Information Density as a Quantitative Measure for AI-enabled Virtual Sensing: Feasibility and Limits
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 Informatio...
| Authors | Hrishikesh Dutta, Roberto Minerva, Reza Farahbakhsh et al. |
|---|---|
| Primary category | cs.IT |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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