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Machine Learning Modelling-Powered IoT Systems for Smart Applications

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IoT-based Intelligent Modelling for Environmental and Ecological Engineering

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 67))

Abstract

With the rapid development of Internet of Things (IoT) and the use of smart devices and social networks in our daily lives, applications-based on IoT are growing exponentially in many fields such industries, business and daily life activities. The IoT technology brings a lot of promise for humanity by improving life quality and comfort and by strengthening human bonds, among others. In the next few years, billions of connected devices will be spread across smart homes, vehicles, cities, and industries. Such connected devices, with restricted resources, will interchange with users and the surrounding environment. In this context, Machine Learning (ML), Which is able to provide embedded intelligence in the IoT devices and networks, can be leveraged to decode the meaning and behavior behind the device’s data, implement accurate predictions, and make decisions for several tasks. In this chapter, we present an overview of research works about ML-base IoT systems in different areas of applications. First, we present a deep overview of IoT’s technology. Then, we highlight the most fundamental concepts of ML categories and algorithms. After that, we shed light on the ML-based IoT critical challenges and provide some potential future research directions. Eventually, we present an IoT-based ML technique scenario for smart irrigation in Agriculture 4.0.

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Authors and Affiliations

  1. Laboratory of Electronics and Microelectronics, University of Monastir, Monastir, Tunisia

    Seifeddine Messaoud

  2. XLIM Institute, University of Poitiers, Bât SP2MI, 11 Bd Marie et Pierre Curie, 86962, Chasseneuil Cedex, France

    Olfa Ben Ahmed & Abbas Bradai

  3. College of Computer Science, King Khalid University, Abha, Saudi Arabia

    Mohamed Atri

Authors
  1. Seifeddine Messaoud
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  2. Olfa Ben Ahmed
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  3. Abbas Bradai
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  4. Mohamed Atri
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Correspondence to Seifeddine Messaoud .

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Editors and Affiliations

  1. Department of Computer Science, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, UK

    Paul Krause

  2. Department of Computer Science, Universitat Politècnica de Catalunya, Barcelona, Spain

    Fatos Xhafa

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Messaoud, S., Ben Ahmed, O., Bradai, A., Atri, M. (2021). Machine Learning Modelling-Powered IoT Systems for Smart Applications. In: Krause, P., Xhafa, F. (eds) IoT-based Intelligent Modelling for Environmental and Ecological Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 67. Springer, Cham. https://doi.org/10.1007/978-3-030-71172-6_8

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