Small-form-factor, low-power wireless sensors - motes - are convenient to deploy, but lack the bandwidth to capture and transmit raw high-frequency data, such as human voices or neural signals, in real time. Local filtering can help, but the right filter settings depend on changing ambient conditions and network effects such as congestion, which makes them dynamic and unpredictable. Mote collection systems for high frequency data must support iteratively tuned, deployment-specific filter settings as well as fast sampling.

VanGo, is a software system for high-frequency data collection, that achieves these goals via integrated processing across network tiers. Bandwidth-limited sensor nodes reduce data in network but rely on microservers, which have greater computational capabilities and a wider scope of observation, to plan how. VanGo provides a cross-platform library for data transformation, measurement, and classification; a fast and low-jitter data acquisition system for motes; and a mechanism to control mote and microserver signal processing.

• Ben Greenstein (bengreenstein at gmail dot com)

• Christopher Mar (cemar at lecs dot cs dot ucla dot edu)

• Alex Pesterev (alex dot p at ucla dot edu)

• Coming soon

Laboratory for Embedded Collaborative Systems - LECS