Guest Editors
Prof. Ruben Gonzalez Crespo, Universidad Internacional de la Rioja (UNIR), Spain.
Dr. Oscar Sanjuán Martínez, LUMEN TECHONOLOGIES, USA.
Dr. J. Javier Rainer Granados, Universidad Internacional de La Rioja (UNIR), Spain.
Summary
Intelligent Internet-of-Things (IoT) will transform artificial intelligence and high dimensional data analysis, by means of shifting from “connected things” to “collective intelligence”.
The recent advancements in artificial intelligence AI; machine learning ML and big data, with a variety of algorithms and platforms, have started to transform conventional software applications into a set of smart and interconnected components that base decision on data capturing, sensing and filtering, proactive collaboration and the integration of federated devices. Deep machine learning and networks components are becoming the backbone of current tendencies such as cyber-physical systems (CPS). Likewise, intelligent integrations and collaborations of smart devices along with their outstanding data collection capabilities are a perfect application domain for such sophisticated learning algorithms. The usefulness of smart and connected mobile devices governed by software utilities has already been demonstrated in several industrial application domains such as healthcare, agriculture and farming, manufacturing, smart buildings, transportation, energy, and environmental surveillance monitoring systems. While these IoT-based systems can capture a good amount of data, the usage of these valuable along with hard-to-produce data are not being fully utilized. ML enables exploring this data in further detail and capturing the hidden relationships that exist among their key factors and parameters, thus providing further insight into the underlying application domains. Furthermore, ML and IoT can be extended for addressing other challenging and unsolved problem such as real time optimization problems, modeling non-linear characteristics of IoT devices and components, and better prediction and classification algorithms.
This Special Issue aims to move forward the state-of-the-art of ML for industrial IoT and to promote innovative applications, methodologies, and trends in research and real-world applications. It seeks to find new uses of ML and IoT, including reinforcement learning scenarios, convolutional and recurrent deep neural networks, the capture of real features and the modeling the behavior of intelligent software and hardware systems.
Keywords
Probable themes include, but are not limited to:
• Modeling IoT systems using ML
• Attention-based approaches to capture significant features in IoT
• Deep learning-based modeling and experience in IoT-based applications such as smart building, healthcare, agriculture, manufacturing, and left-driving cars
• AI technologies for Industry 4.0
• Human–robot interactions
• Cooperative robots
• Mobile applications using ML and IoT for the industry
• Reinforcement learning for modeling decision-making and uncertainty in IoT.
Published Papers
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