This paper updates previous work on fitting traffic profiles. We use more modern statistical techniques to question (and refute) previous assumptions about heavy tails in statistics. In this case we believe that the best fit for traffic volume per unit time is the log-normal distribution. Tail distributions an have big impacts for capacity planning and for prediction of pricing (say 95th percentile).
This paper looks the problem of releasing time-series data when privacy is a concern. It uses information theory to look at what extra information could "leak" if our device sends motion data. For example, can users be reidentified or can features such as height and weight be determined. A machine learning framework is given that can produce a tradeoff between allowing useful data to pass through while distorting the signal minimally to disguise information we wish to be private.
This paper describes a C# library that can be used to build networked programs which can compile to several target hardware and software platforms. This greatly eases development and debugging. The system is tested using NetFPGA as a target and performs almost as well as hand tuned code.
This demo shows how Apple's iBeacon technology can be used to track groups of people who are moving together in a crowd.
This paper is a considerably expanded version of the INFOCOM paper.
Again it argues that TCP is no longer mainly controlled by loss and congestion but instead by algorithms and settings under the control of the sender or receiver deliberately or accidentally designed to restrict throughput for a variety of reasons (for example limiting video sending to the rate at which the viewer is watching).
It contains extended discussion of the methodology and in particular how flight and RTT data was extracted from passive traces.