Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information
Abstract The trajectory localisation of human activities using signal analytics has become a reality due to the widespread use of advanced signal processing systems. Device‐free localisation using WiFi devices is prevalent, and the received signal strength indicator (RSSI) and channel state informat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-10-01
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| Series: | IET Wireless Sensor Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/wss2.12065 |
| _version_ | 1797663903464816640 |
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| author | Fahd Abuhoureyah Wong Yan Chiew Ahmad Sadhiqin Bin Mohd Isira Mohammed Al‐Andoli |
| author_facet | Fahd Abuhoureyah Wong Yan Chiew Ahmad Sadhiqin Bin Mohd Isira Mohammed Al‐Andoli |
| author_sort | Fahd Abuhoureyah |
| collection | DOAJ |
| description | Abstract The trajectory localisation of human activities using signal analytics has become a reality due to the widespread use of advanced signal processing systems. Device‐free localisation using WiFi devices is prevalent, and the received signal strength indicator (RSSI) and channel state information (CSI) signals offer additional benefits. However, radio frequency (RF) localisation is highly dependent on the environment, so updating fingerprint data is necessary by changing the environment. This work presents Fine‐grained Indoor Detection and Angular Radar for recognising and locating humans using a multipath trajectory reflections system that does not require training. It estimates location using a probabilistic approach that considers changes in CSI and RSSI across multiple nodes, generating an informative dataset that reflects the current trajectory and status of the location. The presented method extracts data from clustered Raspberry Pi 4B and Nexmon. The method exhibits a versatile real‐time location‐tracking solution by utilising the distinctive properties of RF signals. This technology has significant implications for various applications, including human medical monitoring, gaming, smart cities, and optimising building layouts to improve efficiency. The model demonstrates location‐independent localisation with up to 80% accuracy in mapping trajectories at any location. The findings indicate that the proposed model is effective and reliable for indoor localisation and activity tracking, making it a promising solution for implementation in real‐world environments. |
| first_indexed | 2024-03-11T19:21:32Z |
| format | Article |
| id | doaj.art-d66096f55d60424e9885e9ffa16cd41c |
| institution | Directory Open Access Journal |
| issn | 2043-6386 2043-6394 |
| language | English |
| last_indexed | 2024-03-11T19:21:32Z |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Wireless Sensor Systems |
| spelling | doaj.art-d66096f55d60424e9885e9ffa16cd41c2023-10-07T05:50:12ZengWileyIET Wireless Sensor Systems2043-63862043-63942023-10-0113516317710.1049/wss2.12065Free device location independent WiFi‐based localisation using received signal strength indicator and channel state informationFahd Abuhoureyah0Wong Yan Chiew1Ahmad Sadhiqin Bin Mohd Isira2Mohammed Al‐Andoli3Centre for Telecommunication Research and Innovation (CeTRI) Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka MalaysiaCentre for Telecommunication Research and Innovation (CeTRI) Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka MalaysiaCentre for Telecommunication Research and Innovation (CeTRI) Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka MalaysiaFaculty of Engineering and Technology Multimedia University – MMU Melaka MalaysiaAbstract The trajectory localisation of human activities using signal analytics has become a reality due to the widespread use of advanced signal processing systems. Device‐free localisation using WiFi devices is prevalent, and the received signal strength indicator (RSSI) and channel state information (CSI) signals offer additional benefits. However, radio frequency (RF) localisation is highly dependent on the environment, so updating fingerprint data is necessary by changing the environment. This work presents Fine‐grained Indoor Detection and Angular Radar for recognising and locating humans using a multipath trajectory reflections system that does not require training. It estimates location using a probabilistic approach that considers changes in CSI and RSSI across multiple nodes, generating an informative dataset that reflects the current trajectory and status of the location. The presented method extracts data from clustered Raspberry Pi 4B and Nexmon. The method exhibits a versatile real‐time location‐tracking solution by utilising the distinctive properties of RF signals. This technology has significant implications for various applications, including human medical monitoring, gaming, smart cities, and optimising building layouts to improve efficiency. The model demonstrates location‐independent localisation with up to 80% accuracy in mapping trajectories at any location. The findings indicate that the proposed model is effective and reliable for indoor localisation and activity tracking, making it a promising solution for implementation in real‐world environments.https://doi.org/10.1049/wss2.12065body sensor networksintelligent sensorssignal detection |
| spellingShingle | Fahd Abuhoureyah Wong Yan Chiew Ahmad Sadhiqin Bin Mohd Isira Mohammed Al‐Andoli Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information IET Wireless Sensor Systems body sensor networks intelligent sensors signal detection |
| title | Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information |
| title_full | Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information |
| title_fullStr | Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information |
| title_full_unstemmed | Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information |
| title_short | Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information |
| title_sort | free device location independent wifi based localisation using received signal strength indicator and channel state information |
| topic | body sensor networks intelligent sensors signal detection |
| url | https://doi.org/10.1049/wss2.12065 |
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