Lena Mashayekhy
University of Delaware, DE·Computer Science·Early-career
Smart devices such as sensors, phones, and vehicles, together called the Internet of Things (IoT), generate huge amounts of data. Much of it must be processed quickly.
- Fields
Computer Science
Engineering
- Topics
edge computing
Internet of Things
IoT
- Grants
- 1 known grant, through Apr 2027
What they work on
- Develop a data-driven method to find mobility patterns of Internet of Things devices that lead to quality-of-service violations in multiaccess edge computing systems.
- Design proactive task offloading and service placement by combining community detection, which finds groups of linked devices, with multidimensional network flow modeling.
- Build a learning-based approach that acts on its own when system changes threaten service quality, and optimize where mobile edge servers are placed and moved.
- Design a reverse auction and a two-sided mechanism that encourage location-flexible devices and edge servers to move, balancing load and improving response time.
More about the research
Multiaccess edge computing (MEC) lets these devices hand off heavy, time-critical computing jobs to nearby edge servers rather than distant data centers. But users and devices move, which makes demand hard to predict across space and time. This movement makes it difficult to guarantee quality of service (QoS), meaning reliable speed and performance.
Why it matters
Mobile apps and IoT systems that need real-time responses could run faster and more reliably if edge computing systems anticipate where users and devices will move. Better use of mobile edge servers could reduce data movement and improve service for end users. This could enable applications that current systems cannot support.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Hulya Seferoglu
University of Illinois Chicago, IL·Engineering·Early-career
Characterize the cost-benefit trade-offs of coded computation, which adds redundancy to tolerate slow or failed workers, in practical edge computing systems.
Hui Guan
University of Massachusetts, MA·Computer Science·Early-career
Build deep learning systems that adjust in real time which deep neural networks run, responding to task difficulty, changing request loads and resource sharing on edge servers and Internet of Things devices.
Fahad Dogar
Tufts University, MA·Computer Science
Measure how much delay, or slack, popular applications can tolerate, through user studies and application analysis at the intersection of networking and human-computer interaction.
The school
University of Delaware: 22 labs on PI Pages, $466M a year on research, 298 research doctorates a year.
Reach out
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