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NeTS/NOSS: ASPEN: Abstraction-based Sensor Programming Environment

NeTS/NOSS: ASPEN: Abstraction-based Sensor Programming Environment
NeTS/NOSS:ASPEN:基于抽象的传感器编程环境
批准号:
0721541
负责人:
Zachary Ives
金额:
$45.0万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2007
资助国家:
美国
项目状态:
已结题
起止时间:
2007-09-01 至 2012-08-31

项目摘要

项目成果

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中文摘要
翻译
随着传感设备变得越来越复杂,越来越广泛地部署在现实世界中,其功能也越来越先进,它们正在被部署在更丰富、更多样化和更复杂的应用中(例如提供访客指南、在家监控患者或管理工作流程)。该项目开发了一种新的通用编程模型,即基于组的编程,用于开发一类新兴的传感器应用程序,这些传感器应用程序监视异构数据并将其抽象为事件、现象和工作流等更高级别的概念。基于组的编程开发了一个统一的声明性框架,用于将异构数据集成到抽象组(设备集)和它们产生的数据流(视图)中;组成组和视图;同样重要的是,表达通信、安全和隐私约束。它综合了来自数据库和数据集成、实时系统和流算法的思想和技术,以便为应用程序开发提供更高层次的框架。该项目开发了一个高效的运行时支持层,用于跨各种不同类型的传感器设备进行基于组的编程,并具有利用底层网络和设备的底层属性的自动优化功能。最后,验证了该模型在医院和家庭医疗保健中的各种应用中的适用性。该项目将培训两名研究生和各种本科和/或高中水平的暑期学生,将开发传感器网络应用的研究生/本科课程,并将产生公开可用的软件。
英文摘要
As sensing devices become increasingly sophisticated, more widely deployed in the real world, and more advanced in their capabilities, they are being deployed in richer, more diverse, and more sophisticated applications (such as providing visitor guides, monitoring patients in the home, or managing workflows). This project develops a new general-purpose programming model, group-based programming, for developing an emerging class of sensor applications that monitor heterogeneous data and abstract it into higher-level concepts like events, phenomena, and workflows. Group-based programming develops a unified, declarative framework for integrating heterogeneous data into abstract groups (sets of devices) and the data streams (views) they produce; composing groups and views; and, equally importantly, expressing communication, security, and privacy constraints. It synthesizes ideas and techniques from databases and data integration, real-time-systems, and streaming algorithms, in order to provide a higher-level framework for application development. The project develops an efficient runtime support layer for group-based programming across a broad array of sensor devices of different types, with automatic optimization capabilities that exploit the underlying properties of the underlying network and devices. Finally, it validates the suitability of this model across a variety of applications in hospital and home health care. The project will train two graduate students and a variety of summer students at the undergraduate and/or high school level, will develop a graduate/undergraduate course in sensor network applications, and will result in publicly available software.
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