Information Density as a Quantitative Measure for AI-enabled Virtual Sensing: Feasibility and Limits
For IoT and smart city applications, this work provides a quantitative framework to optimize sensor deployment and enable virtual sensing, reducing hardware costs and energy consumption.
The paper introduces Information Density as a metric for AI-driven virtual sensing, demonstrating that physical sensors can be replaced with virtual ones under bounded error conditions (e.g., <3.21% mean error with a single sensor) using real-world smart city data from Madrid.
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.