Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks
Localization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML) algorithms have been widely applied in various WS...
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MDPI AG
2024-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/19/6314 |
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| author | Jehan Esheh Sofiene Affes |
| author_facet | Jehan Esheh Sofiene Affes |
| author_sort | Jehan Esheh |
| collection | DOAJ |
| description | Localization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML) algorithms have been widely applied in various WSN-based tasks, their effectiveness is often compromised by limited training data, leading to issues such as overfitting and reduced accuracy, especially when the number of sensor nodes is low. A key strategy to mitigate overfitting involves increasing both the quantity and diversity of the training data. To address the limitations posed by small datasets, this paper proposes an intelligent data augmentation strategy (DAS)-based deep neural network (DNN) that enhances the localization accuracy of WSNs. The proposed DAS replicates the estimated positions of unknown nodes generated by the Dv-hop algorithm and introduces Gaussian noise to these replicated positions, creating multiple modified datasets. By combining the modified datasets with the original training data, we significantly increase the dataset size, which leads to a substantial reduction in normalized root mean square error (NRMSE). The experimental results demonstrate that this data augmentation technique significantly improves the performance of DNNs compared to the traditional Dv-hop algorithm at a low number of nodes while maintaining an efficient computational cost for data augmentation. Therefore, the proposed method provides a scalable and effective solution for enhancing the localization accuracy of WSNs. |
| format | Article |
| id | doaj-art-4adfd5d33f184d65baaf62ea09e0fdde |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-4adfd5d33f184d65baaf62ea09e0fdde2025-08-20T01:47:38ZengMDPI AGSensors1424-82202024-09-012419631410.3390/s24196314Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural NetworksJehan Esheh0Sofiene Affes1EMT Centre (Energy, Materials and Telecommunications), INRS (Institut National de la Recherche Scientifique), Université du Québec, Montréal, QC H5A 1K6, CanadaEMT Centre (Energy, Materials and Telecommunications), INRS (Institut National de la Recherche Scientifique), Université du Québec, Montréal, QC H5A 1K6, CanadaLocalization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML) algorithms have been widely applied in various WSN-based tasks, their effectiveness is often compromised by limited training data, leading to issues such as overfitting and reduced accuracy, especially when the number of sensor nodes is low. A key strategy to mitigate overfitting involves increasing both the quantity and diversity of the training data. To address the limitations posed by small datasets, this paper proposes an intelligent data augmentation strategy (DAS)-based deep neural network (DNN) that enhances the localization accuracy of WSNs. The proposed DAS replicates the estimated positions of unknown nodes generated by the Dv-hop algorithm and introduces Gaussian noise to these replicated positions, creating multiple modified datasets. By combining the modified datasets with the original training data, we significantly increase the dataset size, which leads to a substantial reduction in normalized root mean square error (NRMSE). The experimental results demonstrate that this data augmentation technique significantly improves the performance of DNNs compared to the traditional Dv-hop algorithm at a low number of nodes while maintaining an efficient computational cost for data augmentation. Therefore, the proposed method provides a scalable and effective solution for enhancing the localization accuracy of WSNs.https://www.mdpi.com/1424-8220/24/19/6314data replicationdeep neural networkswireless sensor networksrange-free localizationdata augmentation |
| spellingShingle | Jehan Esheh Sofiene Affes Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks Sensors data replication deep neural networks wireless sensor networks range-free localization data augmentation |
| title | Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks |
| title_full | Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks |
| title_fullStr | Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks |
| title_full_unstemmed | Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks |
| title_short | Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks |
| title_sort | improving localization in wireless sensor networks for the internet of things using data replication based deep neural networks |
| topic | data replication deep neural networks wireless sensor networks range-free localization data augmentation |
| url | https://www.mdpi.com/1424-8220/24/19/6314 |
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