Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization
Indoor user localization and tracking are instrumental to a broad range of services and applications in the Internet of Things (IoT) and particularly in Body Sensor Networks (BSN) and Ambient Assisted Living (AAL) scenarios. Due to the widespread availability of IEEE 802.11, many localization platfo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/17/4/812 |
| _version_ | 1811187958197256192 |
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| author | Loizos Kanaris Akis Kokkinis Antonio Liotta Stavros Stavrou |
| author_facet | Loizos Kanaris Akis Kokkinis Antonio Liotta Stavros Stavrou |
| author_sort | Loizos Kanaris |
| collection | DOAJ |
| description | Indoor user localization and tracking are instrumental to a broad range of services and applications in the Internet of Things (IoT) and particularly in Body Sensor Networks (BSN) and Ambient Assisted Living (AAL) scenarios. Due to the widespread availability of IEEE 802.11, many localization platforms have been proposed, based on the Wi-Fi Received Signal Strength (RSS) indicator, using algorithms such as K-Nearest Neighbour (KNN), Maximum A Posteriori (MAP) and Minimum Mean Square Error (MMSE). In this paper, we introduce a hybrid method that combines the simplicity (and low cost) of Bluetooth Low Energy (BLE) and the popular 802.11 infrastructure, to improve the accuracy of indoor localization platforms. Building on KNN, we propose a new positioning algorithm (dubbed i-KNN) which is able to filter the initial fingerprint dataset (i.e., the radiomap), after considering the proximity of RSS fingerprints with respect to the BLE devices. In this way, i-KNN provides an optimised small subset of possible user locations, based on which it finally estimates the user position. The proposed methodology achieves fast positioning estimation due to the utilization of a fragment of the initial fingerprint dataset, while at the same time improves positioning accuracy by minimizing any calculation errors. |
| first_indexed | 2024-04-11T14:11:12Z |
| format | Article |
| id | doaj.art-64b6c0f6151e45d9a11ac4aa697dbfd6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T14:11:12Z |
| publishDate | 2017-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-64b6c0f6151e45d9a11ac4aa697dbfd62022-12-22T04:19:41ZengMDPI AGSensors1424-82202017-04-0117481210.3390/s17040812s17040812Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor LocalizationLoizos Kanaris0Akis Kokkinis1Antonio Liotta2Stavros Stavrou3Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, The NetherlandsDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, The NetherlandsDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, The NetherlandsFaculty of Pure and Applied Sciences, Open University of Cyprus, Nicosia 2252, CyprusIndoor user localization and tracking are instrumental to a broad range of services and applications in the Internet of Things (IoT) and particularly in Body Sensor Networks (BSN) and Ambient Assisted Living (AAL) scenarios. Due to the widespread availability of IEEE 802.11, many localization platforms have been proposed, based on the Wi-Fi Received Signal Strength (RSS) indicator, using algorithms such as K-Nearest Neighbour (KNN), Maximum A Posteriori (MAP) and Minimum Mean Square Error (MMSE). In this paper, we introduce a hybrid method that combines the simplicity (and low cost) of Bluetooth Low Energy (BLE) and the popular 802.11 infrastructure, to improve the accuracy of indoor localization platforms. Building on KNN, we propose a new positioning algorithm (dubbed i-KNN) which is able to filter the initial fingerprint dataset (i.e., the radiomap), after considering the proximity of RSS fingerprints with respect to the BLE devices. In this way, i-KNN provides an optimised small subset of possible user locations, based on which it finally estimates the user position. The proposed methodology achieves fast positioning estimation due to the utilization of a fragment of the initial fingerprint dataset, while at the same time improves positioning accuracy by minimizing any calculation errors.http://www.mdpi.com/1424-8220/17/4/812indoor positioningindoor localizationfingerprintbluetooth low energy (BLE)Internet of Things (IoT)Body Sensor Networks (BSN)positioning algorithms |
| spellingShingle | Loizos Kanaris Akis Kokkinis Antonio Liotta Stavros Stavrou Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization Sensors indoor positioning indoor localization fingerprint bluetooth low energy (BLE) Internet of Things (IoT) Body Sensor Networks (BSN) positioning algorithms |
| title | Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization |
| title_full | Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization |
| title_fullStr | Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization |
| title_full_unstemmed | Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization |
| title_short | Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization |
| title_sort | fusing bluetooth beacon data with wi fi radiomaps for improved indoor localization |
| topic | indoor positioning indoor localization fingerprint bluetooth low energy (BLE) Internet of Things (IoT) Body Sensor Networks (BSN) positioning algorithms |
| url | http://www.mdpi.com/1424-8220/17/4/812 |
| work_keys_str_mv | AT loizoskanaris fusingbluetoothbeacondatawithwifiradiomapsforimprovedindoorlocalization AT akiskokkinis fusingbluetoothbeacondatawithwifiradiomapsforimprovedindoorlocalization AT antonioliotta fusingbluetoothbeacondatawithwifiradiomapsforimprovedindoorlocalization AT stavrosstavrou fusingbluetoothbeacondatawithwifiradiomapsforimprovedindoorlocalization |