3-Dimensional Manifold and Machine Learning Based Localization Algorithm for Wireless Sensor Networks
Abstract The minimized amount of Localization accuracy is one of the common issues in Wireless sensor networks. The determination of unknown nodes in a network needs good localization approach. This paper proposes a 3-dimensional Manifold and Machine Learning based Localization algorithm for providi...
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| Autor*in: |
Robinson, Y. Harold [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Wireless personal communications - Springer US, 1994, 127(2021), 1 vom: 11. März, Seite 523-541 |
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volume:127 ; year:2021 ; number:1 ; day:11 ; month:03 ; pages:523-541 |
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| DOI / URN: |
10.1007/s11277-021-08291-9 |
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| 520 | |a Abstract The minimized amount of Localization accuracy is one of the common issues in Wireless sensor networks. The determination of unknown nodes in a network needs good localization approach. This paper proposes a 3-dimensional Manifold and Machine Learning based Localization algorithm for providing the solution to the localization problem. The Machine Learning uses to identify the faulty nodes in the network for better efficiency and computes the optimal solution to the real-time localization problems in WSNs. The mobility model is deployed within the sensor node and the sensor node is computed to estimate the position of the sensor node. This technique is utilized to identify the position of the unknown nodes according to the transmission range. Machine Learning technique utilizes to identify the faulty nodes from the sensor nodes for obtaining the maximum efficiency. RMSE is used to measure the errors for providing better accuracy and also increase the level of quantization for WSN localization approach. The simulation results prove that the proposed technique has high accuracy, reduced energy consumption compared with the relevant techniques. | ||
| 650 | 4 | |a Localization | |
| 650 | 4 | |a Manifold | |
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| 650 | 4 | |a Accuracy | |
| 650 | 4 | |a Energy consumption | |
| 650 | 4 | |a Machine learning | |
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| 700 | 1 | |a Julie, E. Golden |4 aut | |
| 700 | 1 | |a Lakshmi Narayanan, K. |4 aut | |
| 700 | 1 | |a Rho, Seungmin |4 aut | |
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10.1007/s11277-021-08291-9 doi (DE-627)OLC2080158341 (DE-He213)s11277-021-08291-9-p DE-627 ger DE-627 rakwb eng 620 VZ Robinson, Y. Harold verfasserin aut 3-Dimensional Manifold and Machine Learning Based Localization Algorithm for Wireless Sensor Networks 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The minimized amount of Localization accuracy is one of the common issues in Wireless sensor networks. The determination of unknown nodes in a network needs good localization approach. This paper proposes a 3-dimensional Manifold and Machine Learning based Localization algorithm for providing the solution to the localization problem. The Machine Learning uses to identify the faulty nodes in the network for better efficiency and computes the optimal solution to the real-time localization problems in WSNs. The mobility model is deployed within the sensor node and the sensor node is computed to estimate the position of the sensor node. This technique is utilized to identify the position of the unknown nodes according to the transmission range. Machine Learning technique utilizes to identify the faulty nodes from the sensor nodes for obtaining the maximum efficiency. RMSE is used to measure the errors for providing better accuracy and also increase the level of quantization for WSN localization approach. The simulation results prove that the proposed technique has high accuracy, reduced energy consumption compared with the relevant techniques. Localization Manifold Transmission range Accuracy Energy consumption Machine learning Vimal, S. aut Julie, E. Golden aut Lakshmi Narayanan, K. aut Rho, Seungmin aut Enthalten in Wireless personal communications Springer US, 1994 127(2021), 1 vom: 11. März, Seite 523-541 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:127 year:2021 number:1 day:11 month:03 pages:523-541 https://doi.org/10.1007/s11277-021-08291-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW AR 127 2021 1 11 03 523-541 |
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10.1007/s11277-021-08291-9 doi (DE-627)OLC2080158341 (DE-He213)s11277-021-08291-9-p DE-627 ger DE-627 rakwb eng 620 VZ Robinson, Y. Harold verfasserin aut 3-Dimensional Manifold and Machine Learning Based Localization Algorithm for Wireless Sensor Networks 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The minimized amount of Localization accuracy is one of the common issues in Wireless sensor networks. The determination of unknown nodes in a network needs good localization approach. This paper proposes a 3-dimensional Manifold and Machine Learning based Localization algorithm for providing the solution to the localization problem. The Machine Learning uses to identify the faulty nodes in the network for better efficiency and computes the optimal solution to the real-time localization problems in WSNs. The mobility model is deployed within the sensor node and the sensor node is computed to estimate the position of the sensor node. This technique is utilized to identify the position of the unknown nodes according to the transmission range. Machine Learning technique utilizes to identify the faulty nodes from the sensor nodes for obtaining the maximum efficiency. RMSE is used to measure the errors for providing better accuracy and also increase the level of quantization for WSN localization approach. The simulation results prove that the proposed technique has high accuracy, reduced energy consumption compared with the relevant techniques. Localization Manifold Transmission range Accuracy Energy consumption Machine learning Vimal, S. aut Julie, E. Golden aut Lakshmi Narayanan, K. aut Rho, Seungmin aut Enthalten in Wireless personal communications Springer US, 1994 127(2021), 1 vom: 11. März, Seite 523-541 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:127 year:2021 number:1 day:11 month:03 pages:523-541 https://doi.org/10.1007/s11277-021-08291-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW AR 127 2021 1 11 03 523-541 |
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10.1007/s11277-021-08291-9 doi (DE-627)OLC2080158341 (DE-He213)s11277-021-08291-9-p DE-627 ger DE-627 rakwb eng 620 VZ Robinson, Y. Harold verfasserin aut 3-Dimensional Manifold and Machine Learning Based Localization Algorithm for Wireless Sensor Networks 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The minimized amount of Localization accuracy is one of the common issues in Wireless sensor networks. The determination of unknown nodes in a network needs good localization approach. This paper proposes a 3-dimensional Manifold and Machine Learning based Localization algorithm for providing the solution to the localization problem. The Machine Learning uses to identify the faulty nodes in the network for better efficiency and computes the optimal solution to the real-time localization problems in WSNs. The mobility model is deployed within the sensor node and the sensor node is computed to estimate the position of the sensor node. This technique is utilized to identify the position of the unknown nodes according to the transmission range. Machine Learning technique utilizes to identify the faulty nodes from the sensor nodes for obtaining the maximum efficiency. RMSE is used to measure the errors for providing better accuracy and also increase the level of quantization for WSN localization approach. The simulation results prove that the proposed technique has high accuracy, reduced energy consumption compared with the relevant techniques. Localization Manifold Transmission range Accuracy Energy consumption Machine learning Vimal, S. aut Julie, E. Golden aut Lakshmi Narayanan, K. aut Rho, Seungmin aut Enthalten in Wireless personal communications Springer US, 1994 127(2021), 1 vom: 11. März, Seite 523-541 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:127 year:2021 number:1 day:11 month:03 pages:523-541 https://doi.org/10.1007/s11277-021-08291-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW AR 127 2021 1 11 03 523-541 |
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10.1007/s11277-021-08291-9 doi (DE-627)OLC2080158341 (DE-He213)s11277-021-08291-9-p DE-627 ger DE-627 rakwb eng 620 VZ Robinson, Y. Harold verfasserin aut 3-Dimensional Manifold and Machine Learning Based Localization Algorithm for Wireless Sensor Networks 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The minimized amount of Localization accuracy is one of the common issues in Wireless sensor networks. The determination of unknown nodes in a network needs good localization approach. This paper proposes a 3-dimensional Manifold and Machine Learning based Localization algorithm for providing the solution to the localization problem. The Machine Learning uses to identify the faulty nodes in the network for better efficiency and computes the optimal solution to the real-time localization problems in WSNs. The mobility model is deployed within the sensor node and the sensor node is computed to estimate the position of the sensor node. This technique is utilized to identify the position of the unknown nodes according to the transmission range. Machine Learning technique utilizes to identify the faulty nodes from the sensor nodes for obtaining the maximum efficiency. RMSE is used to measure the errors for providing better accuracy and also increase the level of quantization for WSN localization approach. The simulation results prove that the proposed technique has high accuracy, reduced energy consumption compared with the relevant techniques. Localization Manifold Transmission range Accuracy Energy consumption Machine learning Vimal, S. aut Julie, E. Golden aut Lakshmi Narayanan, K. aut Rho, Seungmin aut Enthalten in Wireless personal communications Springer US, 1994 127(2021), 1 vom: 11. März, Seite 523-541 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:127 year:2021 number:1 day:11 month:03 pages:523-541 https://doi.org/10.1007/s11277-021-08291-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW AR 127 2021 1 11 03 523-541 |
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Abstract The minimized amount of Localization accuracy is one of the common issues in Wireless sensor networks. The determination of unknown nodes in a network needs good localization approach. This paper proposes a 3-dimensional Manifold and Machine Learning based Localization algorithm for providing the solution to the localization problem. The Machine Learning uses to identify the faulty nodes in the network for better efficiency and computes the optimal solution to the real-time localization problems in WSNs. The mobility model is deployed within the sensor node and the sensor node is computed to estimate the position of the sensor node. This technique is utilized to identify the position of the unknown nodes according to the transmission range. Machine Learning technique utilizes to identify the faulty nodes from the sensor nodes for obtaining the maximum efficiency. RMSE is used to measure the errors for providing better accuracy and also increase the level of quantization for WSN localization approach. The simulation results prove that the proposed technique has high accuracy, reduced energy consumption compared with the relevant techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Abstract The minimized amount of Localization accuracy is one of the common issues in Wireless sensor networks. The determination of unknown nodes in a network needs good localization approach. This paper proposes a 3-dimensional Manifold and Machine Learning based Localization algorithm for providing the solution to the localization problem. The Machine Learning uses to identify the faulty nodes in the network for better efficiency and computes the optimal solution to the real-time localization problems in WSNs. The mobility model is deployed within the sensor node and the sensor node is computed to estimate the position of the sensor node. This technique is utilized to identify the position of the unknown nodes according to the transmission range. Machine Learning technique utilizes to identify the faulty nodes from the sensor nodes for obtaining the maximum efficiency. RMSE is used to measure the errors for providing better accuracy and also increase the level of quantization for WSN localization approach. The simulation results prove that the proposed technique has high accuracy, reduced energy consumption compared with the relevant techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Abstract The minimized amount of Localization accuracy is one of the common issues in Wireless sensor networks. The determination of unknown nodes in a network needs good localization approach. This paper proposes a 3-dimensional Manifold and Machine Learning based Localization algorithm for providing the solution to the localization problem. The Machine Learning uses to identify the faulty nodes in the network for better efficiency and computes the optimal solution to the real-time localization problems in WSNs. The mobility model is deployed within the sensor node and the sensor node is computed to estimate the position of the sensor node. This technique is utilized to identify the position of the unknown nodes according to the transmission range. Machine Learning technique utilizes to identify the faulty nodes from the sensor nodes for obtaining the maximum efficiency. RMSE is used to measure the errors for providing better accuracy and also increase the level of quantization for WSN localization approach. The simulation results prove that the proposed technique has high accuracy, reduced energy consumption compared with the relevant techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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3-Dimensional Manifold and Machine Learning Based Localization Algorithm for Wireless Sensor Networks |
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Vimal, S. Julie, E. Golden Lakshmi Narayanan, K. Rho, Seungmin |
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