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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| Vydáno v: | Journal of marine science and engineering Ročník 12; číslo 11; s. 1958 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Basel
MDPI AG
01.11.2024
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| ISSN: | 2077-1312, 2077-1312 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Audience | Academic |
| Author | Zhu, Rongxin Xue, Libin Cao, Chunjie |
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| Cites_doi | 10.1109/JIOT.2021.3055857 10.1109/JIOT.2023.3332072 10.1109/ICC.2008.713 10.1121/10.0000970 10.1109/TVT.2020.3018489 10.1109/JSEN.2024.3370259 10.1109/TNET.2023.3311682 10.1109/JIOT.2022.3195223 10.1109/JSEN.2024.3417645 10.1109/TWC.2002.804190 10.1016/j.optlaseng.2022.107468 10.1007/s11277-020-07431-x 10.1109/IOTM.001.2200221 10.1016/j.jnca.2021.103295 10.23919/CCC50068.2020.9189162 10.1109/TNSM.2023.3283248 10.1109/ACCESS.2019.2911190 10.1109/ACCESS.2022.3230600 10.1109/TGCN.2023.3249208 10.1109/JSEN.2024.3350041 10.1109/ICAAIC53929.2022.9792656 10.1007/s11276-015-1011-3 10.1109/JSEN.2022.3175994 10.1109/JIOT.2023.3263174 10.1109/PIMRC.2009.5450334 10.3390/s22020478 |
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| SubjectTerms | Acoustics Adaptive algorithms Adaptive systems Algorithms Analysis Batteries Clustering clustering algorithm Communication Competition Energy energy balance Energy consumption Energy harvesting Energy utilization Environmental monitoring Game theory Marine environment marine environment monitoring Monitoring Monitoring systems Network topologies Nodes Ocean bottom Ocean floor Optimization Power supply Revenue Sensors Topology Underwater Underwater detectors |
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| Title | Research on Underwater Sensor Network Adaptive Clustering Algorithm for Marine Environment Monitoring |
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