Information Density as a Quantitative Measure for AI-enabled Virtual Sensing: Feasibility and Limits
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913104114221056 |
|---|---|
| author | Dutta, Hrishikesh Minerva, Roberto Farahbakhsh, Reza Crespi, Noel |
| author_facet | Dutta, Hrishikesh Minerva, Roberto Farahbakhsh, Reza Crespi, Noel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08180 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Information Density as a Quantitative Measure for AI-enabled Virtual Sensing: Feasibility and Limits Dutta, Hrishikesh Minerva, Roberto Farahbakhsh, Reza Crespi, Noel Information Theory Artificial Intelligence Information Retrieval Machine Learning Networking and Internet Architecture Signal Processing 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. |
| title | Information Density as a Quantitative Measure for AI-enabled Virtual Sensing: Feasibility and Limits |
| topic | Information Theory Artificial Intelligence Information Retrieval Machine Learning Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2605.08180 |