Rate Distortion Codes in Sensor Networks: A System-level Analysis
Murayama, Tatsuto, Davis, Peter
–Neural Information Processing Systems
This paper provides a system-level analysis of a scalable distributed sensing modelfor networked sensors. In our system model, a data center acquires datafrom a bunch of L sensors which each independently encode their noisy observations of an original binary sequence, and transmit their encoded data sequences to the data center at a combined rate R, which is limited. Supposing that the sensors use independent LDGM rate distortion codes,we show that the system performance can be evaluated for any given finite R when the number of sensors L goes to infinity. The analysis shows how the optimal strategy for the distributed sensing problem changesat critical values of the data rate R or the noise level.
Neural Information Processing Systems
Dec-31-2006
- Country:
- Asia > Japan (0.14)
- Europe > United Kingdom
- England (0.14)
- Industry:
- Information Technology > Services (0.72)
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