NeTS-NOSS: Consistency Model Driven Deceptive Data Detection and Filtering in Wireless Sensor Networks

NeTS-NOSS：无线传感器网络中一致性模型驱动的欺骗性数据检测和过滤

• 批准号: 0721456
• 负责人: Weisong Shi
• 金额: $ 20万
• 依托单位: Wayne State University
• 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 2011-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0721456&HistoricalAwards=false
• 关键词: NeTS; NOSS; Consistency; Model; Driven

The observation and control of our physical world will expand dramatically using the temporally and spatially dense monitoring afforded by wireless sensor networks technology. Their success is nonetheless determined by whether the sensor networks can provide a high quality stream of data over a long period. Most previous efforts focus on devising techniques to save the sensor node energy and thus extend the lifetime of the whole sensor network. However, with more and more deployments of real sensor systems, in which the main function is to collect interesting data and to share with peers, data quality has been becoming a very important issue in the design of sensor systems. In this project, the investigator undertake a novel approach that detects deceptive data through considering the consistency requirements of data, and study the relationship between the quality of data and the multi-hop communication and energy-efficient design of networked sensor systems. The project consists of four components, including (1) formal models for data consistency and data dynamics, (2) APIs to manage the data consistency, (3) protocols to detect deceptive data and improve the quality of collected data, and (4) several cross-layer protocols to support data consistency and filtering of deceptive data. These four components are integrated into a prototype called Orchis. In addition to technical papers that report the research results, this project will also produce a suite of software tools that will be made available to the community.

利用无线传感器网络技术提供的时间和空间密集监控，我们对物理世界的观察和控制将急剧扩大。然而，它们的成功取决于传感器网络能否在很长时间内提供高质量的数据流。以往的大部分工作集中在设计技术来节省传感器节点的能量，从而延长整个传感器网络的生命周期。然而，随着实际传感器系统的部署越来越多，其主要功能是收集感兴趣的数据并与节点共享，数据质量已经成为传感器系统设计中的一个非常重要的问题。在本项目中，研究人员采用了一种新的方法，通过考虑数据的一致性要求来检测欺骗性数据，并研究数据质量与网络传感器系统的多跳通信和节能设计的关系。该项目由四个部分组成，包括(1)数据一致性和数据动力学的形式化模型，(2)管理数据一致性的API，(3)检测欺骗性数据并提高收集数据质量的协议，以及(4)支持数据一致性和欺骗性数据过滤的几个跨层协议。这四个组件被集成到一个名为Orchis的原型中。除了报告研究结果的技术论文外，该项目还将生成一套软件工具，供社区使用。