Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms
Abstract With the rapid advancement of the Internet of Things and the popularization of mobile Internet-based applications, the location-based service (LBS) has attracted much attention from commercial developers and researchers. Received signal strength indicator (RSSI)-based indoor localization te...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-02-01
|
| Series: | Journal of Electrical Systems and Information Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43067-024-00138-0 |
| _version_ | 1827328240022192128 |
|---|---|
| author | M. W. P. Maduranga Valmik Tilwari Ruvan Abeysekera |
| author_facet | M. W. P. Maduranga Valmik Tilwari Ruvan Abeysekera |
| author_sort | M. W. P. Maduranga |
| collection | DOAJ |
| description | Abstract With the rapid advancement of the Internet of Things and the popularization of mobile Internet-based applications, the location-based service (LBS) has attracted much attention from commercial developers and researchers. Received signal strength indicator (RSSI)-based indoor localization technology has irreplaceable advantages for many LBS applications. However, due to multipath fading, noise, and the limited dynamic range of the RSSI measurements, precise localization based on a path-loss model and multiliterate becomes highly challenging. Therefore, this study proposes a machine learning (ML)-based improved RSSI-based indoor localization approach in which RSSI data is first augmented and then classified using ML algorithms. In addition, we implement an experimental testbed to collect the RSSI value based on Wi-Fi using various reference and target nodes. The received RSSI measurements undergo pre-processing using pseudo-linear solution techniques for closed-form solutions, approximating the original system of nonlinear RSSI measurement equations with a system of linear equations. Finally, the RSSI measurement are trained using ML models such as linear regression, polynomial regression, support vector regression, random forest regression, and decision tree regression. Consequently, the experimental results express in terms of root mean square error and coefficient of determinant compared with various machine learning models with hyper-parameter tuning. |
| first_indexed | 2024-03-07T15:14:45Z |
| format | Article |
| id | doaj.art-294d7a34dfa74ecf9f49d7f39de1ccd8 |
| institution | Directory Open Access Journal |
| issn | 2314-7172 |
| language | English |
| last_indexed | 2024-03-07T15:14:45Z |
| publishDate | 2024-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Electrical Systems and Information Technology |
| spelling | doaj.art-294d7a34dfa74ecf9f49d7f39de1ccd82024-03-05T17:58:06ZengSpringerOpenJournal of Electrical Systems and Information Technology2314-71722024-02-0111112010.1186/s43067-024-00138-0Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithmsM. W. P. Maduranga0Valmik Tilwari1Ruvan Abeysekera2IIC University of TechnologySchool of Electrical Engineering, Korea UniversityIIC University of TechnologyAbstract With the rapid advancement of the Internet of Things and the popularization of mobile Internet-based applications, the location-based service (LBS) has attracted much attention from commercial developers and researchers. Received signal strength indicator (RSSI)-based indoor localization technology has irreplaceable advantages for many LBS applications. However, due to multipath fading, noise, and the limited dynamic range of the RSSI measurements, precise localization based on a path-loss model and multiliterate becomes highly challenging. Therefore, this study proposes a machine learning (ML)-based improved RSSI-based indoor localization approach in which RSSI data is first augmented and then classified using ML algorithms. In addition, we implement an experimental testbed to collect the RSSI value based on Wi-Fi using various reference and target nodes. The received RSSI measurements undergo pre-processing using pseudo-linear solution techniques for closed-form solutions, approximating the original system of nonlinear RSSI measurement equations with a system of linear equations. Finally, the RSSI measurement are trained using ML models such as linear regression, polynomial regression, support vector regression, random forest regression, and decision tree regression. Consequently, the experimental results express in terms of root mean square error and coefficient of determinant compared with various machine learning models with hyper-parameter tuning.https://doi.org/10.1186/s43067-024-00138-0Internet of ThingsIndoor localizationMachine learningReceived signal strength indicatorArtificial intelligence |
| spellingShingle | M. W. P. Maduranga Valmik Tilwari Ruvan Abeysekera Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms Journal of Electrical Systems and Information Technology Internet of Things Indoor localization Machine learning Received signal strength indicator Artificial intelligence |
| title | Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms |
| title_full | Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms |
| title_fullStr | Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms |
| title_full_unstemmed | Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms |
| title_short | Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms |
| title_sort | improved rssi based indoor localization by using pseudo linear solution with machine learning algorithms |
| topic | Internet of Things Indoor localization Machine learning Received signal strength indicator Artificial intelligence |
| url | https://doi.org/10.1186/s43067-024-00138-0 |
| work_keys_str_mv | AT mwpmaduranga improvedrssibasedindoorlocalizationbyusingpseudolinearsolutionwithmachinelearningalgorithms AT valmiktilwari improvedrssibasedindoorlocalizationbyusingpseudolinearsolutionwithmachinelearningalgorithms AT ruvanabeysekera improvedrssibasedindoorlocalizationbyusingpseudolinearsolutionwithmachinelearningalgorithms |