{"title":"Dynamix: An open plug-and-play context framework for android","authors":"D. Carlson, Andreas Schrader","doi":"10.1109/IOT.2012.6402317","DOIUrl":null,"url":null,"abstract":"Today's mobile devices represent exceptional foundations for wide-area Internet of Things (IoT) applications. However, the vast heterogeneity of real-world environments makes it challenging for applications to sense, understand and adapt to the user's continually evolving context. We're investigating a new community-based approach for context-aware computing, where advanced context sensing capabilities are dynamically deployed to mobile devices as plug-ins, and are made available to applications through only a few lines of code. Towards this end, we're developing Dynamix, an open plug-and-play context framework for Android. Dynamix runs as lightweight background service on the user's mobile device, modeling context information from the environment using the device itself as a sensing, processing and communications platform. Mobile applications request context support from Dynamix using simple application programming interfaces (APIs). Dynamix automatically discovers, downloads and installs the plug-ins needed for a given context sensing task. When the user changes environments, new or updated plug-ins can be deployed to the device at runtime, without the need to restart the application or framework. Dynamix comes with a growing collection of ready-made plug-ins, and provides open software developments kits (SDKs) and a scalable repository architecture, which enable 3rd party developers to quickly create and share new plug-ins types with the community. This paper presents the Dynamix approach, describes our prototype implementation and presents promising performance evaluation results.","PeriodicalId":142810,"journal":{"name":"2012 3rd IEEE International Conference on the Internet of Things","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"63","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 3rd IEEE International Conference on the Internet of Things","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IOT.2012.6402317","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 63
Abstract
Today's mobile devices represent exceptional foundations for wide-area Internet of Things (IoT) applications. However, the vast heterogeneity of real-world environments makes it challenging for applications to sense, understand and adapt to the user's continually evolving context. We're investigating a new community-based approach for context-aware computing, where advanced context sensing capabilities are dynamically deployed to mobile devices as plug-ins, and are made available to applications through only a few lines of code. Towards this end, we're developing Dynamix, an open plug-and-play context framework for Android. Dynamix runs as lightweight background service on the user's mobile device, modeling context information from the environment using the device itself as a sensing, processing and communications platform. Mobile applications request context support from Dynamix using simple application programming interfaces (APIs). Dynamix automatically discovers, downloads and installs the plug-ins needed for a given context sensing task. When the user changes environments, new or updated plug-ins can be deployed to the device at runtime, without the need to restart the application or framework. Dynamix comes with a growing collection of ready-made plug-ins, and provides open software developments kits (SDKs) and a scalable repository architecture, which enable 3rd party developers to quickly create and share new plug-ins types with the community. This paper presents the Dynamix approach, describes our prototype implementation and presents promising performance evaluation results.