Machine Learning in the Internet of Things for Industry 4.0

• URL: http://arxiv.org/abs/2005.11146v1
• Date: Fri, 22 May 2020 12:43:15 GMT
• Title: Machine Learning in the Internet of Things for Industry 4.0
• Authors: Tomasz Szydlo, Joanna Sendorek, Robert Brzoza-Woch, Mateusz Windak
• Abstract summary: We show that organization of such systems depends on the entire processing stack, from the hardware layer all the way to the software layer, as well as on the required response times of the IoT system. We propose a flow processing stack for such systems along with the organizational machine learning architectural patterns that enable the possibility to spread the learning and inferencing on the edge and the cloud.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Number of IoT devices is constantly increasing which results in greater complexity of computations and high data velocity. One of the approach to process sensor data is dataflow programming. It enables the development of reactive software with short processing and rapid response times, especially when moved to the edge of the network. This is especially important in systems that utilize online machine learning algorithms to analyze ongoing processes such as those observed in Industry 4.0. In this paper, we show that organization of such systems depends on the entire processing stack, from the hardware layer all the way to the software layer, as well as on the required response times of the IoT system. We propose a flow processing stack for such systems along with the organizational machine learning architectural patterns that enable the possibility to spread the learning and inferencing on the edge and the cloud. In the paper, we analyse what latency is introduced by communication technologies used in the IoT for cloud connectivity and how they influence the response times of the system. Finally, we are providing recommendations which machine learning patterns should be used in the IoT systems depending on the application type.

Related papers

• Give and Take: Federated Transfer Learning for Industrial IoT Network Intrusion Detection [3.7498611358320733]
  We propose a federated transfer learning (FTL) approach to perform IIoT network intrusion detection. As part of the research, we also propose a combinational neural network as the centerpiece for performing FTL. Results showcase high performance for the FTL setup between iterations on both the IIoT clients and the server.
  arXiv  Detail & Related papers  (2023-10-11T10:11:54Z)
• FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems [61.335229621081346]
  Federated Learning (FL) has become a viable technique for realizing privacy-enhancing distributed deep learning on the network edge. In this paper, we propose FLEdge, which complements existing FL benchmarks by enabling a systematic evaluation of client capabilities.
  arXiv  Detail & Related papers  (2023-06-08T13:11:20Z)
• Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications [46.97774949613859]
  Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial intelligence (AI) However, their superior performance comes at the considerable cost of computational complexity. This paper provides an overview of efficient deep learning methods, systems and applications.
  arXiv  Detail & Related papers  (2022-04-25T16:52:48Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• An Automated Data Engineering Pipeline for Anomaly Detection of IoT Sensor Data [0.0]
  System of Chip technology, Internet of Things (IoT), cloud computing, and artificial intelligence has brought more possibilities of improving and solving the current problems. Data analytics and the use of machine learning/deep learning makes it possible to learn the underlying patterns and make decisions based on what was learned from massive data generated from IoT sensors. Process involves the use of IoT sensors, Raspberry Pis, Amazon Web Services (AWS) and multiple machine learning techniques with the intent to identify anomalous cases for the smart home security system.
  arXiv  Detail & Related papers  (2021-09-28T15:57:29Z)
• Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection [10.232121085973782]
  We build a FedIoT platform that contains a synthesized dataset using N-BaIoT, FedDetect algorithm, and a system design for IoT devices. In a network of realistic IoT devices (PI), we evaluate FedIoT platform and FedDetect algorithm in both model and system performance.
  arXiv  Detail & Related papers  (2021-06-15T08:53:42Z)
• Automated Machine Learning Techniques for Data Streams [91.3755431537592]
  This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time. The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
  arXiv  Detail & Related papers  (2021-06-14T11:42:46Z)
• Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence [45.076180487387575]
  Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems.
  arXiv  Detail & Related papers  (2021-05-04T23:42:06Z)
• Machine Learning for Massive Industrial Internet of Things [69.52379407906017]
  Industrial Internet of Things (IIoT) revolutionizes the future manufacturing facilities by integrating the Internet of Things technologies into industrial settings. With the deployment of massive IIoT devices, it is difficult for the wireless network to support the ubiquitous connections with diverse quality-of-service (QoS) requirements. We first summarize the requirements of the typical massive non-critical and critical IIoT use cases. We then identify unique characteristics in the massive IIoT scenario, and the corresponding machine learning solutions with its limitations and potential research directions.
  arXiv  Detail & Related papers  (2021-03-10T20:10:53Z)
• Real-time Neural Networks Implementation Proposal for Microcontrollers [0.0]
  This paper aims to show an implementation strategy of a Multilayer Perceptron (MLP) type neural network, in a low-cost, low-power platform. A modular matrix-based microcontroller with the full classification process was implemented, and also the backpropagation training in the microcontroller. The testing and validation were performed through Hardware in the Loop (HIL) of the Mean Squared Error (MSE) of the training process, classification result, and the processing time of each implementation module.
  arXiv  Detail & Related papers  (2020-06-08T03:51:14Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

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.