Applied ML for Industrial IoT

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Cloud Continuum and Enabled Applications".

Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 2054

Special Issue Editors


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Guest Editor
Department of Artificial Intelligence, Sejong University, Seoul 05006, Republic of Korea
Interests: machine learning; artificial intelligence; information system; IoT; health informatics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta, Indonesia
Interests: machine learning; artificial intelligence; information system; IoT; health informatics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Data Science, Sejong University, Seoul, Republic of Korea
Interests: data mining and analysis; machine learning; image processing; artificial intelligence; health informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning techniques are providing the industry with a potential solution to help develop Internet of Things (IoT) systems and accelerate innovation. The Open IoT cloud platform provides a framework for developing large-scale IoT applications that rely on data collected from a complex network of sensors and smart devices. There are numerous challenges ahead when putting such a framework in place, one of which is meeting the IoT data and services (quality of service (QoS)) requirements for industrial informatics-based applications in terms of energy efficiency, sensing data quality, network resource consumption, and latency.

The three key components of the new era of machine learning convergence (supervised, unsupervised, and reinforcement learning), with reference to IoT data and service quality for industrial applications, are (a) intelligent devices, (b) intelligent systems, and (c) end-to-end analytics. To deliver more computerized IoT data and services, this Special Issue combines machine learning approaches with sophisticated data analytics optimization prospects. Anomaly detection, a multivariate analysis, streaming, and data visualization are just a few of the IoT difficulties that machine learning algorithms have solved.

In fact, recent research on industrial informatics has addressed the inherent power of fusion between machine learning algorithms and IoT applications. It can provide efficient solutions for the machine comprehension of structured or semi-structured data and optimization problems, particularly those involving incomplete or inconsistent data and low computational capabilities, as well as the Internet of Things (IoT). This Special Issue will cover machine learning approaches as well as theoretical studies and new breakthroughs in various IoT data, services, and applications.

Dr. Muhammad Syafrudin
Dr. Ganjar Alfian
Dr. Norma Latif Fitriyani
Guest Editors

Manuscript Submission Information

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Keywords

  • industrial IoT
  • machine learning for IoT
  • time-series data and analysis
  • intelligent systems
  • industrial informatics
  • anomaly detection
  • fault detections
  • edge computing
  • multivariate analysis
  • data visualization
  • application of iot and machine learning in industry

Published Papers (1 paper)

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Research

34 pages, 2372 KiB  
Article
Multi-Network Latency Prediction for IoT and WSNs
by Josiah E. Balota, Ah-Lian Kor and Olatunji A. Shobande
Computers 2024, 13(1), 6; https://doi.org/10.3390/computers13010006 - 23 Dec 2023
Viewed by 1401
Abstract
The domain of Multi-Network Latency Prediction for IoT and Wireless Sensor Networks (WSNs) confronts significant challenges. However, continuous research efforts and progress in areas such as machine learning, edge computing, security technologies, and hybrid modelling are actively influencing the closure of identified gaps. [...] Read more.
The domain of Multi-Network Latency Prediction for IoT and Wireless Sensor Networks (WSNs) confronts significant challenges. However, continuous research efforts and progress in areas such as machine learning, edge computing, security technologies, and hybrid modelling are actively influencing the closure of identified gaps. Effectively addressing the inherent complexities in this field will play a crucial role in unlocking the full potential of latency prediction systems within the dynamic and diverse landscape of the Internet of Things (IoT). Using linear interpolation and extrapolation algorithms, the study explores the use of multi-network real-time end-to-end latency data for precise prediction. This approach has significantly improved network performance through throughput and response time optimization. The findings indicate prediction accuracy, with the majority of experimental connection pairs achieving over 95% accuracy, and within a 70% to 95% accuracy range. This research provides tangible evidence that data packet and end-to-end latency time predictions for heterogeneous low-rate and low-power WSNs, facilitated by a localized database, can substantially enhance network performance, and minimize latency. Our proposed JosNet model simplifies and streamlines WSN prediction by employing linear interpolation and extrapolation techniques. The research findings also underscore the potential of this approach to revolutionize the management and control of data packets in WSNs, paving the way for more efficient and responsive wireless sensor networks. Full article
(This article belongs to the Special Issue Applied ML for Industrial IoT)
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