Behnam Dezfouli
Santa Clara University, CA·Computer Science
WiFi is everywhere, yet it handles two demanding kinds of devices poorly. Small battery-powered Internet of Things (IoT) devices, such as cameras, thermostats and smoke detectors, struggle to save energy and deliver data on time when they share busy networks with phones and laptops.
- Fields
Computer Science
Engineering
- Topics
WiFi
802.11
wireless networking
- Grants
- 1 known grant, through Apr 2027
What they work on
- Build a scalable, programmable software framework for monitoring the WiFi (802.11) stack, along with a reference testbed and traffic generation methods that mimic real-world deployments.
- Develop packet delivery delay prediction to improve the energy efficiency and timeliness of battery-powered IoT devices sharing WiFi networks with phones and laptops under changing traffic.
- Create a user-space WiFi stack framework with programming primitives that make it easier to write deterministic communication schedulers for mission-critical systems.
- Design scheduling algorithms that enforce end-to-end delay guarantees for industrial automation control loops running over WiFi in dynamic environments.
More about the research
Industrial systems, for their part, need guaranteed, predictable delays between sensors and controllers. WiFi offers enough speed for these systems, but its software stack is complex and unpredictable, which makes building such applications impractical. This project develops methods, algorithms and software to make WiFi work for both kinds of applications.
Why it matters
More energy-efficient and timely WiFi could extend battery life and responsiveness for smart-home and campus IoT devices. These include security cameras and smoke detectors. Deterministic WiFi scheduling could let factories use low-cost, widely available WiFi for industrial automation control loops that today need specialized networks.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
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.
Tianyu Zhang
University of Iowa, IA·Engineering
Develop machine-learning methods that predict wireless channel conditions and interference on the factory floor, so 5G/6G radio resources can adapt in real time.
Miao Pan
University of Houston, TX·Computer Science
Design software-hardware co-designed model pruning and adaptive quantization schemes that exploit the sparsity of model updates to reduce computation and memory access during federated learning on a single 5G mobile device.
The school
Santa Clara University: 8 labs on PI Pages, $8M a year on research, 24 research doctorates a year.
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