Advanced KNN-based cost-efficient algorithm for precision localization and energy optimization in dynamic underwater sensor networks
Abstract Underwater environmental exploration using sensor nodes has emerged as a critical endeavor fraught with challenges such as localization errors, energy, and costs attributed to the dynamic nature of underwater environments. This paper proposes a KNN-based cost-efficient machine-learning algo...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86266-7 |
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| author | Nadia Shamshad Lei Wang Kiran Saleem Danish Sarwr Salil Bharany Ahmad Almogren Jaeyoung Choi Ateeq Ur Rehman Ayman Altameem |
| author_facet | Nadia Shamshad Lei Wang Kiran Saleem Danish Sarwr Salil Bharany Ahmad Almogren Jaeyoung Choi Ateeq Ur Rehman Ayman Altameem |
| author_sort | Nadia Shamshad |
| collection | DOAJ |
| description | Abstract Underwater environmental exploration using sensor nodes has emerged as a critical endeavor fraught with challenges such as localization errors, energy, and costs attributed to the dynamic nature of underwater environments. This paper proposes a KNN-based cost-efficient machine-learning algorithm aimed at optimizing underwater context acquisition with sensor nodes. By addressing existing localization challenges, the algorithm minimizes localization errors, energy consumption and Time costs while significantly enhancing localization accuracy to 99.98%. Furthermore, the study employs the KNN-based cost-efficient method to predict nodes’ orientation in dynamic water conditions, thereby facilitating the mapping of the shortest distance between sensor nodes during the underwater context acquisition process. The effectiveness of the proposed KNN-based cost-efficient method is evaluated through real-time experiments conducted in a water tank setup and simulations using the Ns-3.37 version. Results demonstrate notable improvements in localization accuracy by optimizing the localization error rate from 4.59m to $$3.88 \times 10^{-8}$$ m, Reducing localization energy consumption rate 0.0045J in addition for the first time we have also computed the localization Time cost rate which is 0.06762s. we assumed that in real-time and in NS-3 simulations on the Aqua-sim model indicate communication speed at 1500m/s. This research presents an innovative and practical approach to resolving challenges associated with underwater context acquisition through sensor nodes, it offers a comprehensive understanding and emphasizes the real-time implementation of the KNN-based cost-efficient approach. |
| format | Article |
| id | doaj-art-3467b267c4b44ed6b7b4077cba5c6326 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-3467b267c4b44ed6b7b4077cba5c63262025-01-19T12:21:50ZengNature PortfolioScientific Reports2045-23222025-01-0115111710.1038/s41598-025-86266-7Advanced KNN-based cost-efficient algorithm for precision localization and energy optimization in dynamic underwater sensor networksNadia Shamshad0Lei Wang1Kiran Saleem2Danish Sarwr3Salil Bharany4Ahmad Almogren5Jaeyoung Choi6Ateeq Ur Rehman7Ayman Altameem8School of Software, Dalian University of TechnologySchool of Software, Dalian University of TechnologySchool of Software, Dalian University of TechnologySchool of Software, Dalian University of TechnologyChitkara University Institute of Engineering and Technology, Chitkara UniversityDepartment of Computer Science, College of Computer and Information Sciences, King Saud UniversitySchool of Computing, Gachon UniversitySchool of Computing, Gachon UniversityDepartment of Natural and Engineering Sciences, College of Applied Studies and Community Services, King Saud UniversityAbstract Underwater environmental exploration using sensor nodes has emerged as a critical endeavor fraught with challenges such as localization errors, energy, and costs attributed to the dynamic nature of underwater environments. This paper proposes a KNN-based cost-efficient machine-learning algorithm aimed at optimizing underwater context acquisition with sensor nodes. By addressing existing localization challenges, the algorithm minimizes localization errors, energy consumption and Time costs while significantly enhancing localization accuracy to 99.98%. Furthermore, the study employs the KNN-based cost-efficient method to predict nodes’ orientation in dynamic water conditions, thereby facilitating the mapping of the shortest distance between sensor nodes during the underwater context acquisition process. The effectiveness of the proposed KNN-based cost-efficient method is evaluated through real-time experiments conducted in a water tank setup and simulations using the Ns-3.37 version. Results demonstrate notable improvements in localization accuracy by optimizing the localization error rate from 4.59m to $$3.88 \times 10^{-8}$$ m, Reducing localization energy consumption rate 0.0045J in addition for the first time we have also computed the localization Time cost rate which is 0.06762s. we assumed that in real-time and in NS-3 simulations on the Aqua-sim model indicate communication speed at 1500m/s. This research presents an innovative and practical approach to resolving challenges associated with underwater context acquisition through sensor nodes, it offers a comprehensive understanding and emphasizes the real-time implementation of the KNN-based cost-efficient approach.https://doi.org/10.1038/s41598-025-86266-7Machine LearningKNN-Algorithmaccuracy rateLocalizationPredictionNS-3 |
| spellingShingle | Nadia Shamshad Lei Wang Kiran Saleem Danish Sarwr Salil Bharany Ahmad Almogren Jaeyoung Choi Ateeq Ur Rehman Ayman Altameem Advanced KNN-based cost-efficient algorithm for precision localization and energy optimization in dynamic underwater sensor networks Scientific Reports Machine Learning KNN-Algorithm accuracy rate Localization Prediction NS-3 |
| title | Advanced KNN-based cost-efficient algorithm for precision localization and energy optimization in dynamic underwater sensor networks |
| title_full | Advanced KNN-based cost-efficient algorithm for precision localization and energy optimization in dynamic underwater sensor networks |
| title_fullStr | Advanced KNN-based cost-efficient algorithm for precision localization and energy optimization in dynamic underwater sensor networks |
| title_full_unstemmed | Advanced KNN-based cost-efficient algorithm for precision localization and energy optimization in dynamic underwater sensor networks |
| title_short | Advanced KNN-based cost-efficient algorithm for precision localization and energy optimization in dynamic underwater sensor networks |
| title_sort | advanced knn based cost efficient algorithm for precision localization and energy optimization in dynamic underwater sensor networks |
| topic | Machine Learning KNN-Algorithm accuracy rate Localization Prediction NS-3 |
| url | https://doi.org/10.1038/s41598-025-86266-7 |
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