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.
| Comments: | IEEE Transactions on Sustainable Computing (2026) |
| Subjects: | Information Theory (cs.IT); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP) |
| Cite as: | arXiv:2605.08180 [cs.IT] |
| (or arXiv:2605.08180v1 [cs.IT] for this version) | |
| https://doi.org/10.48550/arXiv.2605.08180 arXiv-issued DOI via DataCite |
|
| Related DOI: | https://doi.org/10.1109/TSUSC.2026.3688833
DOI(s) linking to related resources |
Submission history
From: Hrishikesh Dutta [view email]
[v1]
Tue, 5 May 2026 09:03:37 UTC (21,226 KB)