O. Gnawali, David Moss, Dmitry Shirkalin, Russ Clark, Brian Jones, William Eason
{"title":"Poster Abstract: Scaling IoT Device APIs and Analytics","authors":"O. Gnawali, David Moss, Dmitry Shirkalin, Russ Clark, Brian Jones, William Eason","doi":"10.1109/IPSN.2016.7460712","DOIUrl":null,"url":null,"abstract":"Many IoT applications consist of two types of actions: interaction with the device, which can be sensors or actuators, and interaction with the data, for example, to reveal insights. In this poster, we introduce a software stack that provides these functionalities in a scalable manner. The API for device interaction is designed with generality in mind so that widest possible array of devices are supported and in large numbers. The analytics framework, called Composer, is designed to allow user code to be easily integrated into data analytics. We present the design, describe the implementation and deployment, and present some evaluation results. We share the performance data from a live deployment with tens of thousands of active users to demonstrate the scalability of the design.","PeriodicalId":137855,"journal":{"name":"2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPSN.2016.7460712","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Many IoT applications consist of two types of actions: interaction with the device, which can be sensors or actuators, and interaction with the data, for example, to reveal insights. In this poster, we introduce a software stack that provides these functionalities in a scalable manner. The API for device interaction is designed with generality in mind so that widest possible array of devices are supported and in large numbers. The analytics framework, called Composer, is designed to allow user code to be easily integrated into data analytics. We present the design, describe the implementation and deployment, and present some evaluation results. We share the performance data from a live deployment with tens of thousands of active users to demonstrate the scalability of the design.