Dataset for Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning
The dataset titled "Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning" was collected to support the development of an indoor localization system that operates at the room level. The dataset includes measurements of Recei...
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creator | Baejah Nur Ahmadi Rahmat Mulyawan Trio Adiono |
description | The dataset titled "Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning" was collected to support the development of an indoor localization system that operates at the room level. The dataset includes measurements of Received Signal Strength Indication (RSSI) from Bluetooth Low Energy (BLE) beacons (specifically the iBKS105 model) recorded by an ESP32 device. These RSSI values were captured across various rooms, allowing for precise localization within an indoor environment. The dataset is particularly useful for research in indoor localization system including machine learning-based localization algorithms. |
doi_str_mv | 10.5281/zenodo.13317045 |
format | Dataset |
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The dataset includes measurements of Received Signal Strength Indication (RSSI) from Bluetooth Low Energy (BLE) beacons (specifically the iBKS105 model) recorded by an ESP32 device. These RSSI values were captured across various rooms, allowing for precise localization within an indoor environment. 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The dataset includes measurements of Received Signal Strength Indication (RSSI) from Bluetooth Low Energy (BLE) beacons (specifically the iBKS105 model) recorded by an ESP32 device. These RSSI values were captured across various rooms, allowing for precise localization within an indoor environment. The dataset is particularly useful for research in indoor localization system including machine learning-based localization algorithms.</description><fulltext>true</fulltext><rsrctype>dataset</rsrctype><creationdate>2024</creationdate><recordtype>dataset</recordtype><sourceid>PQ8</sourceid><recordid>eNqVjz1PAzEMhrMwIOjM6rFI_bj0WsF89CqQjoVW6hi5idtGytkoF0DXlT9OUMsPYHrlV34sP0rd6WKymD3q6YlYnEx0WeqHYr64Vt9LTNhRgr1EeCMM441vCV7YSS4asRj8CZMXhnXfJWqhyusO8rwljLgLBEv69JZGUIUPSiLpmLkvqJnioYdh1dT3UBFa4W4EyA5e0R49EzT5AHs-3KqrPYaOBpe8UdNVvXl6Hrv8m_WJzHv0Lcbe6ML8apizhvnTKP9P_AB6q1fK</recordid><startdate>20240814</startdate><enddate>20240814</enddate><creator>Baejah</creator><creator>Nur Ahmadi</creator><creator>Rahmat Mulyawan</creator><creator>Trio Adiono</creator><general>Zenodo</general><scope>DYCCY</scope><scope>PQ8</scope><orcidid>https://orcid.org/0000-0002-0105-8349</orcidid></search><sort><creationdate>20240814</creationdate><title>Dataset for Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning</title><author>Baejah ; Nur Ahmadi ; Rahmat Mulyawan ; Trio Adiono</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-datacite_primary_10_5281_zenodo_133170453</frbrgroupid><rsrctype>datasets</rsrctype><prefilter>datasets</prefilter><language>eng</language><creationdate>2024</creationdate><toplevel>online_resources</toplevel><creatorcontrib>Baejah</creatorcontrib><creatorcontrib>Nur Ahmadi</creatorcontrib><creatorcontrib>Rahmat Mulyawan</creatorcontrib><creatorcontrib>Trio Adiono</creatorcontrib><collection>DataCite (Open Access)</collection><collection>DataCite</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Baejah</au><au>Nur Ahmadi</au><au>Rahmat Mulyawan</au><au>Trio Adiono</au><format>book</format><genre>unknown</genre><ristype>DATA</ristype><title>Dataset for Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning</title><date>2024-08-14</date><risdate>2024</risdate><abstract>The dataset titled "Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning" was collected to support the development of an indoor localization system that operates at the room level. The dataset includes measurements of Received Signal Strength Indication (RSSI) from Bluetooth Low Energy (BLE) beacons (specifically the iBKS105 model) recorded by an ESP32 device. These RSSI values were captured across various rooms, allowing for precise localization within an indoor environment. The dataset is particularly useful for research in indoor localization system including machine learning-based localization algorithms.</abstract><pub>Zenodo</pub><doi>10.5281/zenodo.13317045</doi><orcidid>https://orcid.org/0000-0002-0105-8349</orcidid><oa>free_for_read</oa></addata></record> |
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identifier | DOI: 10.5281/zenodo.13317045 |
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title | Dataset for Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning |
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