CRII: CPS: Towards Reliable Cyber-Physical Systems using Unreliable Human Sensors

CRII：CPS：使用不可靠的人体传感器实现可靠的网络物理系统

• 批准号: 1566465
• 负责人: Dong Wang
• 金额: $ 17.5万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1566465&HistoricalAwards=false
• 关键词: CRII; CPS; Towards; Reliable; Cyber

A growing number of Cyber-Physical Systems (CPS) domains, such as environment, transportation, energy, and disaster response, involve humans in non-trivial ways. Humans act as sensors in these scenarios when they contribute data (either directly or via sensors they own) that a CPS application can use. Using humans as sensors (commonly known as social sensing or crowdsensing) is an emerging paradigm, which provides unprecedented opportunities to sense the physical world in an inexpensive, versatile and scalable manner. However, these benefits are based on the assumption that the human-sensed data are reliable, but this is not always the case. In order for social sensing to become a viable component in CPS feedback loops, there is a critical need to understand the correctness of collected observations from unreliable individuals. This challenge is referred to as reliable social sensing. The objective of this project is to develop a new Reliable Social Sensing Model (RSSM) and system prototype, which enables correct reconstruction of states of physical environment from unreliable human sensors.This project leverages and innovates techniques in estimation theory and CPS to fill a critical gap in the rigorous analysis of human-sensed information, thereby providing a reliable social sensing component to build robust CPS with humans-in-the-loop. This project contains three key components. First, a RSSM will be developed to formally reason about the correctness of collective human observations and accurately assess the quality of analysis results. Second, a new reliable social sensing system prototype will be built to integrate the proposed RSSM with the state-of-the-art data processing techniques to handle different types of human sensed data. Third, by evaluating the proposed model and system through a real world social sensing application, the project will effectively validate the correctness of the RSSM and provide new insights into modeling humans as sensors for future research. The success of the project and follow-up work inspired by it could lead to a paradigm shift in CPS with human-in-the-loop by explicitly incorporating rigorous accuracy assessment into the development of new theories, systems and applications that rely on the collective observations from massive human sensors. The proposal is timely due to the increasing interests in social networks, big data, and human-in-the-loop systems, as well as the proliferation of computing artifacts that interact with or monitor the physical world. This research project will also contribute to the curriculum of CPS and Social Sensing courses, and will engage undergraduate and graduate students in STEM disciplines and from underrepresented groups.

越来越多的网络物理系统（CPS）领域，例如环境，运输，能源和灾难反应，涉及人类以非平凡的方式。 当人类可以使用CPS应用程序可以使用的数据（直接或通过其拥有的传感器）贡献数据时，它们在这些情况下充当传感器。将人类用作传感器（通常称为社交传感或人群）是一种新兴的范式，它提供了以廉价，多功能和可扩展的方式以廉价，多功能和可扩展的方式感知物理世界的前所未有的机会。但是，这些好处是基于以下假设：人类感知数据是可靠的，但并非总是如此。为了使社会感知成为CPS反馈循环中的可行组成部分，迫切需要了解不可靠的个体收集的观察结果。这个挑战称为可靠的社会感知。该项目的目的是开发一种新的可靠的社会传感模型（RSSM）和系统原型，该模型能够从不可靠的人类传感器中正确重建物理环境状态。这项项目的利用和创新技术在估计理论和CPS中的创新技术以填补了对人类敏感信息的严格分析，从而填补了可靠的comments comments-and and and and and and and and and and and comments-and-and-insty构建了comments-and and and and and and and and and consement-lobe。该项目包含三个关键组件。首先，将开发一个RSSM，以正式理解集体人类观察的正确性，并准确评估分析结果的质量。其次，将构建一种新的可靠社会传感系统原型，以将提出的RSSM与最新的数据处理技术集成在一起，以处理不同类型的人类感知数据。第三，通过通过现实世界的社会传感应用来评估所提出的模型和系统，该项目将有效地验证RSSM的正确性，并为将人类作为未来研究的传感器建模提供新的见解。 该项目启发的项目的成功和后续工作可能会通过将严格的精度评估纳入依赖大量人类传感器的集体观察结果的新理论，系统和应用的开发中，从而导致与人类融合的CPS范式转变。该提案及时归因于社交网络，大数据和人类在循环系统以及与物理世界相互作用或监测物理世界相互作用或监测的计算文物的扩散。该研究项目还将为CPS和社会传感课程的课程做出贡献，并将吸引本科生和研究生的STEM学科以及代表性不足的群体。