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

摘要：大规模传感器网络通常被看作是分布式数据库，信息查询是其中的基本操作。本项目旨在研究一种基于主动查询转发的传感器网络高效信息发现框架。该方法的基本原理是将查询视为有效地在网络中移动以搜索所需信息的活动实体。算法被设计用于查询指导、语义、适应和优化；然后通过数学分析和计算模拟来评估这些算法。在真实的实验平台上对主动查询框架的重要元素进行了进一步的验证，预期的结果包括：(1)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。总而言之，这些都为下一代传感器网络中的信息管理提供了基本的构建块。在不牺牲信息质量的情况下，有效地利用这种构建块可能会比现有方法提高传感器网络的寿命几个数量级。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的广泛的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来广泛和及时地传播。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，包括传感器和临时网络的新课程，以及对几名硕士/博士学生的培训。

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.