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 the Raphtory system which is used to analysis large-scale time-varying graph systems. It can ingest streaming graph information and store the complete graph history. It enables queries to be made over the graphs at different points in that graph's history.
This work in progress was accepted as a Demo and at the Doctoral workshop for DEBS (Distributed and Event-Based Systems). It shows the early development of a system that ingests events and can create (and eventually query) a dynamic graph.
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 looks at how sensor measurements in mobile phones can be used to determine when people are talking in a group.
This paper describes preliminary results on analysing the movements of people walking next to each other. The data is collected from mobile phone movement sensors carried by experimental subjects. The accelerometers on mobile phones show synchronisation when compared. Correlations between time series are used to infer the presence of a third party with when people are walking. This is preliminary work on a small data set with only three participants.
This paper reflects on experience gained from implementing OpenFlow on an access device. The architecture used involves a box at the front of the device for tagging traffic and using those tags to make the device present as a single large OpenFlow switch distributed in space. The system has been implemented and tested using OFtest.
Code is at https://github.com/richardclegg/xcpd
This paper looks at the phenomenon of long-range dependence. It shows that certain long-range dependent models give answers which contain infinities and also that this behaviour will not be detected by a naive modelling approach. The work is extended in the JCCS paper
This paper is the first to describe FETA a process for analysing stochastic models for graph evolution.