Enhanced Indoor Positioning System Using Ultra-Wideband Technology and Machine Learning Algorithms for Energy-Efficient Warehouse Management

Dominik Gnaś, Dariusz Majerek, Michał Styła (), Przemysław Adamkiewicz, Stanisław Skowron, Monika Sak-Skowron, Olena Ivashko, Józef Stokłosa and Robert Pietrzyk
Additional contact information
Dominik Gnaś: Research and Development Center of Information Technologies (CBRTI), 35-326 Rzeszów, Poland
Dariusz Majerek: Faculty of Mathematics and Information Technology, Lublin University of Technology, 20-502 Lublin, Poland
Michał Styła: Research and Development Center of Information Technologies (CBRTI), 35-326 Rzeszów, Poland
Przemysław Adamkiewicz: Research and Development Center of Information Technologies (CBRTI), 35-326 Rzeszów, Poland
Stanisław Skowron: Faculty of Management, Lublin University of Technology, 20-502 Lublin, Poland
Monika Sak-Skowron: Department of Enterprise Management, John Paul II Catholic University of Lublin KUL, 20-502 Lublin, Poland
Olena Ivashko: Faculty of Administration and Social Sciences, WSEI University, 20-209 Lublin, Poland
Józef Stokłosa: Faculty of Transport and Information Technology, WSEI University, 20-209 Lublin, Poland
Robert Pietrzyk: Faculty of Transport and Information Technology, WSEI University, 20-209 Lublin, Poland

Energies, 2024, vol. 17, issue 16, 1-15

Abstract: The following article presents a proprietary real-time localization system using temporal analysis techniques and detection and localization algorithms supported by machine learning mechanisms. It covers both the technological aspects, such as proprietary electronics, and the overall architecture of the system for managing human and fixed assets. Its origins lie in the ever-increasing degree of automation in the management of company processes and the energy optimization associated with reducing the execution time of tasks in an intelligent building supported by in-building navigation. The positioning and tracking of objects in the presented system was realized using ultra-wideband radio tag technology. An exceptional focus has been placed on reducing the energy requirements of the components in order to maximize battery runtime, generate savings in terms of more efficient management of other energy consumers in the building and increase the equipment’s overall lifespan.

Keywords: temporal distance scaling methods; ultra-wideband technologies; energy saving; machine learning; indoor navigation; fixed asset management systems (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: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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