NOSS: Declarative Framework for Learning and Evaluating Probabilistic Models of Events in Sensor Networks

NOSS：用于学习和评估传感器网络中事件概率模型的声明性框架

• 批准号: 0721665
• 负责人: Himanshu Gupta
• 金额: --
• 依托单位: SUNY at Stony Brook
• 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
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0721665&HistoricalAwards=false
• 关键词: NOSS; Declarative; Framework; Learning; Evaluating

Programming a sensor network is difficult, since the programmer has to juggle low-level details of distributed computing with severe resource constraints, in the presence of noisy data and unreliable components. This project focuses on high-level specification of events and activities in sensor networks, since sensor networks are typically deployed for collaborative detection of events and activities. In particular, the project uses a declarative programming framework based on probabilistic logic for high-level specification of events in sensor networks. The probability distributions embedded in the user program are automatically learnt from training examples using standard machine learning techniques. The above approach facilitates high-level specification of sensor network applications, which is automatically translated into low-level distributed code running on individual sensor nodes. The user is thus freed from the burden ofworrying about low-level details.The project focuses on the following three goals. The first goal is development of a query engine for efficient distributed evaluation of probabilistic deductive queries in sensor networks. The second goal is to develop techniques for efficient estimation (and distributed re-estimation) of probability distributions embedded in the given program. The third goal is to test the viability of the developed techniques by building two appropriate testbeds. The research project has a significant impact on the ease of programming various sensor network applications. The results of the project are disseminated over the internet at http://www.cs.sunysb.edu/~hgupta/TrainSense.

对传感器网络进行编程是困难的，因为程序员必须在存在噪声数据和不可靠组件的情况下，在具有严重资源约束的分布式计算的低级别细节之间进行权衡。 该项目的重点是在传感器网络中的事件和活动的高层次规范，因为传感器网络通常部署的事件和活动的协作检测。 特别是，该项目使用了一个声明性的编程框架，基于概率逻辑的高层次规范的传感器网络中的事件。嵌入在用户程序中的概率分布是使用标准机器学习技术从训练示例中自动学习的。 上述方法有助于传感器网络应用程序的高级规范，该规范被自动转换为在各个传感器节点上运行的低级分布式代码。因此，用户不必再为底层的细节而烦恼。 第一个目标是开发一个查询引擎，用于传感器网络中概率演绎查询的高效分布式评估。 第二个目标是开发嵌入在给定程序中的概率分布的有效估计（和分布式重新估计）的技术。第三个目标是通过建立两个适当的测试平台来测试所开发技术的可行性。 该研究项目对各种传感器网络应用程序编程的便利性产生了重大影响。该项目的结果通过互联网http://www.cs.sunysb.edu/~hgupta/TrainSense传播。