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, 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.
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.
The UT Campus app
A project closely related to much of my research on crowd monitoring is developing an app that will help you find your way on our beautiful campus. I consider it as a wonderful example where researchers and support services meet in innovation. The cool thing about the app is that it not only tells you lots about the campus, notably where and when events are taking place, but that it can guide you to where you want to be. We started with implementing traditional (outdoor) navigation, but have now reached the phase in which we’re integrating indoor navigation as well. To this end, we’re testing techniques that have been developed by our own Pervasive Systems group and which are now being rolled out by the startup Locus Positioning. Lots of people are collaborating in this project, but special credits definitely go to the leading team, Myrthe Swaak and Lucien Nijland. Download for iOS or Android and enjoy!