Large-scale Sensor Networks as Collective and Frustrated Systems
Tatsuto Murayama, Peter T. Davis · 2007
This article presents a large-scale analysis of a distributed sensing model for systemized and networked sensors. In the system model, a data center acquires binary information from a bunch of L sensors which each independently encode their noisy observations of an original bit sequence, and transmit their encoded sequences to the data center at a combined data rate R, which is strictly limited. Supposing that the sensors use independent quantization techniques, we show that the 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 changes at critical values of the data rate R or the noise level p