| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | September 7, 2010 |
| Latest Amendment Date: | November 6, 2014 |
| Award Number: | 1035152 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Sylvia Spengler
sspengle@nsf.gov (703)292-7347 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 15, 2010 |
| End Date: | August 31, 2014 (Estimated) |
| Total Intended Award Amount: | $250,000.00 |
| Total Awarded Amount to Date: | $278,800.00 |
| Funds Obligated to Date: |
FY 2011 = $16,000.00 FY 2013 = $12,800.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
5000 FORBES AVE PITTSBURGH PA US 15213-3815 (412)268-8746 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
5000 FORBES AVE PITTSBURGH PA US 15213-3815 |
| 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): |
Information Technology Researc, Special Projects - CNS |
| Primary Program Source: |
01001112DB NSF RESEARCH & RELATED ACTIVIT 01001314DB 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
The objective of this research is to enable cyberphysical systems (CPS) to be context-aware of people in the environment and to use data from real-world probabilistic sensors. The approach is (1) to use radio tomography (RT) and RFID to provide awareness (location and potential identification) of every person in a building or area, and (2) to develop new middleware tools to enable context-aware computing systems to use probabilistic data, thus allowing new applications to exploit sometimes unreliable estimates of the environment.The intellectual merit of the proposal is in the development of new algorithms and models for building-scale RT with low radio densities and across multiple frequencies; the development of efficient multichannel access protocols for rapid and adaptive peer-to-peer measurements; the development of space-time and probabilistic data representations for use in stream-based context awareness systems and for merging ID and non-ID data; (4) and the development of a human context-aware software development toolkit that interfaces between probabilistic data and context-aware applications.
The proposal impacts broadly the area of Cyberphysical systems that reason about human presence and rely on noisy and potentially ambiguous (practical) sensors. The research has additional dramatic impact in: (1) smart facilities which automatically enforce safety, privacy, and security procedures, increasing the ability to respond in emergency situations and prevent accidents and sabotage; (2) elder care, to monitor for physical or social decline so that effective intervention can be implemented, extending the period elders can live in their own home, without pervasive video surveillance.
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.
The major goal of the project is to support probabilistic sensor data, to apply to domains of elder care and smart environments. On the CMU side, our goal is to collect and model probabilistic data collected about people, to provide them support in their everyday lives.
In our work, we have built both a toolkit to support the reasoning about probabilistic or uncertain sensor input, to automatically answer questions for users (without programmer intervention) about how a system responds or behaves when dealing with uncertain input, and to help programmers build applications that can deal with probabilstic input. Previously the state of the art systems assumed that sensor data was always certain and correct.
In addition, now that we can reason about uncertain input, we can build models of that uncertain input, including models of how people behave and the activities they perform. A large part of our efforts were in improving our ability to produce accurate models that can detect and predict what activities people are performing. These models are necessary to support elders living independently in smart spaces. When combined with our collaborators' work at the University of Utah, we can identify where people are and what they are doing with a minimal amount of training, and technology. This information can be used to record health behaviors, and provide interventions to support independent living.
Last Modified: 10/30/2014
Modified by: Anind K Dey
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