Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
The Internet of things (IoT) has significantly impacted various sectors, including healthcare, environmental monitoring, transportation, and commerce, by enhancing communication networks through the integration of sensors, software, and hardware. This paper presents an accurate IoT indoor localizati...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/17/12/544 |
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| author | Farshad Khodamoradi Javad Rezazadeh John Ayoade |
| author_facet | Farshad Khodamoradi Javad Rezazadeh John Ayoade |
| author_sort | Farshad Khodamoradi |
| collection | DOAJ |
| description | The Internet of things (IoT) has significantly impacted various sectors, including healthcare, environmental monitoring, transportation, and commerce, by enhancing communication networks through the integration of sensors, software, and hardware. This paper presents an accurate IoT indoor localization system based on IoT devices and fingerprinting methods. We explore indoor localization techniques using Bluetooth Low Energy (BLE) and a Radio Signal Strength Indicator (RSSI) to address the limitations of GPS in indoor environments. The study evaluates the effectiveness of iBeacon transmitters for indoor positioning, comparing the Weighted Centroid Localization (WCL) and Positive Weighted Centroid Localization (PWCL) algorithms, along with fingerprinting methods enhanced by outlier detection and mapping filters. Our methodology includes mapping a real environment onto a coordinate axis, collecting training data from 47 sampling points, and implementing four localization algorithms. The results show that the PWCL algorithm improves accuracy over the WCL algorithm, and hybrid methods further reduce localization errors. The HYBRID-MAPPED method achieves the highest accuracy, with an average error of 1.44 m. |
| format | Article |
| id | doaj-art-b4b2d11d670d46ffa3e4b314e7253f7a |
| institution | DOAJ |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-b4b2d11d670d46ffa3e4b314e7253f7a2025-08-20T02:57:08ZengMDPI AGAlgorithms1999-48932024-12-01171254410.3390/a17120544Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting MethodsFarshad Khodamoradi0Javad Rezazadeh1John Ayoade2Electronic Tehran Branch, Islamic Azad University, Tehran 1465834311, IranCrown Institute of Higher Education (CIHE), Sydney, NSW 2060, AustraliaCrown Institute of Higher Education (CIHE), Sydney, NSW 2060, AustraliaThe Internet of things (IoT) has significantly impacted various sectors, including healthcare, environmental monitoring, transportation, and commerce, by enhancing communication networks through the integration of sensors, software, and hardware. This paper presents an accurate IoT indoor localization system based on IoT devices and fingerprinting methods. We explore indoor localization techniques using Bluetooth Low Energy (BLE) and a Radio Signal Strength Indicator (RSSI) to address the limitations of GPS in indoor environments. The study evaluates the effectiveness of iBeacon transmitters for indoor positioning, comparing the Weighted Centroid Localization (WCL) and Positive Weighted Centroid Localization (PWCL) algorithms, along with fingerprinting methods enhanced by outlier detection and mapping filters. Our methodology includes mapping a real environment onto a coordinate axis, collecting training data from 47 sampling points, and implementing four localization algorithms. The results show that the PWCL algorithm improves accuracy over the WCL algorithm, and hybrid methods further reduce localization errors. The HYBRID-MAPPED method achieves the highest accuracy, with an average error of 1.44 m.https://www.mdpi.com/1999-4893/17/12/544locationIoTinternal locationfingerprint algorithm |
| spellingShingle | Farshad Khodamoradi Javad Rezazadeh John Ayoade Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods Algorithms location IoT internal location fingerprint algorithm |
| title | Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods |
| title_full | Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods |
| title_fullStr | Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods |
| title_full_unstemmed | Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods |
| title_short | Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods |
| title_sort | accurate indoor localization with iot devices and advanced fingerprinting methods |
| topic | location IoT internal location fingerprint algorithm |
| url | https://www.mdpi.com/1999-4893/17/12/544 |
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