System-Level Digital Twin Modeling for Underwater Wireless IoT Networks
Digital Twin (DT) technology is pivotal in advancing smart underwater wireless IoT networks and effectively enhancing capabilities for monitoring and managing aquatic environments. For complex system-level DT models in these networks, assembling multiple unit-level DT models becomes crucial. Federat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/1/32 |
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| author | Lei Wang Lei Yan Xinbin Li Song Han |
| author_facet | Lei Wang Lei Yan Xinbin Li Song Han |
| author_sort | Lei Wang |
| collection | DOAJ |
| description | Digital Twin (DT) technology is pivotal in advancing smart underwater wireless IoT networks and effectively enhancing capabilities for monitoring and managing aquatic environments. For complex system-level DT models in these networks, assembling multiple unit-level DT models becomes crucial. Federated Learning (FL) presents a distributed machine learning paradigm that enables devices within underwater wireless IoT networks to collaboratively refine a DT model. Employing FL for DT modeling (FLDTM) is particularly valuable, as it allows for the enhancement of model accuracy without explicitly sharing local data, thereby preserving data privacy under challenging aquatic conditions. In this article, we propose a secure and efficient multi-server FL framework tailored for underwater wireless systems. We introduce a voting-based security prediction model to significantly bolster security in underwater wireless communication. Moreover, we introduce the network flow problem and employ a minimum-cost flow algorithm to enable FL servers’ cooperation. These strategies are integrated into a smart contract, namely, the UCB-based Smart Contract with a Security Prediction model and Minimum-Cost Flow (UCB-SCPF) policy. Experimental results show that the UCB-SCPF policy-based FLDTM framework achieves model accuracy comparable to ideal conditions while demonstrating excellent performance in terms of training efficiency and security. Additionally, the framework maintains stability as the network scale increases. These findings underscore the potential of the UCB-SCPF policy-based FLDTM framework in advancing DT technology for underwater wireless IoT networks. |
| format | Article |
| id | doaj-art-be7aa35ec2914ce38e85ebdc03acdec0 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-be7aa35ec2914ce38e85ebdc03acdec02025-01-24T13:36:36ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011313210.3390/jmse13010032System-Level Digital Twin Modeling for Underwater Wireless IoT NetworksLei Wang0Lei Yan1Xinbin Li2Song Han3Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, ChinaHebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, ChinaKey Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, ChinaKey Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, ChinaDigital Twin (DT) technology is pivotal in advancing smart underwater wireless IoT networks and effectively enhancing capabilities for monitoring and managing aquatic environments. For complex system-level DT models in these networks, assembling multiple unit-level DT models becomes crucial. Federated Learning (FL) presents a distributed machine learning paradigm that enables devices within underwater wireless IoT networks to collaboratively refine a DT model. Employing FL for DT modeling (FLDTM) is particularly valuable, as it allows for the enhancement of model accuracy without explicitly sharing local data, thereby preserving data privacy under challenging aquatic conditions. In this article, we propose a secure and efficient multi-server FL framework tailored for underwater wireless systems. We introduce a voting-based security prediction model to significantly bolster security in underwater wireless communication. Moreover, we introduce the network flow problem and employ a minimum-cost flow algorithm to enable FL servers’ cooperation. These strategies are integrated into a smart contract, namely, the UCB-based Smart Contract with a Security Prediction model and Minimum-Cost Flow (UCB-SCPF) policy. Experimental results show that the UCB-SCPF policy-based FLDTM framework achieves model accuracy comparable to ideal conditions while demonstrating excellent performance in terms of training efficiency and security. Additionally, the framework maintains stability as the network scale increases. These findings underscore the potential of the UCB-SCPF policy-based FLDTM framework in advancing DT technology for underwater wireless IoT networks.https://www.mdpi.com/2077-1312/13/1/32digital twinunderwater wireless IoT networksfederated learningmulti-server cooperation |
| spellingShingle | Lei Wang Lei Yan Xinbin Li Song Han System-Level Digital Twin Modeling for Underwater Wireless IoT Networks Journal of Marine Science and Engineering digital twin underwater wireless IoT networks federated learning multi-server cooperation |
| title | System-Level Digital Twin Modeling for Underwater Wireless IoT Networks |
| title_full | System-Level Digital Twin Modeling for Underwater Wireless IoT Networks |
| title_fullStr | System-Level Digital Twin Modeling for Underwater Wireless IoT Networks |
| title_full_unstemmed | System-Level Digital Twin Modeling for Underwater Wireless IoT Networks |
| title_short | System-Level Digital Twin Modeling for Underwater Wireless IoT Networks |
| title_sort | system level digital twin modeling for underwater wireless iot networks |
| topic | digital twin underwater wireless IoT networks federated learning multi-server cooperation |
| url | https://www.mdpi.com/2077-1312/13/1/32 |
| work_keys_str_mv | AT leiwang systemleveldigitaltwinmodelingforunderwaterwirelessiotnetworks AT leiyan systemleveldigitaltwinmodelingforunderwaterwirelessiotnetworks AT xinbinli systemleveldigitaltwinmodelingforunderwaterwirelessiotnetworks AT songhan systemleveldigitaltwinmodelingforunderwaterwirelessiotnetworks |