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A Robust UWSN Handover Prediction System Using Ensemble Learning

The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different...

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Autores principales: Eldesouky, Esraa, Bekhit, Mahmoud, Fathalla, Ahmed, Salah, Ahmad, Ali, Ahmed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434566/
https://www.ncbi.nlm.nih.gov/pubmed/34502667
http://dx.doi.org/10.3390/s21175777
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author Eldesouky, Esraa
Bekhit, Mahmoud
Fathalla, Ahmed
Salah, Ahmad
Ali, Ahmed
author_facet Eldesouky, Esraa
Bekhit, Mahmoud
Fathalla, Ahmed
Salah, Ahmad
Ali, Ahmed
author_sort Eldesouky, Esraa
collection PubMed
description The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical.
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spelling pubmed-84345662021-09-12 A Robust UWSN Handover Prediction System Using Ensemble Learning Eldesouky, Esraa Bekhit, Mahmoud Fathalla, Ahmed Salah, Ahmad Ali, Ahmed Sensors (Basel) Article The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical. MDPI 2021-08-27 /pmc/articles/PMC8434566/ /pubmed/34502667 http://dx.doi.org/10.3390/s21175777 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Eldesouky, Esraa
Bekhit, Mahmoud
Fathalla, Ahmed
Salah, Ahmad
Ali, Ahmed
A Robust UWSN Handover Prediction System Using Ensemble Learning
title A Robust UWSN Handover Prediction System Using Ensemble Learning
title_full A Robust UWSN Handover Prediction System Using Ensemble Learning
title_fullStr A Robust UWSN Handover Prediction System Using Ensemble Learning
title_full_unstemmed A Robust UWSN Handover Prediction System Using Ensemble Learning
title_short A Robust UWSN Handover Prediction System Using Ensemble Learning
title_sort robust uwsn handover prediction system using ensemble learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434566/
https://www.ncbi.nlm.nih.gov/pubmed/34502667
http://dx.doi.org/10.3390/s21175777
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