Adaptive Scheme of Denoising Autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE Environment
In indoor environments, estimating localization using a received signal strength indicator (RSSI) is difficult because of the noise from signals reflected and refracted by walls and obstacles. In this study, we used a denoising autoencoder (DAE) to remove noise in the RSSI of Bluetooth Low Energy (B...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/12/5544 |
| _version_ | 1797592743383400448 |
|---|---|
| author | Kyuri Kim Jaeho Lee |
| author_facet | Kyuri Kim Jaeho Lee |
| author_sort | Kyuri Kim |
| collection | DOAJ |
| description | In indoor environments, estimating localization using a received signal strength indicator (RSSI) is difficult because of the noise from signals reflected and refracted by walls and obstacles. In this study, we used a denoising autoencoder (DAE) to remove noise in the RSSI of Bluetooth Low Energy (BLE) signals to improve localization performance. In addition, it is known that the signal of an RSSI can be exponentially aggravated when the noise is increased proportionally to the square of the distance increment. Based on the problem, to effectively remove the noise by adapting this characteristic, we proposed adaptive noise generation schemes to train the DAE model to reflect the characteristics in which the signal-to-noise ratio (SNR) considerably increases as the distance between the terminal and beacon increases. We compared the model’s performance with that of Gaussian noise and other localization algorithms. The results showed an accuracy of 72.6%, a 10.2% improvement over the model with Gaussian noise. Furthermore, our model outperformed the Kalman filter in terms of denoising. |
| first_indexed | 2024-03-11T01:57:27Z |
| format | Article |
| id | doaj.art-e3acfb91ff734705b2a92dfa86cbfef5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:57:27Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e3acfb91ff734705b2a92dfa86cbfef52023-11-18T12:32:35ZengMDPI AGSensors1424-82202023-06-012312554410.3390/s23125544Adaptive Scheme of Denoising Autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE EnvironmentKyuri Kim0Jaeho Lee1Department of IT Media Engineering, Duksung Women’s University, Seoul 01369, Republic of KoreaDepartment of Software, Duksung Women’s University, Seoul 01369, Republic of KoreaIn indoor environments, estimating localization using a received signal strength indicator (RSSI) is difficult because of the noise from signals reflected and refracted by walls and obstacles. In this study, we used a denoising autoencoder (DAE) to remove noise in the RSSI of Bluetooth Low Energy (BLE) signals to improve localization performance. In addition, it is known that the signal of an RSSI can be exponentially aggravated when the noise is increased proportionally to the square of the distance increment. Based on the problem, to effectively remove the noise by adapting this characteristic, we proposed adaptive noise generation schemes to train the DAE model to reflect the characteristics in which the signal-to-noise ratio (SNR) considerably increases as the distance between the terminal and beacon increases. We compared the model’s performance with that of Gaussian noise and other localization algorithms. The results showed an accuracy of 72.6%, a 10.2% improvement over the model with Gaussian noise. Furthermore, our model outperformed the Kalman filter in terms of denoising.https://www.mdpi.com/1424-8220/23/12/5544RSSIindoor localizationneural networksdenoising autoencoder |
| spellingShingle | Kyuri Kim Jaeho Lee Adaptive Scheme of Denoising Autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE Environment Sensors RSSI indoor localization neural networks denoising autoencoder |
| title | Adaptive Scheme of Denoising Autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE Environment |
| title_full | Adaptive Scheme of Denoising Autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE Environment |
| title_fullStr | Adaptive Scheme of Denoising Autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE Environment |
| title_full_unstemmed | Adaptive Scheme of Denoising Autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE Environment |
| title_short | Adaptive Scheme of Denoising Autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE Environment |
| title_sort | adaptive scheme of denoising autoencoder for estimating indoor localization based on rssi analytics in ble environment |
| topic | RSSI indoor localization neural networks denoising autoencoder |
| url | https://www.mdpi.com/1424-8220/23/12/5544 |
| work_keys_str_mv | AT kyurikim adaptiveschemeofdenoisingautoencoderforestimatingindoorlocalizationbasedonrssianalyticsinbleenvironment AT jaeholee adaptiveschemeofdenoisingautoencoderforestimatingindoorlocalizationbasedonrssianalyticsinbleenvironment |