{"title":"民用基础设施健康监测无线传感器网络的高效数据压缩","authors":"Shengpu Liu, Liang Cheng","doi":"10.1109/SAHCN.2006.288567","DOIUrl":null,"url":null,"abstract":"In this paper, we present an efficient sensor data compression process for civil infrastructure health monitoring applications. It integrates lifting scheme wavelet transform (LSWT) and distributed source coding (DSC), which can reduce the raw data size by 1:27 to 1:80 while having a minor effect on the modal parameters identified from the sensor data. We have compared our algorithms with other data compression algorithms for structural health monitoring. Results show that our algorithms can achieve 80% ~ 100% higher compression ratios with the same signal-restoration quality","PeriodicalId":58925,"journal":{"name":"Digital Communications and Networks","volume":"1 1","pages":"823-829"},"PeriodicalIF":0.0000,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"Efficient Data Compression in Wireless Sensor Networks for Civil Infrastructure Health Monitoring\",\"authors\":\"Shengpu Liu, Liang Cheng\",\"doi\":\"10.1109/SAHCN.2006.288567\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present an efficient sensor data compression process for civil infrastructure health monitoring applications. It integrates lifting scheme wavelet transform (LSWT) and distributed source coding (DSC), which can reduce the raw data size by 1:27 to 1:80 while having a minor effect on the modal parameters identified from the sensor data. We have compared our algorithms with other data compression algorithms for structural health monitoring. Results show that our algorithms can achieve 80% ~ 100% higher compression ratios with the same signal-restoration quality\",\"PeriodicalId\":58925,\"journal\":{\"name\":\"Digital Communications and Networks\",\"volume\":\"1 1\",\"pages\":\"823-829\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Digital Communications and Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SAHCN.2006.288567\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Communications and Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAHCN.2006.288567","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Efficient Data Compression in Wireless Sensor Networks for Civil Infrastructure Health Monitoring
In this paper, we present an efficient sensor data compression process for civil infrastructure health monitoring applications. It integrates lifting scheme wavelet transform (LSWT) and distributed source coding (DSC), which can reduce the raw data size by 1:27 to 1:80 while having a minor effect on the modal parameters identified from the sensor data. We have compared our algorithms with other data compression algorithms for structural health monitoring. Results show that our algorithms can achieve 80% ~ 100% higher compression ratios with the same signal-restoration quality
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