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

Michał Styła, Bartłomiej Kiczek (), Grzegorz Kłosowski, Tomasz Rymarczyk, Przemysław Adamkiewicz, Dariusz Wójcik and Tomasz Cieplak
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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
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

Energies, 2022, vol. 16, issue 1, 1-20

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.

Keywords: radio tomography imaging; machine learning; deep learning; inverse problem; sensors; indoor localization; smart buildings; energy optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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