Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems
- URL: http://arxiv.org/abs/2408.01173v2
- Date: Fri, 27 Dec 2024 14:46:55 GMT
- Title: Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems
- Authors: Jinbo Wen, Jiawen Kang, Dusit Niyato, Yang Zhang, Shiwen Mao,
- Abstract summary: Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries.
By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures.
GenAI can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing.
- Score: 65.22300383287904
- License:
- Abstract: Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs.
Related papers
- Digital Transformation in the Water Distribution System based on the Digital Twins Concept [0.0]
This paper describes the development of a state-of-the-art DT platform for water distribution systems.
It introduces advanced technologies such as the Internet of Things, Artificial Intelligence, and Machine Learning models.
In this view, the system will contribute to improvements in decision-making capabilities, operational efficiency, and system reliability.
arXiv Detail & Related papers (2024-12-09T17:40:37Z) - Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing [53.77822620185878]
We propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs.
We develop "BayesMulti", a training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections.
Our integrated approach enables use of analog computing in much deeper and wider networks, achieving up to 100-fold improvements.
arXiv Detail & Related papers (2024-12-03T19:20:08Z) - Constructing and Evaluating Digital Twins: An Intelligent Framework for DT Development [11.40908718824589]
Development of Digital Twins (DTs) represents a transformative advance for simulating and optimizing complex systems in a controlled digital space.
This paper introduces an intelligent framework for the construction and evaluation of DTs, specifically designed to enhance the accuracy and utility of DTs in testing algorithmic performance.
We propose a novel construction methodology that integrates deep learning-based policy gradient techniques to dynamically tune the DT parameters, ensuring high fidelity in the digital replication of physical systems.
arXiv Detail & Related papers (2024-06-19T01:45:18Z) - Multi-Tier Computing-Enabled Digital Twin in 6G Networks [50.236861239246835]
In Industry 4.0, industries such as manufacturing, automotive, and healthcare are rapidly adopting DT-based development.
The main challenges to date have been the high demands on communication and computing resources, as well as privacy and security concerns.
To achieve low latency and high security services in the emerging DT, multi-tier computing has been proposed by combining edge/fog computing and cloud computing.
arXiv Detail & Related papers (2023-12-28T13:02:53Z) - TMAP: A Threat Modeling and Attack Path Analysis Framework for Industrial IoT Systems (A Case Study of IoM and IoP) [2.9922995594704984]
To deploy secure Industrial Control and Production Systems (ICPS) in smart factories, cyber threats and risks must be addressed.
Current approaches for threat modeling in cyber-physical systems (CPS) are ad hoc and inefficient.
This paper proposes a novel quantitative threat modeling approach, aiming to identify probable attack vectors, assess the path of attacks, and evaluate the magnitude of each vector.
arXiv Detail & Related papers (2023-12-23T18:32:53Z) - Digital Twins and the Future of their Use Enabling Shift Left and Shift Right Cybersecurity Operations [15.061739314361871]
Digital Twins (DTs) optimize operations and monitor performance in Smart Critical Systems (SCS) domains like smart grids and manufacturing.
This vision paper outlines intelligent SDT design through innovative techniques, exploring hybrid intelligence with data-driven and rule-based semantic SDT models.
arXiv Detail & Related papers (2023-09-24T11:20:58Z) - Causal Semantic Communication for Digital Twins: A Generalizable
Imitation Learning Approach [74.25870052841226]
A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services.
Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints.
A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
arXiv Detail & Related papers (2023-04-25T00:15:00Z) - Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G
Networks: Research Directions for Security and Optimal Control [3.1798318618973362]
Digital twin (DT) technologies have emerged as a solution for real-time data-driven modeling of cyber physical systems.
We establish a conceptual layered architecture for a DT framework with decentralized implementation on cloud computing.
We discuss the significance of DT in lowering the risk of development and deployment of innovative technologies on existing system.
arXiv Detail & Related papers (2022-04-05T03:04:02Z) - Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome.
Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations.
We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
arXiv Detail & Related papers (2021-06-14T20:48:48Z) - AI-based Modeling and Data-driven Evaluation for Smart Manufacturing
Processes [56.65379135797867]
We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes.
We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm.
arXiv Detail & Related papers (2020-08-29T14:57:53Z)
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.