(This page is in need of an update)
Since a number of years I have been concentrating on monitoring the mobility of people, assuming they carry a device such as an electronic badge or a smartphone.
Using active badges
Our first efforts toward crowd monitoring used active badges. Participants were required to use a home-brewed device that deployed a wireless so-called gossiping protocol to exchange information. The main challenge was to devise a large-scale wireless system in which the badges operated on a very low duty cycle (less than 1%, meaning they were passively asleep 99% of the time), while waking up all at the same time. During the active period they would be able to detect each other, which was the information we used to extract a proximity graph. This is a spatial-temporal graph reflecting which devices had detected each other (see the example). We managed to build real systems with over 400 badges and ran simulations demonstrating we could handle thousands of devices that stayed synchronized even in the presence of network partitions.
Much of the current efforts are targeted toward more practical crowd monitoring, namely through scanning of WiFi-enabled personal devices such as smartphones. There are important differences with using badges. First, because so many people carry a smartphone, large-scale experiments with thousands of devices become possible. We have monitored multi-day festivals with over 100,000 participants. Second, WiFi data is extremely noisy, meaning that there is a tremendous data-analytics problem before we can even draw conclusions. Third, because smartphones do not detect each other, we have essentially lost a very powerful instrument: our proximity graphs. Fourth, because we are unintrusively monitoring personal devices, there are serious privacy issues to deal with.
Again, we run experiments with indoor and outdoor (see picture) sensors, previously provided by BlueMark Innovations.
Our current research concentrates on increasing the accuracy of detections (and thus the data analyses) and preserving privacy, which form part of our Living Smart Campus project at the UT. At the same time, we’re putting efforts in automatically detecting structure in proximity graphs and also to adjust smartphones so that they can detect each other directly. Much of this work is done in collaboration with the Polytechnical University of Bucharest.
An important component of this work is to monitor crowd flows while preserving privacy by design. This has turned out to be a nasty problem that has not been addressed enough. We are now looking at solutions that will allow a person to digitally hide in a crowd (known as k-anonymity) while still permitting accurate counting of, for example, the number of people traveling from location A to B.
To be clear on one thing: we do not collect MAC addresses or other personal information. In a nutshell, our ICT service provides us with tuples <ID, sensor, time> where the ID has been derived from a detected MAC address through a provably secure one-way hashing function. That function is changed every 24 hours, making it impossible to identify devices for longer than a day. If you need more information, please contact me.