The Rise of Augmented Intelligence in Edge Networks

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• Chenren Xu (Peking University)
• Thursday 05 October 2017, 11:00-12:00
• FW11, Computer Laboratory, William Gates Building.

If you have a question about this talk, please contact Liang Wang.

Edge networks have been historically serving as the ordinary data pipe as part of the Internet, but recently are expected to play increasingly critical roles for benefiting mobile/IoT applications in terms of lower the response time and energy consumption due to its proximity to the end devices. In this talk, we present two systems which coin this vision and demonstrate the potentials and benefits of the “augmented intelligence” when deployed at the network edge. The first system is called PassiveVLC, which is based on the idea of modulating the light retroreflection with a commercial LCD shutter to realize a passive optical transmitter and thus visible light backscatter communication. PassiveVLC system enables a battery-free tag device to perform passive communication with the illuminating LEDs over the same light carrier, is flexible with tag orientation, robust to ambient lighting conditions, and can achieve up to 1 kbps uplink speed. The second system, SoftStage, is a client-edge cooperative middleware that effectively leverages and manages in-network caching and services to perform reactive content staging to improve vehicular content delivery 1.5~10x without any assumption about the client mobility pattern.  

Bio Dr. Chenren Xu received his Ph.D. from Rutgers University, and his B.E. from Shanghai University. He has held postdoctoral and visiting positions at Carnegie Mellon University and AT&T Labs. He is the recipient of Gold Medal of Samsung Best Paper Award, Best Paper Nominee Award of ACM UbiComp’14 and Best Poster Award of ACM  SenSys’11. His research interests focus on wireless networking from the system perspective, including high mobility data networking for high-speed train/vehicle, low-power visible light communication for IoT/M2M and affective computing for health/expressiveness monitoring.

This talk is part of the Computer Laboratory Systems Research Group Seminar series.

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