
NSF Org: |
CNS Division Of Computer and Network Systems |
Recipient: |
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Initial Amendment Date: | April 21, 2016 |
Latest Amendment Date: | May 2, 2017 |
Award Number: | 1566465 |
Award Instrument: | Standard Grant |
Program Manager: |
Marilyn McClure
mmcclure@nsf.gov (703)292-5197 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | May 1, 2016 |
End Date: | April 30, 2019 (Estimated) |
Total Intended Award Amount: | $174,998.00 |
Total Awarded Amount to Date: | $190,998.00 |
Funds Obligated to Date: |
FY 2017 = $16,000.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
940 GRACE HALL NOTRE DAME IN US 46556-5708 (574)631-7432 |
Sponsor Congressional District: |
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Primary Place of Performance: |
Fitzpatrick Hall Notre Dame IN US 46556-5637 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
CRII CISE Research Initiation, Special Projects - CNS |
Primary Program Source: |
01001718DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
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, in order for social sensing to become a viable component in human-centric CPS, 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, machine learning, 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 generates several key outcomes. First, a RSSM has been be developed to formally reason about the correctness of collective human observations. Second, the RSSM has been generalized to consider the constraints imposed by the physical world, the multi-modal sensing paradigms and the integration of AI and human intelligence. Third, the RSSM has been implemented on a scalable distributed computing system hosted at University of Notre Dame and validated through several real-world human-cyber-physical system applications (e.g., intelligent transportation systems, mobile crowdsensing). Fourth, the research results have been successfully disseminated to the relevant research communities (12 high quality journal and conference papers in good venues in CPS, networking, and big data analytics) and the project provides the training opportunities for 3 Ph.D. students and 5 undergraduate students who directly contributed to the publications from this project. Last but at least, the project also provides great outreach opportunities for local students and residents to visit the PI's lab and engage with the researchers on this project.
Last Modified: 06/11/2019
Modified by: Dong Wang
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