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

摘要：大型传感器网络通常被视为分布式数据库，其中信息查询是其基本操作。本项目旨在研究基于“主动查询转发”新范式的传感器网络中有效信息发现的框架。这种方法的基本原则是将查询视为在网络中有效移动以搜索所需信息的活动实体。该项目采用了多部分方法。算法设计用于查询引导、语义、自适应和优化；然后通过数学分析和计算模拟对这些进行评估。在实际实验平台上进一步验证了主动查询框架的重要组成部分。预期结果包括：(1)可扩展的查询框架，(2)其组成机制，以及(3)相关的评估和验证研究。总之，这些为下一代传感器网络的信息管理提供了一个基本的构建块。有效地利用这些构建模块可以在不牺牲信息质量的情况下，将传感器网络的生命周期提高到现有方法的数量级。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等广泛的大型传感器网络应用。该项目的成果将通过高质量的出版物、会谈和与几个工业团队的互动，及时广泛传播。该项目还与南加州大学的本科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，以及几名硕士/博士的培训。学生。

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.