
NSF Org: |
CNS Division Of Computer and Network Systems |
Recipient: |
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Initial Amendment Date: | September 9, 2017 |
Latest Amendment Date: | September 9, 2017 |
Award Number: | 1718400 |
Award Instrument: | Standard Grant |
Program Manager: |
Alhussein Abouzeid
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | October 1, 2017 |
End Date: | January 31, 2021 (Estimated) |
Total Intended Award Amount: | $110,000.00 |
Total Awarded Amount to Date: | $110,000.00 |
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: |
940 Grace Hall Notre Dame IN US 46556-5708 |
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, CPS-Cyber-Physical Systems |
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
A vibrant and healthy wireless network edge is essential to the modern economy. New technologies such as the Internet of Things, self-driving vehicles, and a host of new automation technologies rely on robust, high-speed wireless technology for operation. Unfortunately, the demand for wireless connectivity has far outstripped the amount of wireless spectrum available. Using more spectrum and improving spectral efficiency are some longer-term solutions. A more immediate approach is to explore how to flatten the demand curve - reducing peak demands through aggressive time shifting and content caching. The focus of this research is to explore how the free storage space that sits unused on mobile devices can be leveraged by content providers and network operators to radically improve wireless performance. In short, the research seeks to push data during idle network times to avoid overloads during peak times. Whether it is a crowded sporting event or a crowded subway station, the intended result of the work is mobile devices that download data more quickly while operating with longer battery lifetimes. The work seeks to make wireless network performance in crowded venues remarkably better.
In this work, free space on mobile devices is made securely writable by trusted content providers and network operators, effectively allowing the network operator to push content dynamically to a device. The mechanisms and trade-offs that occur in large scale wireless systems (WiFi, cellular) present numerous challenges with respect to how and when to push content to the devices such that there is a net gain to overall wireless network system health. The work proposes to develop the architecture which seeks to allow devices in tandem with the network to sense redundant content. Mobile devices and the network operator constantly monitor and detect redundancy, triggering thresholds by which redundant content is efficiently pushed appropriately in the network through D2D (device to device) sharing and targeted broadcasts. Content is pre-staged during network idle periods or high bandwidth opportunities to time shift, improving the perceived Quality of Experience of the user in both responsiveness and efficiency. The project will develop prototype apps, create a Software Development Kit for Android and iOS, and conduct robust evaluations in demanding, dense environments.
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.
The wireless networks of today are essential to our everday lives. Data is constantly being moved and we often take for granted the speed and availability of wireless networks until of course, one experiences poor network performance. For many people, that poor performance tends to happen when it is needed most, in large and crowded venues such as athletic events, airports, and classrooms. The focus of this research was to figure out ways in how to make network performance considerably better by trading some of the unused storage on one's mobile to automatically pre-fetch popular content. In short, if content starts to become popular, could my device get that popular video or content from my neighbor's phone or could it be pushed to me when the network is less busy? Rather than going out to the Internet to find it, could we optimize locally and avoid the normal wireless network bottlenecks by removing redundant or unneeded data transfers?
A key part of the effort was to explore real-world network performance to see if such gains could be realized not just in the lab but in practice. To that end, we gathered data across multiple years of ultra-dense, high-network usage (football tailgates) and explored the extent to which our ideas for opportunistic pushing of content and the usage of left-over storage might be possible.
The intellectual merit of our work encompasses several key findings such as: (1) Network content has gotten too diverse and secure transmissions further reduce potential gains for dynamically learning what is popular in the last mile; (2) Device-to-device transfers are unlikely to be beneficial enough to overcome the security downsides; (3) The rise of short-video and rich mobile ads offer potential promise for taking advantage of left-over storage but it is unclear at this time that the benefits are sufficient.
The broader impacts of the work include: (1) Training of multiple graduate students that have entered or will enter the domestic US workforce (one in academia, multiple to industry); (2) The creation of rich datasets that can be leveraged by other researchers to improve network performance for dense WiFi environments.
Last Modified: 10/03/2021
Modified by: Aaron D Striegel
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