Presentation at ICCCN 2018 on FMNC

[August 1st, 2018] Great conference at ICCCN 2018 in Hangzhou, China.  Had a chance to catch up with lots of familiar faces now having attended ICCCN for the past 7 years straight.  Great to meet up with my former student, Qi Liao, who also had a paper at ICCCN.  On Wednesday, I presented our base work on Fast Mobile Network Characterization (FMNC) which was an invited paper at ICCCN.  A really solid work done by my student that has laid the foundation on much of our work in this area.

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Paper: Leveraging Frame Aggregation for Estimating WiFi Available Bandwidth

(March 14, 2017) Our paper entitled “Leveraging Frame Aggregation for Estimating WiFi Available Bandwidth” was accepted into IEEE SECON 2017 (26% acceptance rate).  The work is the second one under the Fast Mobile Network Characterization umbrella.  This work focuses on the potential to use reflected aggregation (client-mod free) as observed by A-MPDU frame characteristics for inferring the available bandwidth of a link ranging across a wide variety of speeds.

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Paper: Redundancy Elimination Might Be Overrated: A Quantitative Study on Wireless Traffic

(March 7, 2017) Our paper entitled “Redundancy Elimination Might Be Overrated: A Quantitative Study on Wireless Traffic” was accepted to the INFOCOM workshop covering edge computing, caching, and offloading.  This work drew from our NSF EAGER grant covering redundancy across a wide variety of environments (South Shore commuter train, University Relations tent, classroom).

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Paper: Exploring Compliance: Observations from a Large Scale Fitbit Study

(February 24th, 2017) Our paper entitled “Exploring Compliance: Observations from a Large Scale Fitbit Study” to SocialSens 2017 received a notification that it is accepted. The paper is a joint work across much of the collaborating faculty with the NetHealth study. Louis Faust served as the lead author and looked at the relationship of various aspects of compliance and the underlying factors with regards to compliance via various machine learning approaches.  Nice work and congrats to Louis on a job well done!

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