
NSF Org: |
CNS Division Of Computer and Network Systems |
Recipient: |
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Initial Amendment Date: | August 25, 2012 |
Latest Amendment Date: | August 25, 2012 |
Award Number: | 1250180 |
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: | September 1, 2012 |
End Date: | August 31, 2015 (Estimated) |
Total Intended Award Amount: | $200,000.00 |
Total Awarded Amount to Date: | $200,000.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
1314 S MOUNT VERNON AVE WILLIAMSBURG VA US 23185 (757)221-3965 |
Sponsor Congressional District: |
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Primary Place of Performance: |
Office of Sponsored Programs Williamsburg VA US 23187-8795 |
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): | CSR-Computer Systems Research |
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): | 47.070 |
ABSTRACT
Modern smartphones are notorious for consuming energy far too quickly, especially for access to the Internet through wireless radios.
Although certain advances have been made on the hardware side such as better batteries, this research is focused on improving energy management software to make better use of existing batteries.
Considering that the frequency of use of these radios is spurred by the popularity of smartphone applications, the wide availability of applications, for instance the Android Market has over 500,000 registered applications, shows that the need to save smartphone radio energy is highly relevant and urgent today.
Different from existing research, this high-risk high-reward research takes a network traffic aware approach to save smartphone radio energy. The main intellectual merits include: (1) Solutions for determining low priority delay tolerant smartphone applications without assistant from application developers. (2) Solutions for determining delay tolerant traffic periods within high priority real-time smartphone applications. (3) Solutions for tracking low priority delay tolerant applications, and delay tolerant traffic periods in high priority real-time applications through the system to optimize radio energy efficiency.
This research will extend smartphone battery lifetime with the potential to benefit over one billion smartphone users worldwide. User experience of smartphone usage will be enhanced. Industry smartphone practice will also be improved. This research will be integrated into three courses, and also benefit research of female graduate students and undergraduate students. Special distribution efforts are also planned to increase the number of women and girls in computing.
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.
This project aims to save smartphone energy with a network traffic aware approach. We start this project by research on two main aspects: exploiting application priority for energy savings, and exploiting delay tolerant time periods within high priority, i.e. delay-sensitive, applications for energy savings.
First, we invent a set of novel solutions that prioritize different applications, in terms of their delay tolerance, based on network traffic analysis, and then use it to optimize smartphone radio energy. For example, our SAPSM solution is able to learn a smartphone user’s delay-tolerance, in terms of whether she/he can tolerant 100ms~200ms delay for specified applications, through a network traffic analysis based machine learning. This solution demonstrates up to 56% energy saving in an Android phone implementation.
Second, for applications that, in general, are considered delay-sensitive, including VoIP and live video streaming, we develop a set of novel solutions that identify delay-tolerant segments within delay-sensitive applications, and then during those segments put radio to low power mode to save energy. For example, our SiFi solution is able to put WiFi in low power mode while nobody is speaking through VoIP, achieving 40% energy saving for an Android smartphone.
Third, we research on several cross-cutting issues of our network traffic aware approach. We investigate the impact of daily WLAN broadcast traffic on smartphone energy efficiency, and devise a family of strategies to minimize energy consumption at the presence of dynamic broadcast traffic; We develop solutions that prioritize and terminate smartphone background traffic for energy saving; We analyze and compare traffic-aware and non-traffic features in prioritizing smartphone applications; We build typology of silences for VoIP traffic and compare it with un-typed SiFi solution; We utilize sensor traffic on smartphone to assist network traffic analysis; We also invent a hybrid Bluetooth/WiFi approach for network traffic optimization.
As a broader impact, our research on network traffic analysis enables us to apply a similar approach on storage traffic analysis for smartphone energy saving. We also optimize smartphone delay while saving energy. As another broader impact, we incorporate our smartphone energy saving solutions in smartphone-centered body sensor networks for healthcare applications.
In total, 17 academic papers have been published based on research results from this NSF project. Four PhD students (one female), 3 Master students (one female), two undergraduate students (one female), and one high school student have contributed to this project. Three U.S. patents have been filed (one awarded, two pending) based on our research results. Commercializing efforts are still ongoing.
Last Modified: 09/25/2015
Modified by: Gang Zhou