A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems

• URL: http://arxiv.org/abs/2210.01985v1
• Date: Wed, 5 Oct 2022 02:18:36 GMT
• Title: A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems
• Authors: Li Yang, Abdallah Shami
• Abstract summary: We propose a novel Multi-Stage Automated Network Analytics (MSANA) framework for concept drift adaptation in IIoT systems. MSANA consists of dynamic data pre-processing, Drift-based Dynamic Feature Selection (DD-FS) method, dynamic model learning & selection, and Window-based Performance Weighted Probability Averaging Ensemble (W-PWPAE) model. It is a complete automated data stream analytics framework that enables automatic, effective, and efficient data analytics for IIoT systems in Industry 5.0.
• Score: 10.350337750192997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Industry 5.0 aims at maximizing the collaboration between humans and machines. Machines are capable of automating repetitive jobs, while humans handle creative tasks. As a critical component of Industrial Internet of Things (IIoT) systems for service delivery, network data stream analytics often encounter concept drift issues due to dynamic IIoT environments, causing performance degradation and automation difficulties. In this paper, we propose a novel Multi-Stage Automated Network Analytics (MSANA) framework for concept drift adaptation in IIoT systems, consisting of dynamic data pre-processing, the proposed Drift-based Dynamic Feature Selection (DD-FS) method, dynamic model learning & selection, and the proposed Window-based Performance Weighted Probability Averaging Ensemble (W-PWPAE) model. It is a complete automated data stream analytics framework that enables automatic, effective, and efficient data analytics for IIoT systems in Industry 5.0. Experimental results on two public IoT datasets demonstrate that the proposed framework outperforms state-of-the-art methods for IIoT data stream analytics.

Related papers

• 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. By digitizing data throughout the product life cycle, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. mechanisms that leverage sensing Industrial Internet of Things (IIoT) devices to share data for the construction of DTs are susceptible to adverse selection problems.
  arXiv  Detail & Related papers  (2024-08-02T10:47:10Z)
• Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning [50.332027356848094]
  AI-based applications are deployed at intelligent controllers to carry out functionalities like scheduling or power control. The mapping between context and AI model parameters is ideally done in a zero-shot fashion. This paper introduces a general methodology for the online optimization of AMS mappings.
  arXiv  Detail & Related papers  (2024-06-22T11:17:50Z)
• AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving [68.73885845181242]
  We propose an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.
  arXiv  Detail & Related papers  (2024-03-26T04:27:56Z)
• A Generative Approach for Production-Aware Industrial Network Traffic Modeling [70.46446906513677]
  We investigate the network traffic data generated from a laser cutting machine deployed in a Trumpf factory in Germany. We analyze the traffic statistics, capture the dependencies between the internal states of the machine, and model the network traffic as a production state dependent process. We compare the performance of various generative models including variational autoencoder (VAE), conditional variational autoencoder (CVAE), and generative adversarial network (GAN)
  arXiv  Detail & Related papers  (2022-11-11T09:46:58Z)
• IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective [10.350337750192997]
  We conduct a review of existing methods in the model selection, tuning, and updating procedures in the area of AutoML. To justify our findings and help industrial users and researchers better implement AutoML approaches, a case study of applying AutoML to IoT anomaly detection problems is conducted.
  arXiv  Detail & Related papers  (2022-09-16T16:02:56Z)
• Using Neural Networks by Modelling Semi-Active Shock Absorber [0.0]
  A permanently increasing number of on-board automotive control systems requires new approaches to their digital mapping. Various methods applying neural networks (NN) can be good candidates for relevant digital twin (DT) tools in automotive control system design. The paper presents an approach, which demonstrates how the regression tasks can be efficiently handled by the modeling of a semi-active shock absorber.
  arXiv  Detail & Related papers  (2022-07-19T09:21:21Z)
• Gradient-Based Trajectory Optimization With Learned Dynamics [80.41791191022139]
  We use machine learning techniques to learn a differentiable dynamics model of the system from data. We show that a neural network can model highly nonlinear behaviors accurately for large time horizons. In our hardware experiments, we demonstrate that our learned model can represent complex dynamics for both the Spot and Radio-controlled (RC) car.
  arXiv  Detail & Related papers  (2022-04-09T22:07:34Z)
• PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams [9.953967527396316]
  We propose a Performance Weighted Probability Averaging Ensemble (PWPAE) framework for drift adaptive IoT anomaly detection through IoT data stream analytics. Experiments on two public datasets show the effectiveness of our proposed PWPAE method compared against state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-09-10T17:50:49Z)
• FENXI: Deep-learning Traffic Analytics at the Edge [69.34903175081284]
  We present FENXI, a system to run complex analytics by leveraging TPU. FENXI decouples operations and traffic analytics which operates at different granularities. Our analysis shows that FENXI can sustain forwarding line rate traffic processing requiring only limited resources.
  arXiv  Detail & Related papers  (2021-05-25T08:02:44Z)
• ARDA: Automatic Relational Data Augmentation for Machine Learning [23.570173866941612]
  We present system, an end-to-end system that takes as input a dataset and a data repository, and outputs an augmented data set. Our system has two distinct components: (1) a framework to search and join data with the input data, based on various attributes of the input, and (2) an efficient feature selection algorithm that prunes out noisy or irrelevant features from the resulting join.
  arXiv  Detail & Related papers  (2020-03-21T21:55:22Z)

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.