination, advancing the synergy between DTs, wireless networks, and AI-driven automation.
arXiv Detail & Related papers (2025-08-09T00:59:55Z) - AI Flow: Perspectives, Scenarios, and Approaches [51.38621621775711]
We introduce AI Flow, a framework that integrates cutting-edge IT and CT advancements.<n>First, device-edge-cloud framework serves as the foundation, which integrates end devices, edge servers, and cloud clusters.<n>Second, we introduce the concept of familial models, which refers to a series of different-sized models with aligned hidden features.<n>Third, connectivity- and interaction-based intelligence emergence is a novel paradigm of AI Flow.
arXiv Detail & Related papers (2025-06-14T12:43:07Z) - Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
arXiv Detail & Related papers (2025-05-03T13:55:38Z) - Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries.<n>By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures.<n>GenAI can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing.
arXiv Detail & Related papers (2024-08-02T10:47:10Z) - Machine Learning Insides OptVerse AI Solver: Design Principles and
Applications [74.67495900436728]
We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver.
We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem.
We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
arXiv Detail & Related papers (2024-01-11T15:02:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.