Research on Underwater Sensor Network Adaptive Clustering Algorithm for Marine Environment Monitoring
In recent years, underwater environmental monitoring has primarily relied on monitoring systems based on underwater sensor networks (UWSNs). The underwater sensor node using a self-powered monitoring system has not been widely used because of the complicated design and high cost of its energy-harves...
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MDPI AG
2024-11-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/11/1958 |
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| author | Libin Xue Chunjie Cao Rongxin Zhu |
| author_facet | Libin Xue Chunjie Cao Rongxin Zhu |
| author_sort | Libin Xue |
| collection | DOAJ |
| description | In recent years, underwater environmental monitoring has primarily relied on monitoring systems based on underwater sensor networks (UWSNs). The underwater sensor node using a self-powered monitoring system has not been widely used because of the complicated design and high cost of its energy-harvesting device. Thus, the mobile monitoring nodes within UWSNs are typically powered by batteries with limited energy, and replacement on the seabed is challenging. As a result, optimizing the energy consumption of the mobile monitoring network is of significant importance. The clustering algorithm for UWSNs is acknowledged as a vital approach to balancing and reducing network energy consumption. Nevertheless, most existing clustering algorithms employ fixed schemes to balance the energy consumption among nodes, which are unable to dynamically adapt to changes in network topology and do not account for the complexities of the underwater channel environment, thus not aligning with the actual scenarios of marine environment monitoring. Consequently, this paper introduces an adaptive clustering algorithm for marine environment monitoring (MEMAC). The algorithm incorporates the multipath channel information of the underwater environment and the traffic weight between nodes into the probability model to calculate the probability of the node being elected as the cluster head (CH). The final calculated expected revenues are the user’s revenues after participating in the game under the influence of the multipath effect, and the revenues of all users jointly determine the performance of the clustering algorithm proposed in this paper. When the energy consumption of the CH node is too much and needs to be rotated, MEMAC, through a CH rotation mechanism and a comprehensive analysis of the overall remaining energy of the network, further optimizes the CH selection strategy while ensuring network stability. Simulation results indicate that the network lifetime of the proposed MEMAC method is extended by 58.9% and 19.17% compared to the two latest clustering algorithms, the Game Theory-Based Clustering Scheme (GTC) and the Centralized Control-Based Clustering Scheme (CCCS), respectively. This demonstrates that the algorithm can achieve efficient energy utilization and notably enhance network performance. |
| format | Article |
| id | doaj-art-6c0863e97bb7494986407e8b381e8f82 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-6c0863e97bb7494986407e8b381e8f822025-08-20T01:53:53ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-11-011211195810.3390/jmse12111958Research on Underwater Sensor Network Adaptive Clustering Algorithm for Marine Environment MonitoringLibin Xue0Chunjie Cao1Rongxin Zhu2School of Cyberspace Security, Hainan University, Haikou 570228, ChinaSchool of Cyberspace Security, Hainan University, Haikou 570228, ChinaSchool of Computer Science and Technology, Hainan University, Haikou 570228, ChinaIn recent years, underwater environmental monitoring has primarily relied on monitoring systems based on underwater sensor networks (UWSNs). The underwater sensor node using a self-powered monitoring system has not been widely used because of the complicated design and high cost of its energy-harvesting device. Thus, the mobile monitoring nodes within UWSNs are typically powered by batteries with limited energy, and replacement on the seabed is challenging. As a result, optimizing the energy consumption of the mobile monitoring network is of significant importance. The clustering algorithm for UWSNs is acknowledged as a vital approach to balancing and reducing network energy consumption. Nevertheless, most existing clustering algorithms employ fixed schemes to balance the energy consumption among nodes, which are unable to dynamically adapt to changes in network topology and do not account for the complexities of the underwater channel environment, thus not aligning with the actual scenarios of marine environment monitoring. Consequently, this paper introduces an adaptive clustering algorithm for marine environment monitoring (MEMAC). The algorithm incorporates the multipath channel information of the underwater environment and the traffic weight between nodes into the probability model to calculate the probability of the node being elected as the cluster head (CH). The final calculated expected revenues are the user’s revenues after participating in the game under the influence of the multipath effect, and the revenues of all users jointly determine the performance of the clustering algorithm proposed in this paper. When the energy consumption of the CH node is too much and needs to be rotated, MEMAC, through a CH rotation mechanism and a comprehensive analysis of the overall remaining energy of the network, further optimizes the CH selection strategy while ensuring network stability. Simulation results indicate that the network lifetime of the proposed MEMAC method is extended by 58.9% and 19.17% compared to the two latest clustering algorithms, the Game Theory-Based Clustering Scheme (GTC) and the Centralized Control-Based Clustering Scheme (CCCS), respectively. This demonstrates that the algorithm can achieve efficient energy utilization and notably enhance network performance.https://www.mdpi.com/2077-1312/12/11/1958marine environment monitoringgame theoryenergy balanceclustering algorithm |
| spellingShingle | Libin Xue Chunjie Cao Rongxin Zhu Research on Underwater Sensor Network Adaptive Clustering Algorithm for Marine Environment Monitoring Journal of Marine Science and Engineering marine environment monitoring game theory energy balance clustering algorithm |
| title | Research on Underwater Sensor Network Adaptive Clustering Algorithm for Marine Environment Monitoring |
| title_full | Research on Underwater Sensor Network Adaptive Clustering Algorithm for Marine Environment Monitoring |
| title_fullStr | Research on Underwater Sensor Network Adaptive Clustering Algorithm for Marine Environment Monitoring |
| title_full_unstemmed | Research on Underwater Sensor Network Adaptive Clustering Algorithm for Marine Environment Monitoring |
| title_short | Research on Underwater Sensor Network Adaptive Clustering Algorithm for Marine Environment Monitoring |
| title_sort | research on underwater sensor network adaptive clustering algorithm for marine environment monitoring |
| topic | marine environment monitoring game theory energy balance clustering algorithm |
| url | https://www.mdpi.com/2077-1312/12/11/1958 |
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