{"title":"演示摘要:CrowdMeter -使用仿真预测人群传感应用程序的性能","authors":"Manoj R. Rege, V. Handziski, A. Wolisz","doi":"10.1109/IPSN.2014.6846778","DOIUrl":null,"url":null,"abstract":"Predicting performance of crowd-sensing applications at large scale, in the pre-deployment phase, represents a significant challenge for developers. We demonstrate a solution to this problem in the form of a cloud-based emulation platform called CrowdMeter. Our platform emulates mobile devices and access network links, models human factors in crowd-sensing, and leverages virtualization through cloud infrastructure-as-service resources to model large scale crowd-sensing. In this demo we exhibit the capabilities of CrowdMeter by deploying VideoQuest, a simple crowd-sensing application, on hundreds of emulated mobile devices, and by measuring its performance.","PeriodicalId":297218,"journal":{"name":"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks","volume":"165 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Demonstration abstract: CrowdMeter — Predicting performance of crowd-sensing applications using emulations\",\"authors\":\"Manoj R. Rege, V. Handziski, A. Wolisz\",\"doi\":\"10.1109/IPSN.2014.6846778\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Predicting performance of crowd-sensing applications at large scale, in the pre-deployment phase, represents a significant challenge for developers. We demonstrate a solution to this problem in the form of a cloud-based emulation platform called CrowdMeter. Our platform emulates mobile devices and access network links, models human factors in crowd-sensing, and leverages virtualization through cloud infrastructure-as-service resources to model large scale crowd-sensing. In this demo we exhibit the capabilities of CrowdMeter by deploying VideoQuest, a simple crowd-sensing application, on hundreds of emulated mobile devices, and by measuring its performance.\",\"PeriodicalId\":297218,\"journal\":{\"name\":\"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks\",\"volume\":\"165 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IPSN.2014.6846778\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPSN.2014.6846778","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Demonstration abstract: CrowdMeter — Predicting performance of crowd-sensing applications using emulations
Predicting performance of crowd-sensing applications at large scale, in the pre-deployment phase, represents a significant challenge for developers. We demonstrate a solution to this problem in the form of a cloud-based emulation platform called CrowdMeter. Our platform emulates mobile devices and access network links, models human factors in crowd-sensing, and leverages virtualization through cloud infrastructure-as-service resources to model large scale crowd-sensing. In this demo we exhibit the capabilities of CrowdMeter by deploying VideoQuest, a simple crowd-sensing application, on hundreds of emulated mobile devices, and by measuring its performance.
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