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，如环境、交通、能源和灾难应对，以不平凡的方式涉及到人类。人类在这些场景中充当传感器，当他们提供CPS应用程序可以使用的数据(直接或通过他们拥有的传感器)时。使用人类作为传感器(通常称为社会感知或人群感知)是一种新兴的范式，它提供了前所未有的机会，以廉价、多功能和可扩展的方式感知物理世界。然而，这些好处是基于人类感知的数据是可靠的假设，但情况并不总是如此。为了使社会感知成为CPS反馈回路中的一个可行组件，迫切需要了解从不可靠的个人那里收集的观察结果的正确性。这一挑战被称为可靠的社会感知。该项目的目标是开发一种新的可靠的社会感知模型(RSSM)和系统原型，能够从不可靠的人类感觉中正确地重建物理环境的状态。该项目利用和创新估计理论和CPS中的技术来填补人类感知信息严格分析的关键空白，从而提供可靠的社会感知组件来构建具有健壮性的人在环中的CPS。该项目包含三个关键组成部分。首先，将开发RSSM，以形式化地推理集体人类观察的正确性，并准确评估分析结果的质量。其次，将建立一个新的可靠的社会感知系统原型，将所提出的RSSM与最先进的数据处理技术相结合，以处理不同类型的人类感知数据。第三，通过一个真实的社会感知应用对所提出的模型和系统进行评估，该项目将有效地验证RSSM的正确性，并为未来的研究提供将人类建模为传感器的新见解。该项目的成功以及受其启发的后续工作可能会通过明确地将严格的准确性评估纳入依赖于来自大量人类传感器的集体观测的新理论、系统和应用的开发中，导致人在回路中的CPS的范式转变。由于人们对社交网络、大数据和人在环路系统的兴趣与日俱增，以及与物理世界交互或监控物理世界的计算产品的激增，这项提议是及时的。这一研究项目还将对社区服务和社会感官课程的课程做出贡献，并将吸引STEM学科的本科生和研究生以及来自代表性不足群体的学生。