Combining RSSI and Accelerometer Features for Room-Level Localization
The continuing advancements in technology have resulted in an explosion in the use of interconnected devices and sensors. Internet-of-Things (IoT) systems are used to provide remote solutions in different domains, like healthcare and security. A common service offered by IoT systems is the estimatio...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/8/2723 |
| _version_ | 1797537916273033216 |
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| author | Athina Tsanousa Vasileios-Rafail Xefteris Georgios Meditskos Stefanos Vrochidis Ioannis Kompatsiaris |
| author_facet | Athina Tsanousa Vasileios-Rafail Xefteris Georgios Meditskos Stefanos Vrochidis Ioannis Kompatsiaris |
| author_sort | Athina Tsanousa |
| collection | DOAJ |
| description | The continuing advancements in technology have resulted in an explosion in the use of interconnected devices and sensors. Internet-of-Things (IoT) systems are used to provide remote solutions in different domains, like healthcare and security. A common service offered by IoT systems is the estimation of a person’s position in indoor spaces, which is quite often achieved with the exploitation of the Received Signal Strength Indication (RSSI). Localization tasks with the goal to locate the room are actually classification problems. Motivated by a current project, where there is the need to locate a missing child in crowded spaces, we intend to test the added value of using an accelerometer along with RSSI for room-level localization and assess the performance of ensemble learning methods. We present here the results of this preliminary approach of the early and late fusion of RSSI and accelerometer features in room-level localization. We further test the performance of the feature extraction from RSSI values. The classification algorithms and the fusion methods used to predict the room were evaluated using different protocols applied to a public dataset. The experimental results revealed better performance of the RSSI extracted features, while the accelerometer’s individual performance was poor and subsequently affected the fusion results. |
| first_indexed | 2024-03-10T12:23:03Z |
| format | Article |
| id | doaj.art-424b8f5d52de4fbfb12d752dbb1f3568 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T12:23:03Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-424b8f5d52de4fbfb12d752dbb1f35682023-11-21T15:17:56ZengMDPI AGSensors1424-82202021-04-01218272310.3390/s21082723Combining RSSI and Accelerometer Features for Room-Level LocalizationAthina Tsanousa0Vasileios-Rafail Xefteris1Georgios Meditskos2Stefanos Vrochidis3Ioannis Kompatsiaris4Centre for Research and Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi, 57001 Thessaloniki, GreeceCentre for Research and Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi, 57001 Thessaloniki, GreeceCentre for Research and Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi, 57001 Thessaloniki, GreeceCentre for Research and Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi, 57001 Thessaloniki, GreeceCentre for Research and Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi, 57001 Thessaloniki, GreeceThe continuing advancements in technology have resulted in an explosion in the use of interconnected devices and sensors. Internet-of-Things (IoT) systems are used to provide remote solutions in different domains, like healthcare and security. A common service offered by IoT systems is the estimation of a person’s position in indoor spaces, which is quite often achieved with the exploitation of the Received Signal Strength Indication (RSSI). Localization tasks with the goal to locate the room are actually classification problems. Motivated by a current project, where there is the need to locate a missing child in crowded spaces, we intend to test the added value of using an accelerometer along with RSSI for room-level localization and assess the performance of ensemble learning methods. We present here the results of this preliminary approach of the early and late fusion of RSSI and accelerometer features in room-level localization. We further test the performance of the feature extraction from RSSI values. The classification algorithms and the fusion methods used to predict the room were evaluated using different protocols applied to a public dataset. The experimental results revealed better performance of the RSSI extracted features, while the accelerometer’s individual performance was poor and subsequently affected the fusion results.https://www.mdpi.com/1424-8220/21/8/2723room-level localizationRSSIinertial sensorsfusion |
| spellingShingle | Athina Tsanousa Vasileios-Rafail Xefteris Georgios Meditskos Stefanos Vrochidis Ioannis Kompatsiaris Combining RSSI and Accelerometer Features for Room-Level Localization Sensors room-level localization RSSI inertial sensors fusion |
| title | Combining RSSI and Accelerometer Features for Room-Level Localization |
| title_full | Combining RSSI and Accelerometer Features for Room-Level Localization |
| title_fullStr | Combining RSSI and Accelerometer Features for Room-Level Localization |
| title_full_unstemmed | Combining RSSI and Accelerometer Features for Room-Level Localization |
| title_short | Combining RSSI and Accelerometer Features for Room-Level Localization |
| title_sort | combining rssi and accelerometer features for room level localization |
| topic | room-level localization RSSI inertial sensors fusion |
| url | https://www.mdpi.com/1424-8220/21/8/2723 |
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