The paper presents a six‑month dataset of indoor air‑quality measurements from low‑cost and reference sensors, collected at five locations with contextual metadata. It defines four evaluation scenarios—reference‑efficient, location‑transfer, long‑term drift, and event‑conditioned—to test spatial generalization and robustness to distribution shifts. A lightweight temporal model that compresses input windows and fuses residual temporal and feature information achieves strong calibration performance across all scenarios while keeping edge‑inference cost low.
arXiv:2607. 23503v1 Announce Type: cross Abstract: Internet of Things (IoT) applications generate vast amounts of Correlated Time Series (CTS) data that often contain missing values and require imputation.
arXiv:2607. 19381v1 Announce Type: new Abstract: Air pollution causes an estimated 7.
arXiv:2607. 25687v1 Announce Type: cross Abstract: Full-field reconstruction of air pollution is essential for evaluating pollution exposure and supporting public health decision-making.
arXiv:2606. 11490v1 Announce Type: new Abstract: Indoor localization from wireless measurements remains challenging in large-scale deployments due to substantial variation in building geometry, the set of detectable access points (APs), and the heterogeneity of received signals.
arXiv:2608. 09775v1 Announce Type: new Abstract: Accurate air quality forecasting is essential for public health and urban environmental management, but remains challenging because pollutant channels differ in periodicity and distribution drift, while their concentration trajectories contain both multi-scale dependencies and rapid changes.
arXiv:2609.06131v1 Announce Type: new Abstract: Environmental identification in wireless sensing is essential for 6G integrated sensing and communication (ISAC) systems to achieve reliable situationa...
arXiv:2605. 18793v2 Announce Type: replace-cross Abstract: Accurate spatiotemporal pattern analysis is critical in fields such as urban traffic, meteorology, and public health monitoring.
arXiv:2610.19015v1 Announce Type: new Abstract: Real measurements are too limited to train an accurate indoor pathloss predictor that works across a wide range of buildings. Large-scale synthetic pre...
The paper introduces a transfer learning framework for VLC-based indoor localization that improves cross-area robustness and reduces data needs. Using real measurements from a Bosch facility, fine‑tuning a deep neural network with only 40% of target data recovers 97% of full‑data performance. The authors also propose a Transfer Efficiency Score (TES) to select the best source model, achieving localization errors as low as 40.9 cm and a 3.28 cm mean error on an unseen area.
arXiv:2606. 00260v1 Announce Type: cross Abstract: Human Activity Recognition (HAR) from ambient sensors enables smart-home applications such as health monitoring and assisted living.
arXiv:2607. 05449v1 Announce Type: cross Abstract: Accurate work-zone geometry perception is critical for intelligent transportation systems, and ultra-wideband sensing offers a low-cost approach for infrastructure-aided reconstruction.
arXiv:2606. 07648v1 Announce Type: cross Abstract: Air pollution represents one of the most critical environmental and public health challenges globally, with traditional sensor-based monitoring systems facing significant scalability and economic constraints.