42 research outputs found
Minimum Energy Information Fusion in Sensor Networks
In this paper we consider how to organize the sharing of information in a
distributed network of sensors and data processors so as to provide
explanations for sensor readings with minimal expenditure of energy. We point
out that the Minimum Description Length principle provides an approach to
information fusion that is more naturally suited to energy minimization than
traditional Bayesian approaches. In addition we show that for networks
consisting of a large number of identical sensors Kohonen self-organization
provides an exact solution to the problem of combining the sensor outputs into
minimal description length explanations.Comment: postscript, 8 pages. Paper 65 in Proceedings of The 2nd International
Conference on Information Fusio
Sentient Networks
In this paper we consider the question whether a distributed network of
sensors and data processors can form "perceptions" based on the sensory data.
Because sensory data can have exponentially many explanations, the use of a
central data processor to analyze the outputs from a large ensemble of sensors
will in general introduce unacceptable latencies for responding to dangerous
situations. A better idea is to use a distributed "Helmholtz machine"
architecture in which the collective state of the network as a whole provides
an explanation for the sensory data.Comment: PostScript, 14 page