| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | April 27, 2020 |
| Latest Amendment Date: | April 27, 2020 |
| Award Number: | 2027650 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Deepankar Medhi
dmedhi@nsf.gov (703)292-2935 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | May 1, 2020 |
| End Date: | October 31, 2021 (Estimated) |
| Total Intended Award Amount: | $100,000.00 |
| Total Awarded Amount to Date: | $100,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
2550 NORTHWESTERN AVE # 1100 WEST LAFAYETTE IN US 47906-1332 (765)494-1055 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
305 N. University Street West Lafayette, IN US 47907-2107 |
| 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): | COVID-19 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
This project proposes to measure and diagnose performance and reliability of the national 4G/4.5G mobile networks in face of COVID-19 pandemic. The objective is to meet the immediate need of assessing operational mobile networks, unveiling technical issues, understanding their pressing challenges, and proposing remedies without major infrastructure upgrade during this public health crisis.
The project exploits a novel on-device measurement approach. It leverages extensive efforts in building software tools and conducting large-scale measurement in the recent years to conduct five thrusts. First, it plans to conduct a longitudinal study to quantify how performance of US carrier networks change in face of COVID-19 and analyze why behind the pressing technical challenges. Second, it plans to design and assess data-driven device-side solutions to boost data performance without any infrastructure upgrade. Third, it plans to characterize and diagnose failures which likely occur more often during this period of time. Forth, it plans to assess security issues disclosed in prior studies and examine how possible attacks vary. Fifth, it plans to open up data and facilitate researchers in the community to empower long-term network innovations.
The project will help assess the mobile users' experience during the COVID-19 pandemic. In the long term, open datasets collected and released by this project will empower data-driven software solutions to efficiently utilize enhanced network capabilities and accelerate 5G innovations in mobile network research community.
The software tools and collected data will be available at http://milab.cs.purdue.edu/. The repository will be maintained for at least five years.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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.
In this project, we have conducted a lab-scale study to measure, diagnose, and improve performance of our national 4G/5G mobile networks in face of the COVID-19 pandemic. The sudden COVID-19 outbreak in the US and the world raised our big concerns on our national critical infrastructure for the Internet. Tens of millions of Americans had to work from home and live on the Internet, likely straining our operational mobile network infrastructure. Our objective is to unveil technical issues, diagnose their root causes, and propose remedies (without major infrastructure upgrade) for the operational mobile networks during this health crisis. We expect to meet the immediate needs during this period and the long-term goals for 5G innovations in the future.
We summarize our main outcomes in two aspects.
Intellectual Merit: We have conducted extensive on-device experiments to assess mobile broadband performance, availability and reliability over US carrier networks (AT&T, Verizon and T-Mobile). We have leveraged our developed tools of MobileInsight and MI-LAB, as well as large datasets collected by us in the past years. Our results have revealed a glimpse of mobile broadband performance of US carrier networks in face of the COVID-19 crisis. We are glad to see that network performance does not degrade during this period. Actually, our national mobile networks work well in the face of this public health crisis. This is partly because US carriers overprovision and network capacities are able to accommodate the increasing demands; It is also partly because of 5G rollout during this pandemic. US carriers do not slow down their 5G rollout except a short pause during the lockdown in Mid 2020. Our measurement studies have also confirmed and deepened our understanding in missing significant performance potentials in operational mobile networks, which was first revealed in our prior study in 4.5G networks but continues to exist in 4.5G/5G networks nowadays. We have explored data-empowered solutions to improve data speeds without upgrading network infrastructure. Our results have partly appeared in three publications at MobiCom'20, MobiCom'21 and ICNP'21.
Broader Impacts: The proposed solutions can improve mobile broadband speed and user experience with heavy mobile Internet traffic, without physical upgrades of operational 4G/5G networks. Datasets and software tools have been released to the public for reproduction and follows-up. Specifically, we have added more than 21K experiments (more than 3TB data) in this project. All the newly collected datasets have been still released at MI-LAB at http://milab.cs.purdue.edu/. Such datasets can be used to help third-party research groups, companies and starts-up to conduct activities out of their interests, develop products and publish papers at networking, security and machine learning conferences. Last but not the least, this project helps training undergrad and graduate students in information and communication technology, through the curriculum development, graduate/undergraduate advising, independent studies and online material dissemination.
Last Modified: 11/13/2021
Modified by: Chunyi Peng
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