NeTS-NOSS: SNI: A General and Robust Networking Architecture for Distributed Data Processing in Sensor Networks

NeTS-NOSS：SNI：传感器网络中分布式数据处理的通用且稳健的网络架构

• 批准号: 0625518
• 负责人: Carlos Guestrin
• 金额: $ 42.19万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0625518&HistoricalAwards=false
• 关键词: NeTS; NOSS; SNI; General; Robust

Sensor Network Inference (SNI) architecture is the first general and robust networking architecture developed specifically for inference in sensor networks that enables the rapid deployment of a wide range of complex large-scale querying, data processing and actuation tasks on a low-cost wireless sensornet. Unlike most previous approaches that focus on individual examples of such inference tasks (e.g., tracking or contour finding), our infrastructure is leveraged by a powerful abstraction of such tasks, Junction Trees, which enables the efficient solution of many inference problems, including probabilistic inference (e.g., sensor calibration and target tracking), regression (e.g., data modeling and contour finding), and optimization (e.g., actuator control, decision-making, and pattern classification). SNI is general and easy to deploy: the effective abstraction for a wide range of complex tasks enables the rapid deployment of novel sensornets applications. Furthermore, our approach is resource aware, efficient and adaptive: nodes have limited computational, communication and power resources, thus SNI automatically optimizes its communication pattern to reduce resource usage; this optimization is data driven, since the complexity of the high-level task is greatly dependent on the current state of the monitored phenomena. Robustness to node and communication failures is a fundamental element of SNI: Low-cost sensornets are prone to lossy communication, sensor and node failures; our architecture seeks to provide both theoretical and empirical robustness guarantees. Our evaluation process includes thorough testing on two different testbeds, with different hardware, in different locations. This evaluation is coupled with our education plan by using SNI in undergraduate and graduate classes.

传感器网络推理（SNI）体系结构是专门针对传感器网络推断的第一个通用且可靠的网络体系结构，可在低成本无线SensOrnet上快速部署一系列复杂的大规模查询，数据处理和驱动任务。 Unlike most previous approaches that focus on individual examples of such inference tasks (e.g., tracking or contour finding), our infrastructure is leveraged by a powerful abstraction of such tasks, Junction Trees, which enables the efficient solution of many inference problems, including probabilistic inference (e.g., sensor calibration and target tracking), regression (e.g., data modeling and contour finding), and optimization (e.g.,执行器控制，决策和模式分类）。 SNI是一般易于部署的：多种复杂任务的有效抽象使得可以快速部署新型的SensOrnets应用程序。此外，我们的方法是资源意识，高效和自适应的：节点的计算，通信和功率资源有限，因此SNI自动优化其通信模式以减少资源使用；该优化是数据驱动的，因为高级任务的复杂性在很大程度上取决于被监视现象的当前状态。对节点和通信失败的鲁棒性是SNI的基本要素：低成本的SensOrnet容易出现失去的通信，传感器和节点故障。我们的建筑旨在提供理论和经验鲁棒性保证。我们的评估过程包括在不同位置对带有不同硬件的两个不同测试床上进行彻底测试。通过在本科和研究生课程中使用SNI，该评估与我们的教育计划相结合。