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

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。在这种情况下，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。数据完整性可能受到破坏，这不仅是因为用户、测量设备和应用程序的错误，还因为恶意主体可能注入不准确的数据，目的是欺骗数据用户。在数据质量和收集和保护这些数据的成本之间存在一个基本的权衡，例如，在传感器节点能量方面。该项目侧重于在考虑成本和能源限制的情况下，评估传感器网络数据流完整性问题的多方面解决方案。该解决方案的关键要素是：(a)支持基于来源的传感器数据可信度评估的循环框架，以及基于传感器提供的数据的传感器可信度评估；(b)持续更新传感器数据和节点信任分数的策略；(c)一个博弈论模型，用于分析和减轻由于试图破坏数据完整性的活跃对手而造成的风险；(d)传感器网络睡眠/唤醒调度和路由协议，平衡数据质量和能源效率的权衡。该项目还包括开发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、国土安全以及其他几个领域的应用产生了影响。

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.