Information, Energy and Density for Ad Hoc Sensor Networks over Correlated Random Fields: Large Deviations Analysis

TL;DR

This paper analyzes the asymptotic behavior of information and energy efficiency in large-scale 2-D sensor networks over correlated random fields using large deviations theory, providing insights into their deployment and performance.

Contribution

It introduces a large deviations framework to characterize information and energy efficiency in 2-D sensor networks over correlated fields, a novel approach in this context.

Findings

Total information scales with network size and correlation structure.

Energy efficiency varies with node density and coverage area.

Asymptotic formulas for information and energy metrics are derived.

Abstract

Using large deviations results that characterize the amount of information per node on a two-dimensional (2-D) lattice, asymptotic behavior of a sensor network deployed over a correlated random field for statistical inference is investigated. Under a 2-D hidden Gauss-Markov random field model with symmetric first order conditional autoregression, the behavior of the total information [nats] and energy efficiency [nats/J] defined as the ratio of total gathered information to the required energy is obtained as the coverage area, node density and energy vary.