NeTS: Medium: Collaborative Research: A Comprehensive Approach for Data Quality and Provenance in Sensor Networks

NeTS：媒介：协作研究：传感器网络中数据质量和来源的综合方法

• 批准号: 0964294
• 负责人: Sonia Fahmy
• 金额: $ 30万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2014-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0964294&HistoricalAwards=false
• 关键词: NeTS; Medium; Collaborative; Research; Comprehensive

Sensor networks enable real-time gathering of large amounts of data that can be mined and analyzed for taking critical actions. As such, sensor networks are a key component of decision-making infrastructures. A critical issue in this context is the trustworthiness of the data being collected. Data integrity and quality decide the trustworthiness of data. Data integrity can be undermined not only because of errors by users, measurement devices and applications, but also because of malicious subjects who may inject inaccurate data with the goal of deceiving the data users. A fundamental tradeoff exists between data quality and the cost to gather and protect this data, e.g., in terms of sensor node energy. This project focuses on a multi-faceted solution to the problem of assessing integrity of data streams in sensor networks, taking into account cost and energy constraints. Key elements of the solution are: (a) a cyclic framework supporting the assessment of sensor data trustworthiness based on provenance, and sensor trustworthiness based on data that sensors provide; (b) strategies for continuously updating trust scores of sensor data and nodes; (c) a game-theoretic model to analyze and mitigate the risks due to active adversaries that try to undermine data integrity; (d) protocols for sensor network sleep/wake scheduling and routing that balance the data quality and energy efficiency tradeoff. The project also includes the development of tools for assessing data trustworthiness, and experimental evaluation of the system performance. The research has impact on healthcare, homeland security, and applications in several other domains.

传感器网络能够实时收集大量数据，这些数据可以被挖掘和分析，以便采取关键行动。因此，传感器网络是决策基础设施的关键组成部分。这方面的一个关键问题是所收集数据的可信度。数据的完整性和质量决定了数据的可信度。 数据完整性可能会受到破坏，不仅是因为用户、测量设备和应用程序的错误，还因为恶意主体可能会注入不准确的数据，以欺骗数据用户。在数据质量与收集和保护该数据的成本之间存在基本的权衡，例如，在传感器节点能量方面。该项目的重点是一个多方面的解决方案，在传感器网络中的数据流的完整性评估的问题，考虑到成本和能源的限制。该解决方案的关键要素是：（a）支持基于出处的传感器数据可信度评估和基于传感器提供的数据的传感器可信度评估的循环框架;（B）用于持续更新传感器数据和节点的信任分数的策略;（c）用于分析和减轻由于试图破坏数据完整性的活跃对手而引起的风险的博弈论模型;（d）用于传感器网络睡眠/唤醒调度和路由的协议，其平衡数据质量和能量效率折衷。该项目还包括开发用于评估数据可信度的工具，以及对系统性能进行实验评估。 该研究对医疗保健，国土安全和其他几个领域的应用产生了影响。