Award Abstract # 1566465
CRII: CPS: Towards Reliable Cyber-Physical Systems using Unreliable Human Sensors

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: UNIVERSITY OF NOTRE DAME DU LAC
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 2016 = $174,998.00
FY 2017 = $16,000.00
History of Investigator:
  • Dong Wang (Principal Investigator)
    dwang24@illinois.edu
Recipient Sponsored Research Office: University of Notre Dame
940 GRACE HALL
NOTRE DAME
IN  US  46556-5708
(574)631-7432
Sponsor Congressional District: 02
Primary Place of Performance: University of Notre Dame
Fitzpatrick Hall
Notre Dame
IN  US  46556-5637
Primary Place of Performance
Congressional District:
02
Unique Entity Identifier (UEI): FPU6XGFXMBE9
Parent UEI: FPU6XGFXMBE9
NSF Program(s): CRII CISE Research Initiation,
Special Projects - CNS
Primary Program Source: 01001617DB NSF RESEARCH & RELATED ACTIVIT
01001718DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 1714, 8228, 9251
Program Element Code(s): 026Y00, 171400
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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Chao Huang, DongWang "Unsupervised Interesting Places Discovery in Location-Based Social Sensing" Juried Conference Paper: 12th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS 16) , 2016
Chao Huang, Dong Wang "Exploiting Spatial-Temporal-Social Constraints for Localness Inference Using Online Social Media" Juried Conference Paper:The 2016 IEEE/ACM InternationalConference on Advances in Social Networks Analysis and Mining ASONAM 2016, , 2016
Daniel Zhang, Jose Badilla, Yang Zhang, Dong Wang "Towards Reliable Missing Truth Discovery in Online Social Media Sensing Applications" The IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM 2018) , 2018 10.1109/ASONAM.2018.8508655
Dong Wang, Nathan Vance, Chao Huang "Who to Select: Identifying Critical Sources in Social Sensing" Elsevier Knowledge Based Systems (KBS) , v.145 , 2018
Jermaine Marshall, Dong Wang "Mood-Sensitive Truth Discovery For ReliableRecommendation Systems in Social Sensing" Juried Conference Paper: 10th ACM Conference on Recommender Systems (Recsys 2016) , 2016
Jermaine Marshall, Munira Syed, Dong Wang "Hardness-aware Truth Discovery in Social Sensing Applications" Juried Conference Paper: 12th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS 16) , 2016
Yang Zhang, Nathan Vance, Daniel Zhang, Dong Wang. "Optimizing Online Task Allocation for Multi-Attribute Social Sensing" The 27th International Conference on Computer Communications and Networks (ICCCN 2018) , 2018 10.1109/ICCCN.2018.8487401
Yang Zhang, Yiwen Lu, Daniel Zhang, Lanyu Shang, Dong Wang "RiskSens: A Multi-view Learning Approach to Identifying Risky Traffic Locations in Intelligent Transportation Systems Using Social and Remote Sensing" 2018 IEEE International Conference on Big Data (IEEE BigData 2018) , 2018 10.1109/BigData.2018.8621996

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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