Nellore Kapileswar, Judy Simon, Polasi Phani Kumar, Thomas M Chen, Sathiyanarayanan Mithileysh
{"title":"改进的dDQL：双深度q -学习支持的水下物联网定位","authors":"Nellore Kapileswar, Judy Simon, Polasi Phani Kumar, Thomas M Chen, Sathiyanarayanan Mithileysh","doi":"10.1002/ett.70207","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Reliable sensor node localization is essential for internet of underwater things (IoUT) applications because it allows management, communication, and sensing in large, uncharted oceanic environments. This research focuses on developing a learning-enabled node localization model for IoUT using autonomous underwater vehicles (AUVs). To estimate the locations of AUVs, active and passive sensor nodes, a double deep Q-learning (dDQL) based localization algorithm is introduced. AUVs serve as mobile anchor nodes, and the algorithm uses an online value iteration process to optimize node locations. Active sensor nodes initiate the localization process by transmitting messages, whereas passive sensor nodes determine their location without sending signals. Furthermore, the proposed algorithm for exaggerated crayfish optimization (ExCo) utilizes the selection of optimal actions. The proposed dDQL with ExCo acquired RMSE, localization error, time, delay, throughput, and energy consumption of 1.44E-07 m, 7.19E-08 m, 16153.16 s, 13.08 s, 0.98 bps, and 0.35 J, respectively.</p>\n </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 7","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improved dDQL: A Double Deep Q-Learning Enabled Localization for Internet of Underwater Things\",\"authors\":\"Nellore Kapileswar, Judy Simon, Polasi Phani Kumar, Thomas M Chen, Sathiyanarayanan Mithileysh\",\"doi\":\"10.1002/ett.70207\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>Reliable sensor node localization is essential for internet of underwater things (IoUT) applications because it allows management, communication, and sensing in large, uncharted oceanic environments. This research focuses on developing a learning-enabled node localization model for IoUT using autonomous underwater vehicles (AUVs). To estimate the locations of AUVs, active and passive sensor nodes, a double deep Q-learning (dDQL) based localization algorithm is introduced. AUVs serve as mobile anchor nodes, and the algorithm uses an online value iteration process to optimize node locations. Active sensor nodes initiate the localization process by transmitting messages, whereas passive sensor nodes determine their location without sending signals. Furthermore, the proposed algorithm for exaggerated crayfish optimization (ExCo) utilizes the selection of optimal actions. The proposed dDQL with ExCo acquired RMSE, localization error, time, delay, throughput, and energy consumption of 1.44E-07 m, 7.19E-08 m, 16153.16 s, 13.08 s, 0.98 bps, and 0.35 J, respectively.</p>\\n </div>\",\"PeriodicalId\":23282,\"journal\":{\"name\":\"Transactions on Emerging Telecommunications Technologies\",\"volume\":\"36 7\",\"pages\":\"\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2025-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transactions on Emerging Telecommunications Technologies\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/ett.70207\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"TELECOMMUNICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions on Emerging Telecommunications Technologies","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ett.70207","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
Improved dDQL: A Double Deep Q-Learning Enabled Localization for Internet of Underwater Things
Reliable sensor node localization is essential for internet of underwater things (IoUT) applications because it allows management, communication, and sensing in large, uncharted oceanic environments. This research focuses on developing a learning-enabled node localization model for IoUT using autonomous underwater vehicles (AUVs). To estimate the locations of AUVs, active and passive sensor nodes, a double deep Q-learning (dDQL) based localization algorithm is introduced. AUVs serve as mobile anchor nodes, and the algorithm uses an online value iteration process to optimize node locations. Active sensor nodes initiate the localization process by transmitting messages, whereas passive sensor nodes determine their location without sending signals. Furthermore, the proposed algorithm for exaggerated crayfish optimization (ExCo) utilizes the selection of optimal actions. The proposed dDQL with ExCo acquired RMSE, localization error, time, delay, throughput, and energy consumption of 1.44E-07 m, 7.19E-08 m, 16153.16 s, 13.08 s, 0.98 bps, and 0.35 J, respectively.
期刊介绍：
ransactions on Emerging Telecommunications Technologies (ETT), formerly known as European Transactions on Telecommunications (ETT), has the following aims:
- to attract cutting-edge publications from leading researchers and research groups around the world
- to become a highly cited source of timely research findings in emerging fields of telecommunications
- to limit revision and publication cycles to a few months and thus significantly increase attractiveness to publish
- to become the leading journal for publishing the latest developments in telecommunications
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