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.
@article{arxiv.2605.08180,
title = {Information Density as a Quantitative Measure for AI-enabled Virtual Sensing: Feasibility and Limits},
author = {Hrishikesh Dutta and Roberto Minerva and Reza Farahbakhsh and Noel Crespi},
journal= {arXiv preprint arXiv:2605.08180},
year = {2026}
}