Sensing Capacity for Markov Random Fields

TL;DR

This paper analyzes the sensing capacity of sensor networks monitoring 2D Markov random fields, providing a lower bound on their ability to distinguish environmental states with Markov dependencies.

Contribution

It introduces a novel information-theoretic framework modeling sensing as encoding, deriving a lower bound on sensing capacity for dependent sensor observations.

Findings

Lower bound on sensing capacity established

Sensor network can distinguish environments with Markov structure

Dependence across sensors affects sensing performance

Abstract

This paper computes the sensing capacity of a sensor network, with sensors of limited range, sensing a two-dimensional Markov random field, by modeling the sensing operation as an encoder. Sensor observations are dependent across sensors, and the sensor network output across different states of the environment is neither identically nor independently distributed. Using a random coding argument, based on the theory of types, we prove a lower bound on the sensing capacity of the network, which characterizes the ability of the sensor network to distinguish among environments with Markov structure, to within a desired accuracy.