Real-Time Location System (RTLS) Based on the Bluetooth Technology for Internal Logistics
The problem of object localization in indoor environments is very important in order to make a company effective and to detect disruption in the logistics system in real-time. Present research investigates how the IoT (Internet of Things) location system based on Bluetooth can be implemented for thi...
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Veröffentlicht in: | Sustainability 2023-03, Vol.15 (6), p.4976 |
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description | The problem of object localization in indoor environments is very important in order to make a company effective and to detect disruption in the logistics system in real-time. Present research investigates how the IoT (Internet of Things) location system based on Bluetooth can be implemented for this solution. The location based on the Bluetooth is hard to predict. Radio wave interference in this frequency is affected by other devices, steel, vessels containing water, and more. However, proper data processing and signal stabilization can increase the accuracy of the location. To be sure that the location system based on the BT (Bluetooth) can be implemented for real cases, an analysis of signal strength amplitude and disruption was made. The paper presents R&D (Research and Development) works with a practical test in real cases. The signal strength fluctuation for the receiver is between 7 and 10 dBm for ESP32 device and between 13 and 14 dBm for Raspberry. For commercial implementation the number of devices scanned in the time window is also important. For Raspberry, the optimal time window is 5 s; in this time six transmitters can be detected. ESP32 has a problem with detecting devices in a short time, as just two transmitters can be detected in 4–8 s time window. Localisation precision depends on the distance between transmitter and receiver, and the angle from the axis of the directional antenna. For the distance of 10 m the measurement error is 1.2–6.1 m, whilst for the distance of 40 m the measurement error is 4.9 to 24.6 m. Using a Kalman filter can reduce the localization error to 1.5 m. |
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Present research investigates how the IoT (Internet of Things) location system based on Bluetooth can be implemented for this solution. The location based on the Bluetooth is hard to predict. Radio wave interference in this frequency is affected by other devices, steel, vessels containing water, and more. However, proper data processing and signal stabilization can increase the accuracy of the location. To be sure that the location system based on the BT (Bluetooth) can be implemented for real cases, an analysis of signal strength amplitude and disruption was made. The paper presents R&D (Research and Development) works with a practical test in real cases. The signal strength fluctuation for the receiver is between 7 and 10 dBm for ESP32 device and between 13 and 14 dBm for Raspberry. For commercial implementation the number of devices scanned in the time window is also important. For Raspberry, the optimal time window is 5 s; in this time six transmitters can be detected. ESP32 has a problem with detecting devices in a short time, as just two transmitters can be detected in 4–8 s time window. Localisation precision depends on the distance between transmitter and receiver, and the angle from the axis of the directional antenna. For the distance of 10 m the measurement error is 1.2–6.1 m, whilst for the distance of 40 m the measurement error is 4.9 to 24.6 m. Using a Kalman filter can reduce the localization error to 1.5 m.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su15064976</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Algorithms ; Automation ; Bluetooth ; Bluetooth technology ; Clinics ; Data processing ; Directional antennas ; Disruption ; Error analysis ; Global positioning systems ; GPS ; Indoor environments ; Infrastructure ; Internet of Things ; Kalman filtering ; Kalman filters ; Localization ; Logistics ; Productivity ; R&D ; Radio frequency identification ; Radio waves ; Real time ; Receivers & amplifiers ; Research & development ; Research methodology ; Severe acute respiratory syndrome coronavirus 2 ; Signal processing ; Signal stabilization ; Signal strength ; Transmitters</subject><ispartof>Sustainability, 2023-03, Vol.15 (6), p.4976</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-61f858b348ecf87cac362e68d0c53fa4d350af9aadb42f69d00e77cea174e8e43</citedby><cites>FETCH-LOGICAL-c368t-61f858b348ecf87cac362e68d0c53fa4d350af9aadb42f69d00e77cea174e8e43</cites><orcidid>0000-0002-2109-2165</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Lorenc, Augustyn</creatorcontrib><creatorcontrib>Szarata, Jakub</creatorcontrib><creatorcontrib>Czuba, Michał</creatorcontrib><title>Real-Time Location System (RTLS) Based on the Bluetooth Technology for Internal Logistics</title><title>Sustainability</title><description>The problem of object localization in indoor environments is very important in order to make a company effective and to detect disruption in the logistics system in real-time. Present research investigates how the IoT (Internet of Things) location system based on Bluetooth can be implemented for this solution. The location based on the Bluetooth is hard to predict. Radio wave interference in this frequency is affected by other devices, steel, vessels containing water, and more. However, proper data processing and signal stabilization can increase the accuracy of the location. To be sure that the location system based on the BT (Bluetooth) can be implemented for real cases, an analysis of signal strength amplitude and disruption was made. The paper presents R&D (Research and Development) works with a practical test in real cases. The signal strength fluctuation for the receiver is between 7 and 10 dBm for ESP32 device and between 13 and 14 dBm for Raspberry. For commercial implementation the number of devices scanned in the time window is also important. For Raspberry, the optimal time window is 5 s; in this time six transmitters can be detected. ESP32 has a problem with detecting devices in a short time, as just two transmitters can be detected in 4–8 s time window. Localisation precision depends on the distance between transmitter and receiver, and the angle from the axis of the directional antenna. For the distance of 10 m the measurement error is 1.2–6.1 m, whilst for the distance of 40 m the measurement error is 4.9 to 24.6 m. Using a Kalman filter can reduce the localization error to 1.5 m.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Automation</subject><subject>Bluetooth</subject><subject>Bluetooth technology</subject><subject>Clinics</subject><subject>Data processing</subject><subject>Directional antennas</subject><subject>Disruption</subject><subject>Error analysis</subject><subject>Global positioning systems</subject><subject>GPS</subject><subject>Indoor environments</subject><subject>Infrastructure</subject><subject>Internet of Things</subject><subject>Kalman filtering</subject><subject>Kalman filters</subject><subject>Localization</subject><subject>Logistics</subject><subject>Productivity</subject><subject>R&D</subject><subject>Radio frequency identification</subject><subject>Radio waves</subject><subject>Real time</subject><subject>Receivers & amplifiers</subject><subject>Research & development</subject><subject>Research methodology</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Signal processing</subject><subject>Signal stabilization</subject><subject>Signal strength</subject><subject>Transmitters</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpVkV9LwzAUxYsoOOZe_AQBX5zQmTRtkz5u4p9BQdjqg08lS2-6jLaZTQru25sxQXdf7uXwO-deuEFwS_CM0gw_2oEkOI0zll4EowgzEhKc4Mt_83UwsXaHfVFKMpKOgs8ViCYsdAsoN1I4bTq0PlgHLbpfFfl6ihbCQoW87LaAFs0Azhi3RQXIbWcaUx-QMj1adg76TjQ-pdbWaWlvgislGguT3z4OPl6ei6e3MH9_XT7N81DSlLswJYonfENjDlJxJoWXI0h5hWVClYgrmmChMiGqTRypNKswBsYkCMJi4BDTcXB3yt335msA68qdGY6n2DJiGWGU04h4anaiatFAqTtlXC_8MlFBq6XpQGmvz1lMM39WzL1hembwjINvV4vB2nK5Xp2zDydW9sbaHlS573Ur-kNJcHl8Tfn3GvoD4ix-8A</recordid><startdate>20230301</startdate><enddate>20230301</enddate><creator>Lorenc, Augustyn</creator><creator>Szarata, Jakub</creator><creator>Czuba, Michał</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-2109-2165</orcidid></search><sort><creationdate>20230301</creationdate><title>Real-Time Location System (RTLS) Based on the Bluetooth Technology for Internal Logistics</title><author>Lorenc, Augustyn ; Szarata, Jakub ; Czuba, Michał</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-61f858b348ecf87cac362e68d0c53fa4d350af9aadb42f69d00e77cea174e8e43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Automation</topic><topic>Bluetooth</topic><topic>Bluetooth technology</topic><topic>Clinics</topic><topic>Data processing</topic><topic>Directional antennas</topic><topic>Disruption</topic><topic>Error analysis</topic><topic>Global positioning systems</topic><topic>GPS</topic><topic>Indoor environments</topic><topic>Infrastructure</topic><topic>Internet of Things</topic><topic>Kalman filtering</topic><topic>Kalman filters</topic><topic>Localization</topic><topic>Logistics</topic><topic>Productivity</topic><topic>R&D</topic><topic>Radio frequency identification</topic><topic>Radio waves</topic><topic>Real time</topic><topic>Receivers & amplifiers</topic><topic>Research & development</topic><topic>Research methodology</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>Signal processing</topic><topic>Signal stabilization</topic><topic>Signal strength</topic><topic>Transmitters</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lorenc, Augustyn</creatorcontrib><creatorcontrib>Szarata, Jakub</creatorcontrib><creatorcontrib>Czuba, Michał</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lorenc, Augustyn</au><au>Szarata, Jakub</au><au>Czuba, Michał</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Real-Time Location System (RTLS) Based on the Bluetooth Technology for Internal Logistics</atitle><jtitle>Sustainability</jtitle><date>2023-03-01</date><risdate>2023</risdate><volume>15</volume><issue>6</issue><spage>4976</spage><pages>4976-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>The problem of object localization in indoor environments is very important in order to make a company effective and to detect disruption in the logistics system in real-time. 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ESP32 has a problem with detecting devices in a short time, as just two transmitters can be detected in 4–8 s time window. Localisation precision depends on the distance between transmitter and receiver, and the angle from the axis of the directional antenna. For the distance of 10 m the measurement error is 1.2–6.1 m, whilst for the distance of 40 m the measurement error is 4.9 to 24.6 m. Using a Kalman filter can reduce the localization error to 1.5 m.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/su15064976</doi><orcidid>https://orcid.org/0000-0002-2109-2165</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Algorithms Automation Bluetooth Bluetooth technology Clinics Data processing Directional antennas Disruption Error analysis Global positioning systems GPS Indoor environments Infrastructure Internet of Things Kalman filtering Kalman filters Localization Logistics Productivity R&D Radio frequency identification Radio waves Real time Receivers & amplifiers Research & development Research methodology Severe acute respiratory syndrome coronavirus 2 Signal processing Signal stabilization Signal strength Transmitters |
title | Real-Time Location System (RTLS) Based on the Bluetooth Technology for Internal Logistics |
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