ACM SenSys 2011 just wrapped up this week in Seattle. This is the premier conference in the area of wireless sensor networks, although lately the conference has embraced a bunch of other technologies, including sensing on smartphones and micro-air vehicles. It's an exciting conference and brings together a bunch of different areas.
Rather than a full trip report, I wanted to quickly write up two highlights of the conference: The keynote by Michel Maharbiz on cybernetic beetles (!), and an awesome talk by James Biagioni on using smartphone data to automatically determine bus routes and schedules.
Keynote by Mich Maharbiz - Cyborg beetles: building interfaces between the synthetic and the multicellular
Mich is a professor at Berkeley and works in the interface between biology and engineering. His latest project is to adding a "remote control" circuit to a live insect -- a large beetle -- allowing one to control the flight of the insect. Basically, they stick electrodes into the beetle's brain and muscles, and a little microcontroller mounted on the back of the insect sends pulses to cause the insect to take off, land, and turn. A low-power radio on the microcontroller lets you control the flight using, literally, a Wii Mote.
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| Oh yes ... this is real. |
There has been a lot of interest in the research community in building insect-scale flying robots -- the Harvard RoboBees project is just one example. Mich's work takes a different approach: let nature do the work of building the flyer, but augment it with remote control capabilities. These beetles are large enough that they can carry a 3 gram payload, can fly for kilometers at a time, and live up to 180 days.
Mich's group found that by sending simple electrical pulses to the brain and muscles that they could activate and deactivate the insect's flying mechanism, causing it to take off and land. Controlling turns is a bit more complicated, but by stimulating certain muscles behind the wings they can cause the beetle to turn left or right on command.
They have also started looking at how to tap into the beetle's sensory organs -- essentially implanting electrodes behind the eye and antennae -- so it is possible to take electrical recordings of the neural activity. And they are also looking at implanting a micro fuel cell that generates electricity from the insect's hemolymph -- essentially turning its own internal fuel source into a battery.
Mich and I were actually good friends while undergrads at Cornell together. Back then he was trying to build a six-legged insect inspired walking robot. I am not sure if it ever worked, but it's kind of amazing to run into him some 15 years later and see he's still working on these totally out-there ideas.
EasyTracker: Automatic Transit Tracking, Mapping, and Arrival Time Prediction Using Smartphones
James Biagioni, Tomas Gerlich, Timothy Merrifield, and Jakob Eriksson (University of Illinois at Chicago)
James, a PhD student at UIC, gave a great talk on this project. (One of the best conference talks I have seen in a long time. I found out later that he won the best talk award - well deserved!) The idea is amazing: To use GPS data collected from buses to automatically determine both the route and the schedule of the bus system, and give users real-time indications of expected arrival times for each route. All the transit agency has to do is install a GPS-enabled cellphone in each bus (and not even label which bus it is, or which route it would be taking - routes change all the time anyway). The data is collected and processed centrally to automatically build the tracking system for that agency.
The system starts with unlabeled GPS traces to extract routes and locations / times of stops. They use kernel density estimation with a Gaussian kernel function to “clean up” the raw traces and come up with clean route information. Some clever statistical analysis to throw out bogus route data.
To do stop extraction, they use a point density estimate with thresholding for each GPS location, which results in clusters at points where buses tend to stop. This will produce a bunch of "fake" stops at traffic lights and stop signs - the authors decided to err on the side of too many stops than too few, so they consider this to be an acceptable tradeoff.
To extract the bus schedule, they look at the arrival times of buses on individual days and use k-means clustering to determine the “centroid time” of each stop. This works fine for first stop on route (which should be close to true schedule). For downstream stops this data ends up being to be too noisy, so instead they compute the mean travel time to each downstream stop.
Another challenge is labeling buses: Need to know which bus is coming down the road towards you. For this, they use a history of GPS traces from each bus, and build an HMM to determine which route the bus is currently serving. Since buses change routes all the time, even during the same day, this has to be tracked over time. Finally, for arrival time prediction, they use the previously-computed arrival time between stops to estimate when the bus is likely to arrive.
I really liked this work and the nice combination of techniques used to take some noisy and complex sensor data and distill it into something useful.