| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | September 7, 2012 |
| Latest Amendment Date: | July 31, 2016 |
| Award Number: | 1239423 |
| Award Instrument: | Standard Grant |
| Program Manager: |
David Corman
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2012 |
| End Date: | September 30, 2016 (Estimated) |
| Total Intended Award Amount: | $800,000.00 |
| Total Awarded Amount to Date: | $836,000.00 |
| Funds Obligated to Date: |
FY 2013 = $12,000.00 FY 2014 = $12,000.00 FY 2015 = $12,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
910 GENESEE ST ROCHESTER NY US 14611-3847 (585)275-4031 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Rochester NY US 14627-0140 |
| 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): |
Special Projects - CNS, CPS-Cyber-Physical Systems |
| Primary Program Source: |
01001314DB NSF RESEARCH & RELATED ACTIVIT 01001415DB NSF RESEARCH & RELATED ACTIVIT 01001516DB 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
Data-driven intelligence is an essential foundation for physical systems in transportation safety and efficiency, area surveillance and security, as well as environmental sustainability. This project develops new computer system infrastructure and algorithms for self-sustainable data-driven systems in the field. Research outcomes of the project include (a) a low-maintenance, environmentally-friendly hardware platform with solar energy harvesting and super capacitor-based energy storage, (b) virtualization software infrastructure for low-power nodes to enable inter-operability among distributed field nodes and from/to the data center, and (c) new image and data processing approaches for resource-adaptive fidelity adjustment and function partitioning. The synergy between the self-sustainable hardware, system software support, wireless communications management, and application data processing manifests through global coordination for quality-of-service, energy efficiency, and data privacy.
In broader impacts, this project enables data-driven intelligence in the field for important physical system domains. Integration of the technologies involved is accomplished through real-world system deployment and experimentation, including an intelligent campus traffic and parking management system and collaborative work with industry collaborators. The results of this project will further enhance the technological competitiveness for US industries in key areas such as intelligent transportation. The education component includes cross-disciplinary curriculum enhancements and the development of a new instructional platform for realistic experiments with cyber-physical systems. Within the scope of this project, the PIs perform mentoring and outreach activities to recruit/retain women and minorities in science and engineering.
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.
Data-driven intelligence is an essential foundation for physical systems in transportation safety and efficiency, area surveillance and security, as well as environmental sustainability. This project has developed a new computer system infrastructure for the efficient utilization of low-power resources for data processing at the sources. The synergy between the self-sustainable hardware, system software support, and application data processing manifests through global coordination for quality-of-service, energy efficiency, and data privacy.
A particular outcome of this project is the construction of the first working prototype of a data-intensive field system relying solely on supercapacitors for energy buffering. Such a system eliminates the negative environmental impact of rechargeable batteries and substantially improves the energy buffering reliability. Our system includes integrated software / hardware engineering to tackle energy modeling, budgeting, and adaptive control problems. Our working prototype has been successfully deployed at a University of Rochester campus building rooftop to analyze patterns of traffic in the vicinity. A picture of our deployed system is attached to this report. A picture of our open-source energy harvester (named UR-SolarCap) is also attached.
We have also developed novel communication protocols to facilitate field-deployed sensing systems. A particular contribution is to dynamically adjust antenna use for data transmission and reception, leading to best energy efficiency and operating lifetime. We have enhanced the widely used ns-3 network simulation framework to explicitly introduce the concept of energy harvester / predictor, and include a model for a supercapacitor energy source. Our developed technologies have also had international impacts, including a collaboration with researchers from the University of Ghana to monitor water quality, and work with researchers at the Sri Lanka Institute of Information Technology to tag elephants for position monitoring in the field.
Last Modified: 10/20/2016
Modified by: Wendi B Heinzelman
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