{"title":"基于深度神经网络的水下无线传感器网络距离定位:海报摘要","authors":"Yuhan Dong, Zheng Li, Rui Wang, Kai Zhang","doi":"10.1145/3055031.3055069","DOIUrl":null,"url":null,"abstract":"In underwater wireless sensor networks (USWNs), localizing unknown nodes is essential for most applications while is more complex than that of terrestrial WSNs. In this paper, we propose a range-based localization scheme using deep neural network (DNN). Numerical results suggest that the proposed DNN localization algorithm outperforms traditional schemes using least squares support vector machines (LS-SVM) or generalized least squares (GLS) in terms of localization accuracy and efficiency. Moreover, the proposed algorithm requires a small number of anchor nodes, which is plausible for practical applications.","PeriodicalId":206082,"journal":{"name":"Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Range-based localization in underwater wireless sensor networks using deep neural network: poster abstract\",\"authors\":\"Yuhan Dong, Zheng Li, Rui Wang, Kai Zhang\",\"doi\":\"10.1145/3055031.3055069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In underwater wireless sensor networks (USWNs), localizing unknown nodes is essential for most applications while is more complex than that of terrestrial WSNs. In this paper, we propose a range-based localization scheme using deep neural network (DNN). Numerical results suggest that the proposed DNN localization algorithm outperforms traditional schemes using least squares support vector machines (LS-SVM) or generalized least squares (GLS) in terms of localization accuracy and efficiency. Moreover, the proposed algorithm requires a small number of anchor nodes, which is plausible for practical applications.\",\"PeriodicalId\":206082,\"journal\":{\"name\":\"Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks\",\"volume\":\"51 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3055031.3055069\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3055031.3055069","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Range-based localization in underwater wireless sensor networks using deep neural network: poster abstract
In underwater wireless sensor networks (USWNs), localizing unknown nodes is essential for most applications while is more complex than that of terrestrial WSNs. In this paper, we propose a range-based localization scheme using deep neural network (DNN). Numerical results suggest that the proposed DNN localization algorithm outperforms traditional schemes using least squares support vector machines (LS-SVM) or generalized least squares (GLS) in terms of localization accuracy and efficiency. Moreover, the proposed algorithm requires a small number of anchor nodes, which is plausible for practical applications.