Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)
Tatsuto Murayama, Peter Davis
This paper provides a system-level analysis of a scalable distributed sens- ing model for networked sensors. In our system model, a data center ac- quires data from 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 dis- tortion 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 prob- lem changes at critical values of the data rate R or the noise level.