用信息密度评估传感器配置，实现低误差虚拟传感。
Information Density as a Quantitative Measure for AI-enabled Virtual Sensing: Feasibility and Limits
- 基于特征空间相位与互信息量化信息密度。
- 单个传感器可实现小于3.21%的平均误差。
- 适合智能城市中节能高效的传感系统设计。
现代物联网和传感器网络产生海量数据，对存储、传输和实时处理带来巨大挑战。传统方法如压缩感知和基于机器学习的压缩常存在计算效率低和不可逆数据损失问题。本文提出以信息密度为量化指标，支持传感器部署并实现人工智能驱动的虚拟传感。通过利用传感器信号在空间、时间及跨模态间的相关性，即使无物理传感器也可完成感知任务。我们构建了两个互补度量：(i) 特征空间相位（Phase in Eigen Space）与 (ii) 互信息（Mutual Information），用于量化信息密度，从而在同模态与跨模态场景下选择最优传感器配置。基于马德里智慧城市基础设施的真实数据验证表明，在误差受限条件下，可用虚拟传感器替代物理传感器，例如仅用一个传感器即可实现<3.21%的平均误差。结果展示了在智能环境中实现可扩展、低能耗传感系统的潜力。
原文摘要 · Abstract (English)
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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