NeTS-NOSS: Data-Centric Active Querying in Sensor Networks

NeTS-NOSS：传感器网络中以数据为中心的主动查询

• 批准号: 0435505
• 负责人: Bhaskar Krishnamachari
• 金额: $ 75万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0435505&HistoricalAwards=false
• 关键词: NeTS; NOSS; Data; Centric; Active

Abstract: Large scale sensor networks can often be viewed as distributed databases in which information querying is a fundamental operation.This project aims to research a framework for efficient information discovery in sensor networks, based on a new paradigm called "active query forwarding". The basic principle of this approach is to consider queries as active entities which move efficiently through the network in search of desired information.A multi-part methodology is employed in the project. Algorithms are designed for query guidance, semantics, adaptation, and optimization; these are then evaluated through both mathematical analysis and computational simulations. Important elements of the active querying framework are further validated on real experimental platforms.The expected results consist of: (1) a scalable querying framework, (2) its constituent mechanisms, and (3) the related evaluation and validation studies. Together, these provide a fundamental building block for information management in next-generation sensor networks. Utilizing such building blocks efficiently can potentially improve sensor network lifetimes by orders-of-magnitude over existing approaches, without sacrificing information quality. The techniques developed in this project are useful for a wide range of large-scale sensor network applications, from industrial process control to structural health monitoring.The results of the project are to be disseminated widely and in a timely manner through high quality publications, talks, and interactions with several industrial teams. The project is also closely integrated with the undergraduate and graduate networks curriculum at the University of Southern California, including new courses on sensor and ad-hoc networks and the training of several M.S./Ph.D. students.

摘要：大规模的传感器网络通常被视为分布式数据库，其中信息查询是最基本的操作，本项目旨在研究一种基于“主动查询转发”的传感器网络信息发现框架。该方法的基本原理是将查询看作是在网络中有效移动的主动实体，以搜索所需的信息。算法被设计为查询指导，语义，适应和优化，然后通过数学分析和计算模拟来评估。在真实的实验平台上进一步验证了主动查询框架的重要组成部分，包括：（1）一个可扩展的查询框架，（2）其组成机制，（3）相关的评估和验证研究。总之，这些为下一代传感器网络中的信息管理提供了基本的构建块。有效地利用这些构建块可以潜在地通过现有方法的数量级来提高传感器网络的寿命，而不会牺牲信息质量。该项目开发的技术可用于从工业过程控制到结构健康监测的各种大规模传感器网络应用。该项目的结果将通过高质量的出版物、讲座和与几个工业团队的互动广泛而及时地传播。该项目还与南加州大学的本科生和研究生网络课程紧密结合，包括传感器和ad-hoc网络的新课程以及几个硕士的培训。博士学生