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Machine Learning-Enhanced Radio Tomographic Device for Energy Optimization in Smart Buildings

Author

Listed:
  • Michał Styła

    (Research and Development Center of Information Technologies (CBRTI), 35-326 Rzeszów, Poland)

  • Bartłomiej Kiczek

    (Institute of Physics, Maria Curie-Sklodowska University, 20-031 Lublin, Poland)

  • Grzegorz Kłosowski

    (Faculty of Management, Lublin University of Technology, 20-502 Lublin, Poland)

  • Tomasz Rymarczyk

    (Faculty of Transport and Computer Science, WSEI University, 20-209 Lublin, Poland)

  • Przemysław Adamkiewicz

    (Research and Development Center of Information Technologies (CBRTI), 35-326 Rzeszów, Poland
    Faculty of Transport and Computer Science, WSEI University, 20-209 Lublin, Poland)

  • Dariusz Wójcik

    (Faculty of Transport and Computer Science, WSEI University, 20-209 Lublin, Poland)

  • Tomasz Cieplak

    (Faculty of Management, Lublin University of Technology, 20-502 Lublin, Poland)

Abstract

Smart buildings are becoming a new standard in construction, which allows for many possibilities to introduce ergonomics and energy savings. These contain simple improvements, such as controlling lights and optimizing heating or air conditioning systems in the building, but also more complex ones, such as indoor movement tracking of building users. One of the necessary components is an indoor localization system, especially without any device worn by the person being located. These types of solutions are important in locating people inside smart buildings, managing hospitals of the future and other similar institutions. The article presents a prototype of an innovative energy-efficient device for radio tomography, in which the hardware and software layers of the solution are presented. The presented example consists of 32 radio sensors based on a Bluetooth 5 protocol controlled by a central unit. The preciseness of the system was verified both visually and quantitatively by the image reconstruction as a result of solving the inverse tomographic problem using three neural networks.

Suggested Citation

  • Michał Styła & Bartłomiej Kiczek & Grzegorz Kłosowski & Tomasz Rymarczyk & Przemysław Adamkiewicz & Dariusz Wójcik & Tomasz Cieplak, 2022. "Machine Learning-Enhanced Radio Tomographic Device for Energy Optimization in Smart Buildings," Energies, MDPI, vol. 16(1), pages 1-20, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:275-:d:1016191
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    References listed on IDEAS

    as
    1. Tomasz Rymarczyk & Konrad Niderla & Edward Kozłowski & Krzysztof Król & Joanna Maria Wyrwisz & Sylwia Skrzypek-Ahmed & Piotr Gołąbek, 2021. "Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control," Energies, MDPI, vol. 14(23), pages 1-21, December.
    2. Konrad Kania & Tomasz Rymarczyk & Mariusz Mazurek & Sylwia Skrzypek-Ahmed & Mirosław Guzik & Piotr Oleszczuk, 2021. "Optimisation of Technological Processes by Solving Inverse Problem through Block-Wise-Transform-Reduction Method Using Open Architecture Sensor Platform," Energies, MDPI, vol. 14(24), pages 1-21, December.
    3. Tomasz Rymarczyk & Grzegorz Kłosowski & Anna Hoła & Jerzy Hoła & Jan Sikora & Paweł Tchórzewski & Łukasz Skowron, 2021. "Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning Algorithms," Energies, MDPI, vol. 14(5), pages 1-24, February.
    Full references (including those not matched with items on IDEAS)

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