Evaluating local contributions to global performance in wireless sensor and actuator networks (bibtex)
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Abstract:
Wireless sensor networks are often studied with the goal of removing information from the network as efficiently as possible. However, when the application also includes an actuator network, it is advantageous to determine actions in-network. In such settings, optimizing the sensor node behavior with respect to sensor information fidelity does not necessarily translate into optimum behavior in terms of action fidelity. Inspired by neural systems, we present a model of a sensor and actuator network based on the vector space tools of frame theory that applies to applications analogous to reflex behaviors in biological systems. Our analysis yields bounds on both absolute and average actuation error that point directly to strategies for limiting sensor communication based not only on local measurements but also on a measure of how important each sensor-actuator link is to the fidelity of the total actuation output.
Reference:
Evaluating local contributions to global performance in wireless sensor and actuator networksC.J. Rozell and D.H. Johnson. Lecture Notes in Computer Science, vol. 4026, pp. 1–16, June 2006. it Proceedings of the International Conference on Distributed Computing in Sensor Systems (DCOSS), San Francisco, CA, June 2006
Bibtex Entry:
@Article{rozell.06,
  author = 	 {Rozell, C.J. and Johnson, D.H.},
  title = 	 {Evaluating local contributions to global performance in wireless sensor and actuator networks},
  journal = 	 {Lecture Notes in Computer Science},
  year = 	 2006,
  volume =	 4026,
  month = {June},
  pages =	 {1--16},
  note =	 {{\it Proceedings of the International Conference on Distributed Computing in Sensor Systems (DCOSS)}, San Francisco, CA, June 2006},
  abstract = {Wireless sensor networks are often studied with the goal of removing
         information from the network as efficiently as possible.  However,
         when the application also includes an actuator network, it is
         advantageous to determine actions in-network.  In such settings,
         optimizing the sensor node behavior with respect to sensor information
         fidelity does not necessarily translate into optimum behavior in terms
         of action fidelity.  Inspired by neural systems, we present a model of
         a sensor and actuator network based on the vector space tools of
         frame theory that applies to applications analogous to reflex
         behaviors in biological systems.  Our analysis yields bounds on both
         absolute and average actuation error that point directly to strategies
         for limiting sensor communication based not only on local measurements
         but also on a measure of how important each sensor-actuator link is to
         the fidelity of the total actuation output.},
  url = {http://siplab.gatech.edu/pubs/rozellDCOSS2006.pdf}
}
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