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

• URL: http://arxiv.org/abs/2605.08180v1
• Date: Tue, 05 May 2026 09:03:37 GMT
• Title: Information Density as a Quantitative Measure for AI-enabled Virtual Sensing: Feasibility and Limits
• Abstract summary: This paper introduces Information Density as a quantitative metric to support sensor deployment and enable AI-driven virtual sensing.<n>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.
• Score: 3.2957112871590777
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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