Communication of uncoded sensor measurements through nanoscale binary-node stochastic pooling networks

Abstract

In recent work we defined a concept referred to as stochastic pooling networks, to describe a class of network structures in which various unexpected emergent features have been observed. Examples of stochastic pooling networks can be found in a diverse range of scientific and engineering contexts, as well as across a vast range of scales, ranging from macroscopic social networks to nanoscale electronics. Here we discuss the relevance of the stochastic pooling network concept to the design of communication and sensing networks at the nanoscale. The information theoretic limits to the performance of such networks when employed to communicate sensor measurements are analysed, and shown to compare favorably with the best possible choice of communication scheme. Optimization of the network in the presence of noise finds that a partially homogeneous network improves performance, and thus suggests an approach to simplifying the design of nanoscale analog-to-digital converters and sensor networks.