Information Density as a Quantitative Measure for AI-enabled Virtual Sensing: Feasibility and Limits

TL;DR

This paper introduces Information Density as a metric for virtual sensing, enabling sensor deployment optimization and demonstrating the feasibility of replacing physical sensors with virtual ones in smart city environments.

Contribution

It proposes a novel framework using information density measures to support sensor placement and virtual sensing, validated with real-world smart city data.

Findings

Achieved less than 3.21% mean error with a single virtual sensor.

Validated the framework's effectiveness in real-world smart city data.

Demonstrated scalable, energy-efficient sensing system potential.

Abstract

Modern IoT and sensor networks generate vast amounts of data, posing significant challenges for storage, transmission, and real-time processing. Traditional approaches, such as compressive sensing and machine learning-based compression, often suffer from computational inefficiencies and irreversible data loss. This paper introduces Information Density as a quantitative metric to support sensor deployment and enable AI-driven virtual sensing. We propose a framework that leverages spatial, temporal and inter-modal correlations among sensor signals to perform sensing tasks even in the absence of physical sensors. Two complementary measures: (i) Phase in Eigen Space and (ii) Mutual Information, are developed to quantify and assess information density, enabling the selection of optimal sensor configurations across both intra-modality and cross-modality scenarios. Validated using real-world data from Madrid's smart city infrastructure, this framework demonstrates the feasibility of replacing physical sensors with virtual ones under bounded error conditions (e.g., achieving $<3.21\%$ mean error with a single sensor). The results highlight the potential for scalable and energy-efficient sensing systems in smart environments.