Capturing high-frequency phenomena using a bandwidth-limited sensor network

  • Authors:

  • Ben Greenstein;Christopher Mar;Alex Pesterev;Shahin Farshchi;Eddie Kohler;Jack Judy;Deborah Estrin

  • Affiliations:

  • University of California, Los Angeles;University of California, Los Angeles;University of California, Los Angeles;University of California, Los Angeles;University of California, Los Angeles;University of California, Los Angeles;University of California, Los Angeles

  • Venue:
  • Proceedings of the 4th international conference on Embedded networked sensor systems
  • Year:
  • 2006

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Abstract

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 we show that 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 samplin.VANGO, our software system for high-frequency data collection, 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. With VANGO we have developed new applications: the first acoustic collection system for motes responsive to changing environmental conditions and user interests, and the first neural spike acquisition application capable of supporting a network of nodes.