|
Sponsor: National Science Foundation (NSF)
Duration: October 2019 - September 2022
Investigator: Dr. Jiang (Linda) Xie (PI)
Students: Haoxin Wang, Siqi Huang
 Overview:
Mobile edge computing (MEC) has emerged as a promising technology to overcome the challenges of executing latency-sensitive
and computation-intensive applications at resource-limited mobile devices, by pushing mobile computing, network control,
and storage resources to the edge of mobile wireless networks. The mobility support issue is considered as a critical component
to ensure the success of MEC.
The research objective of this project is to design, analyze, and evaluate new algorithms for providing seamless mobility support
in mobile edge computing (MEC) networks with integrated computing and communication activities.
The proposed design is aimed at minimizing service disruptions and performance degradation caused by user mobility in MEC.
This research is the first that systematically addresses the unique challenges encountered during the course of
seamless mobility support in MEC networks. This research offers fundamental building blocks and provides invaluable insights
towards seamless mobility in MEC networks.
Research Activities:
This project includes three research tasks to achieve the seamless mobility goal:
-
Smart handoff triggering: design smart handoff triggering schemes to achieve fast and accurate triggers
for seamless mobility support;
-
Fast service rebuilding: design fast service rebuilding processes for seamlessly restoring offloaded services
on the new MEC server after a handoff is triggered; and
-
Fair research allocation: design service allocation schemes to minimize performance degradation during mobility
caused by radio resource allocation unfairness.
Publications:
-
Haoxin Wang, BaekGyu Kim, Jiang Xie, and Zhu Han, "Energy Drain of the Object Detection Processing Pipeline
for Mobile Devices: Analysis and Implications,"
IEEE Transactions on Green Communications and Networking, vol. 5, no. 1, pp. 41-60, March 2021.
-
Haoxin Wang, Tingting Liu, BaekGyu Kim, Chung-Wei Lin, Shinichi Shiraishi, Jiang Xie, and Zhu Han, "Architectural Design Alternatives based on Cloud/Edge/Fog Computing for Connected Vehicles,"
IEEE Communications Surveys and Tutorials, vol. 22, no. 4, pp. 2349-2377, Fourth Quarter 2020.
-
Siqi Huang and Jiang Xie, "Pearl: A Fast Deep Learning Driven Compression Framework for UHD Video Delivery,"
Proc. IEEE International Conference on Communications (ICC 2021), June 2021.
-
Siqi Huang, Tao Han, and Jiang Xie, "A Smart-Decision System for Realtime Mobile AR Applications,"
IEEE Global Communications Conference (GLOBECOM), December 2019.
-
Haoxin Wang, BaekGyu Kim, Jiang Xie, and Zhu Han, "How Is Energy Consumed in Smartphone Deep Learning Apps? Executing Locally vs. Remotely,"
IEEE Global Communications Conference (GLOBECOM), December 2019.
Broader Impact:
This research will help generate innovative mobility support techniques for numerous applications based on the mobile edge
computing technology, e.g., autonomous driving, cognitive assistance, mobile health, home networking, and Internet of Everything.
It will also have significant impacts on research in emerging technologies with high mobility scenarios,
such as connected vehicles and unmanned aerial systems.
Education Activities:
-
Curriculum Enhancement: research results on mobile edge computing from this research
are incorporated into the graduate-level courses.
-
Graduate Student Mentoring: two PhD students are working on the research of this project.
-
Outreach to Industry: collaborative research on mobile fog computing is
conducted with Toyota InfoTechnology Center.
Software Code and Demo:
Software codes and demos from this project can be found at this GitHub site.
|
