{"title":"具有决策反馈的并联分散传感器网络性能研究","authors":"S. Kopparthi, B. Natarajan","doi":"10.1109/ISWPC.2007.342680","DOIUrl":null,"url":null,"abstract":"In most previous studies, distributed sensor networks are typically assumed to be memory less. In this paper, we consider a distributed sensor network with feedback at both the sensor level as well as at the fusion center. Specifically, we analyze (1) a local feedback system (LFS), where the most recent local decision is fed back to its corresponding local sensor; (2) a local and global feedback system 1 (LGFS1) where the most recent local decision is fed back to its corresponding local sensor and the most recent global decision is fed back to the fusion center, and (3) a local and global feedback system 2 (LGFS2), where the most recent global decision is fed back to all the local sensors and to the fusion center in addition to the local decision being fed back to its corresponding local sensor. For all the cases, we derive the decision rule and compare the global probability of error using simulations. We show that in an error-free channel, any form of feedback improves GPE performance relative to no feedback system. However, feeding the global decision back to local sensors not only drains resources but also provides the worst performance among feedback schemes","PeriodicalId":403213,"journal":{"name":"2007 2nd International Symposium on Wireless Pervasive Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Performance of Parallel Decentralized Sensor Network with Decision Feedback\",\"authors\":\"S. Kopparthi, B. Natarajan\",\"doi\":\"10.1109/ISWPC.2007.342680\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In most previous studies, distributed sensor networks are typically assumed to be memory less. In this paper, we consider a distributed sensor network with feedback at both the sensor level as well as at the fusion center. Specifically, we analyze (1) a local feedback system (LFS), where the most recent local decision is fed back to its corresponding local sensor; (2) a local and global feedback system 1 (LGFS1) where the most recent local decision is fed back to its corresponding local sensor and the most recent global decision is fed back to the fusion center, and (3) a local and global feedback system 2 (LGFS2), where the most recent global decision is fed back to all the local sensors and to the fusion center in addition to the local decision being fed back to its corresponding local sensor. For all the cases, we derive the decision rule and compare the global probability of error using simulations. We show that in an error-free channel, any form of feedback improves GPE performance relative to no feedback system. However, feeding the global decision back to local sensors not only drains resources but also provides the worst performance among feedback schemes\",\"PeriodicalId\":403213,\"journal\":{\"name\":\"2007 2nd International Symposium on Wireless Pervasive Computing\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-04-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 2nd International Symposium on Wireless Pervasive Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISWPC.2007.342680\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 2nd International Symposium on Wireless Pervasive Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISWPC.2007.342680","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Performance of Parallel Decentralized Sensor Network with Decision Feedback
In most previous studies, distributed sensor networks are typically assumed to be memory less. In this paper, we consider a distributed sensor network with feedback at both the sensor level as well as at the fusion center. Specifically, we analyze (1) a local feedback system (LFS), where the most recent local decision is fed back to its corresponding local sensor; (2) a local and global feedback system 1 (LGFS1) where the most recent local decision is fed back to its corresponding local sensor and the most recent global decision is fed back to the fusion center, and (3) a local and global feedback system 2 (LGFS2), where the most recent global decision is fed back to all the local sensors and to the fusion center in addition to the local decision being fed back to its corresponding local sensor. For all the cases, we derive the decision rule and compare the global probability of error using simulations. We show that in an error-free channel, any form of feedback improves GPE performance relative to no feedback system. However, feeding the global decision back to local sensors not only drains resources but also provides the worst performance among feedback schemes