| NSF Org: |
CBET Division of Chemical, Bioengineering, Environmental, and Transport Systems |
| Recipient: |
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| Initial Amendment Date: | May 26, 2016 |
| Latest Amendment Date: | May 26, 2016 |
| Award Number: | 1637251 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Bruce Hamilton
CBET Division of Chemical, Bioengineering, Environmental, and Transport Systems ENG Directorate for Engineering |
| Start Date: | September 1, 2016 |
| End Date: | August 31, 2019 (Estimated) |
| Total Intended Award Amount: | $251,976.00 |
| Total Awarded Amount to Date: | $251,976.00 |
| Funds Obligated to Date: |
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| 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): | EnvS-Environmtl Sustainability |
| Primary Program Source: |
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| 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): | 47.041 |
ABSTRACT
1637251
Wang, Dong
The overall goal of this project is to develop a citizen science based smart water sensing system that accurately and efficiently detects drinking water contamination by using crowdsensing water quality data measured at the consumers' end. Monitoring drinking water quality at the point of use is vitally important to inform consumers about the water safety and to facilitate the decision-making process to minimize public health threats for a sustainable community. This project targets to: i) provide a brand new and transformative drinking water monitoring system by leveraging the collective power of crowdsensing in a community; ii) address fundamental challenges in crowdsensing and enable humans to be both sensors and users of the system; iii) integrate education and research through citizen science to enhance knowledge of common people on water quality and public health; and iv) engage government officials and residents (end users) throughout the process to address a real-world problem in a local community, and generate outcomes that will be broadly applicable in other places to enable more sustainable and connected communities.
In this project, the PIs plan to develop a new Smart Water Sensing (SWS) system to reliably monitor the water contamination levels in a local community (Granger, IN) and a novel Crowdsensing Data Analysis Engine (CDAE) to address the data reliability and data sparsity challenges of using crowdsensing data. The research is a novel combination of two distinct disciplines: computer science and environmental engineering. The development of the proposed SWS system is exploratory given little prior work, but the success of this project would help to make crowdsensing a reliable alternative that transforms the household drinking water quality monitoring process.
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.
In this project, the team develops a citizen science based smart water sensing system that can accurately and efficiently detect drinking water contamination at the consumers' end. The main outcomes of the project include the following two aspects. Intellectual Merit: i) the team has designed a new smart drinking water monitoring system by leveraging the collective power of general public in a community; ii) the team has developed a Crowdsensing Data Analytical Engine (CDAE) to address the fundamental challenges in cleaning and completing the crowdsensing data for the smart water sensing system. Broader Impacts: i) the project has enhanced knowledge of community participants on water quality and public health by integrating the education and research through citizen science; ii) the project has contributed to addressing a real-world problem in the local community by engaging government officials and residents (end users), and the generated outcomes could be broadly applicable in other places to enable more sustainable and connected communities; iii) the project has provided great educational and training opportunities that benefited a number of students ranging from elementary school to graduate school.
The research results have been successfully disseminated to the relevant research communities (8 journal and conference papers in good venues relevant to smart cities, big data analytics and crowdsensing) and the project provides the training opportunities for 5 Ph.D. students and 6 undergraduate students who directly contributed to the research and publications from this project.
Last Modified: 12/27/2019
Modified by: Dong Wang
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