I'm here in Berkeley for SenSys 2009, the premier venue on sensor network systems. There are 21 papers in the conference this year (out of about 120 submissions) and the quality of the papers is very high. The proceedings have been posted online here. I happen to be the program co-chair along with Jie Liu from MSR, so I feel compelled to blog about the conference.
This morning, Bill Weihl from Google gave the keynote presentation on "The Power of Energy Information." He talked about Google's PowerMeter system which allows consumers to track and visualize the power consumption in their homes -- potentially allowing people to learn about their patterns of electricity use and identify anomalies (like a broken air conditioner). Bill also talked about preliminary work at Google to shift power generation load through smart charging of plug-in electric vehicles, dynamically turning charging on and off across fleets of vehicles based on the grid's fluctuating capacity. It was a great talk and emphasized the potential for large-scale information on energy consumption.
A few highlights from some of my favorite talks today.
Om Gnawali from Stanford gave a talk on the Collection Tree Protocol, which is now the default routing protocol in TinyOS. CTP is the result of a substantial effort to increase robustness and reduce route maintenance overheads for large-scale spanning tree networks. Perhaps the best part of the paper is that they ran extensive measurements on about a dozen sensor network testbeds (including our own MoteLab testbed) to validate the protocol's performance. It is interesting to see the variation in performance across different settings.
Mike Liang from JHU gave a talk on RACNet, a sensor network designed to collect temperature and other data in large datacenters. Unlike "conventional" sensor networks, RACNet uses powered nodes (plugged into the USB port on a server rack) so the need to use, say, low-power MAC protocols is not a concern. RACNet focuses on a token-based data collection protocol to achieve high reliability across a range of node deployment densities, for networks with hundreds of nodes. This is an unusual setting for wireless sensor networks since one might imagine that it is trivial to have the servers themselves collect this data, but it's actually better to have a separate monitoring network that does not directly impact the servers.
Arvind Thiagarajan from MIT gave a talk on VTrack, which uses sparse localization data from mobile phones -- using a combination of GPS and WiFi localization -- to estimate travel time delays for individual road segments. This system provides high-resolution estimates of drive times through "crowdsourcing" mobile phone location data. This involves correcting noisy location estimates using an HMM to map trajectories to road segments. They evaluate the system on more than 800 hours of drive data from a fleet of taxicabs in Boston. I'm glad this paper made it into SenSys since it is not a paper about motes and TinyOS, and points the way towards future directions for the field.
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