Raphtory: Streaming analysis of distributed temporal graphs
Statistics of Internet traffic volumes
Finding dory in the crowd: Detecting social interactions using multi-modal mobile sensing
On the Distribution of Traffic Volumes in the Internet and its Implications
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).
Mobile Sensor Data Anonymization
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.
Emu: Rapid Prototyping of Networking Services
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.
Demo: Detecting group formations using Beacon technology
This demo shows how Apple's iBeacon technology can be used to track groups of people who are moving together in a crowd.
On rate limitation mechanisms for TCP throughput: a longitudinal analysis
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.
Detecting Group Formations using iBeacon Technology
This paper looks at how sensor measurements in mobile phones can be used to determine when people are talking in a group.
This talk is based around the Transactions on Networking paper. We use 232 traffic traces to establish that for "mid-large" internet link (backbone links or ingress/egress links from reasonable sized institutions) the traffic is well-modelled by a log-normal distribution.
The associated paper is here: