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

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• 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