Enhancing Localization Accuracy and Reducing Processing Time in Indoor Positioning Systems: A Comparative Analysis of AI Models
This paper presents a comparative study of different AI models for indoor positioning systems, emphasizing improvements in localization accuracy and processing time. This study examines Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and the Kalman...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/475 |
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| author | Salwa Sahnoun Rihab Souissi Sirine Chiboub Aziza Chabchoub Mohamed Khalil Baazaoui Ahmed Fakhfakh Faouzi Derbel |
| author_facet | Salwa Sahnoun Rihab Souissi Sirine Chiboub Aziza Chabchoub Mohamed Khalil Baazaoui Ahmed Fakhfakh Faouzi Derbel |
| author_sort | Salwa Sahnoun |
| collection | DOAJ |
| description | This paper presents a comparative study of different AI models for indoor positioning systems, emphasizing improvements in localization accuracy and processing time. This study examines Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and the Kalman filter using a real Received Signal Strength Indicator (RSSI) and 9-axis ICM-20948 sensor. An in-depth analysis is provided in this paper for data cleaning and feature selection to reduce errors for all the models. We evaluate these models in terms of localization accuracy and prediction time. The RNN model shows the best performance, achieving a localization error of 0.247 m with a delay of 0.077 s per position location for an area of 12 m × 9.5 m using four anchors. This research highlights the importance of selecting AI models for effective mobile tracking according to test and validation data. |
| format | Article |
| id | doaj-art-7ce5fca6df6c484981dfb94ed08f40fb |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-7ce5fca6df6c484981dfb94ed08f40fb2025-01-24T13:49:04ZengMDPI AGSensors1424-82202025-01-0125247510.3390/s25020475Enhancing Localization Accuracy and Reducing Processing Time in Indoor Positioning Systems: A Comparative Analysis of AI ModelsSalwa Sahnoun0Rihab Souissi1Sirine Chiboub2Aziza Chabchoub3Mohamed Khalil Baazaoui4Ahmed Fakhfakh5Faouzi Derbel6Laboratory of Signals, Systems, Artificial Intelligence and Networks (SM@RTS), Digital Research Center of Sfax (CRNS), Sfax University, Sfax 3021, TunisiaLaboratory of Signals, Systems, Artificial Intelligence and Networks (SM@RTS), Digital Research Center of Sfax (CRNS), Sfax University, Sfax 3021, TunisiaLaboratory of Signals, Systems, Artificial Intelligence and Networks (SM@RTS), Digital Research Center of Sfax (CRNS), Sfax University, Sfax 3021, TunisiaLaboratory of Signals, Systems, Artificial Intelligence and Networks (SM@RTS), Digital Research Center of Sfax (CRNS), Sfax University, Sfax 3021, TunisiaLaboratory of Signals, Systems, Artificial Intelligence and Networks (SM@RTS), Digital Research Center of Sfax (CRNS), Sfax University, Sfax 3021, TunisiaLaboratory of Signals, Systems, Artificial Intelligence and Networks (SM@RTS), Digital Research Center of Sfax (CRNS), Sfax University, Sfax 3021, TunisiaSmart Diagnostic and Online Monitoring, Leipzig University of Applied Sciences, Wächterstraße 13, 04107 Leipzig, GermanyThis paper presents a comparative study of different AI models for indoor positioning systems, emphasizing improvements in localization accuracy and processing time. This study examines Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and the Kalman filter using a real Received Signal Strength Indicator (RSSI) and 9-axis ICM-20948 sensor. An in-depth analysis is provided in this paper for data cleaning and feature selection to reduce errors for all the models. We evaluate these models in terms of localization accuracy and prediction time. The RNN model shows the best performance, achieving a localization error of 0.247 m with a delay of 0.077 s per position location for an area of 12 m × 9.5 m using four anchors. This research highlights the importance of selecting AI models for effective mobile tracking according to test and validation data.https://www.mdpi.com/1424-8220/25/2/475indoor positioning systemsAIneural networksANNRNNLSTM |
| spellingShingle | Salwa Sahnoun Rihab Souissi Sirine Chiboub Aziza Chabchoub Mohamed Khalil Baazaoui Ahmed Fakhfakh Faouzi Derbel Enhancing Localization Accuracy and Reducing Processing Time in Indoor Positioning Systems: A Comparative Analysis of AI Models Sensors indoor positioning systems AI neural networks ANN RNN LSTM |
| title | Enhancing Localization Accuracy and Reducing Processing Time in Indoor Positioning Systems: A Comparative Analysis of AI Models |
| title_full | Enhancing Localization Accuracy and Reducing Processing Time in Indoor Positioning Systems: A Comparative Analysis of AI Models |
| title_fullStr | Enhancing Localization Accuracy and Reducing Processing Time in Indoor Positioning Systems: A Comparative Analysis of AI Models |
| title_full_unstemmed | Enhancing Localization Accuracy and Reducing Processing Time in Indoor Positioning Systems: A Comparative Analysis of AI Models |
| title_short | Enhancing Localization Accuracy and Reducing Processing Time in Indoor Positioning Systems: A Comparative Analysis of AI Models |
| title_sort | enhancing localization accuracy and reducing processing time in indoor positioning systems a comparative analysis of ai models |
| topic | indoor positioning systems AI neural networks ANN RNN LSTM |
| url | https://www.mdpi.com/1424-8220/25/2/475 |
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