| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | March 1, 2010 |
| Latest Amendment Date: | July 17, 2014 |
| Award Number: | 0954039 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Thyagarajan Nandagopal
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | August 1, 2010 |
| End Date: | July 31, 2016 (Estimated) |
| Total Intended Award Amount: | $424,673.00 |
| Total Awarded Amount to Date: | $424,673.00 |
| Funds Obligated to Date: |
FY 2011 = $79,978.00 FY 2012 = $82,401.00 FY 2013 = $84,921.00 FY 2014 = $122,422.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
426 AUDITORIUM RD RM 2 EAST LANSING MI US 48824-2600 (517)355-5040 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
426 AUDITORIUM RD RM 2 EAST LANSING MI US 48824-2600 |
| 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): | Networking Technology and Syst |
| Primary Program Source: |
01001112DB NSF RESEARCH & RELATED ACTIVIT 01001213DB NSF RESEARCH & RELATED ACTIVIT 01001314DB NSF RESEARCH & RELATED ACTIVIT 01001415DB 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
Wireless Sensor Networks (WSNs) have the potential to monitor the physical world at an unprecedented spatio-temporal scale. Recently, WSNs have been deployed for many emerging performance-critical applications such as monitoring important infrastructures (power grid and bridges) and detecting natural hazards (volcanoes and earthquakes). However, deeply integrated with the physical world, WSNs often suffer from significant performance variations caused by uncertainties including environmental noise, dynamics of physical phenomena, and network deployment inaccuracy.
This project develops a principled network design and analysis approach to performance assurance of WSNs. In contrast to existing heuristics-based solutions, this approach adopts data fusion, an advanced information processing scheme, to enable sensors to efficiently collaborate in delivering predictable network performance. This project has four aims: 1) A framework for analyzing spatio-temporal sensing performance based on established data fusion models. The analysis captures fundamental relationship between spatio-temporal coverage defined by event detection and false alarm probabilities, fusion models, network density, and physical uncertainties including noise and deployment inaccuracy. 2) Data fusion schemes that exploit mobile sensors to reconfigure the capability of a network in response to physical dynamics. 3) A unified fusion and communication architecture abstraction that allows developers to implement and optimize network protocols using group-level primitives. 4) A model-driven medium access control protocol that can achieve predictable throughput and delay in collaborative data processing.
This project will have impact in numerous critical applications that require stringent sensing and communication performance. The results of this project will be integrated into outreach activities, curriculum development, and student mentoring.
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.
Wireless Sensor Networks (WSNs) are increasingly deployed for numerous performance-critical application domains. Representative examples include security enforcement, important natural resources and infrastructures (water, soil, power grid, and civil structures) monitoring, natural and physical hazards (volcano, earthquakes, and fire) monitoring. Deeply integrated with the physical world, WSNs inevitably suffer from a variety of physical uncertainties including environmental noise, sensor biases, and random radio interference. First, the sensing performance of each node is undermined by biases in imperfect sensor hardware and the noises in measurements, resulting in complex impact on the overall network performance. In addition, the communication quality of wireless links suffers from random radio interference due to the unique many-to-one traffic pattern in many sensor networks.
This project develops a new fusion-centric approach to the design and analysis of performance-critical WSNs. First, we propose a new sensing performance control framework by integrating data fusion and calibration algorithms. In this framework, each sensor learns its local sensing model from noisy measurements and the local sensing models are then calibrated to a system-level sensing model. This approach fairly distributes computation overhead among sensors and significantly reduces the communication overhead of calibration. Such a system-level calibration methodology is particularly suitable for a class of low-power sensor networks that have tight energy and communication bandwidth budgets. Second, we develop a new interference measurement and modeling framework for fusion-based sensor networks. We propose a new regression-based interference model and analytically characterize its accuracy based on statistics theory, and develop novel protocols for measuring the proposed model with assured accuracy at run time. Third, we have applied the proposed sensing and communication performance control frameworks in several critical domains, including volcano monitoring, aquatic monitoring using robotic sensors, and data center thermal/energy management.
This project develops new systems and performance control frameworks that can significantly improve both sensing and communication performance of a broad class of low-power sensor networks that must operate in challenging physical environments. The results of this project advance the state of art in data fusion, signal processing, communication protocols, and sensor systems.
Last Modified: 02/14/2017
Modified by: Guoliang Xing
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