Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring
Wireless Sensor Networks (WSNs) have been adopted in various environmental pollution monitoring applications. As an important environmental field, water quality monitoring is a vital process to ensure the sustainable, important feeding of and as a life-maintaining source for many living creatures. T...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/12/5733 |
| _version_ | 1797592652635439104 |
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| author | Catherine Nayer Tadros Nader Shehata Bassem Mokhtar |
| author_facet | Catherine Nayer Tadros Nader Shehata Bassem Mokhtar |
| author_sort | Catherine Nayer Tadros |
| collection | DOAJ |
| description | Wireless Sensor Networks (WSNs) have been adopted in various environmental pollution monitoring applications. As an important environmental field, water quality monitoring is a vital process to ensure the sustainable, important feeding of and as a life-maintaining source for many living creatures. To conduct this process efficiently, the integration of lightweight machine learning technologies can extend its efficacy and accuracy. WSNs often suffer from energy-limited devices and resource-affected operations, thus constraining WSNs’ lifetime and capability. Energy-efficient clustering protocols have been introduced to tackle this challenge. The low-energy adaptive clustering hierarchy (LEACH) protocol is widely used due to its simplicity and ability to manage large datasets and prolong network lifetime. In this paper, we investigate and present a modified LEACH-based clustering algorithm in conjunction with a K-means data clustering approach to enable efficient decision making based on water-quality-monitoring-related operations. This study is operated based on the experimental measurements of lanthanide oxide nanoparticles, selected as cerium oxide nanoparticles (ceria NPs), as an active sensing host for the optical detection of hydrogen peroxide pollutants via a fluorescence quenching mechanism. A mathematical model is proposed for the K-means LEACH-based clustering algorithm for WSNs to analyze the quality monitoring process in water, where various levels of pollutants exist. The simulation results show the efficacy of our modified K-means-based hierarchical data clustering and routing in prolonging network lifetime when operated in static and dynamic contexts. |
| first_indexed | 2024-03-11T01:57:13Z |
| format | Article |
| id | doaj.art-935eff678acd4b668ed71a649fa01f05 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:57:13Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-935eff678acd4b668ed71a649fa01f052023-11-18T12:35:21ZengMDPI AGSensors1424-82202023-06-012312573310.3390/s23125733Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution MonitoringCatherine Nayer Tadros0Nader Shehata1Bassem Mokhtar2Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria 21544, EgyptDepartment of Mathematics and Physics, Faculty of Engineering, Alexandria University, Alexandria 21544, EgyptDepartment of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria 21544, EgyptWireless Sensor Networks (WSNs) have been adopted in various environmental pollution monitoring applications. As an important environmental field, water quality monitoring is a vital process to ensure the sustainable, important feeding of and as a life-maintaining source for many living creatures. To conduct this process efficiently, the integration of lightweight machine learning technologies can extend its efficacy and accuracy. WSNs often suffer from energy-limited devices and resource-affected operations, thus constraining WSNs’ lifetime and capability. Energy-efficient clustering protocols have been introduced to tackle this challenge. The low-energy adaptive clustering hierarchy (LEACH) protocol is widely used due to its simplicity and ability to manage large datasets and prolong network lifetime. In this paper, we investigate and present a modified LEACH-based clustering algorithm in conjunction with a K-means data clustering approach to enable efficient decision making based on water-quality-monitoring-related operations. This study is operated based on the experimental measurements of lanthanide oxide nanoparticles, selected as cerium oxide nanoparticles (ceria NPs), as an active sensing host for the optical detection of hydrogen peroxide pollutants via a fluorescence quenching mechanism. A mathematical model is proposed for the K-means LEACH-based clustering algorithm for WSNs to analyze the quality monitoring process in water, where various levels of pollutants exist. The simulation results show the efficacy of our modified K-means-based hierarchical data clustering and routing in prolonging network lifetime when operated in static and dynamic contexts.https://www.mdpi.com/1424-8220/23/12/5733WSN clusteringLEACHK-means algorithmunsupervised learningwater quality monitoring |
| spellingShingle | Catherine Nayer Tadros Nader Shehata Bassem Mokhtar Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring Sensors WSN clustering LEACH K-means algorithm unsupervised learning water quality monitoring |
| title | Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring |
| title_full | Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring |
| title_fullStr | Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring |
| title_full_unstemmed | Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring |
| title_short | Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring |
| title_sort | unsupervised learning based wsn clustering for efficient environmental pollution monitoring |
| topic | WSN clustering LEACH K-means algorithm unsupervised learning water quality monitoring |
| url | https://www.mdpi.com/1424-8220/23/12/5733 |
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