{
  "abstract": "Long-term environmental IoT/LPWAN deployments often aggregate heterogeneous data streams from diverse devices without systematic auditing of each variable's actual coverage prior to analysis-an increased risk when multiple sensor types coexist within the same network. This work proposes a C++17 processing pipeline applied to the Tour Perret LoRaWAN dataset (421 937 frames, June 2021--June 2023, 5 devices). A systematic coverage audit integrated into the extraction phase reveals that CO2 is completely missing (0% coverage) and pressure is reported only by an isolated base module (Wyres, 4.24% of the corpus), invalidating the initial hypothesis of a joint four-variable analysis. The analysis is refocused on the Temperature--Humidity pair, jointly available across 205{,}005 frames (95.76%) from 4 Elsys EMS sensors. The pipeline sequentially performs anomaly detection (modified Z-score, Isolation Forest), bivariate Principal Component Analysis, clustering (K-means, DBSCAN), and a multilayer perceptron predicting humidity from temperature and time, whose residuals serve as a detector for slow drift. On real-world data: Temperature--Humidity correlation r=-0.662, two-component PCA (PC1 =83.1% of variance), Isolation Forest detects 2.12% multivariate anomalies while univariate Z-score detects none, calibrated DBSCAN isolates 0.17% noise across 13 clusters, and the MLP achieves $R^2=0.672$ (RMSE =10.54%) in cross-validation. This work demonstrates that per-variable coverage auditing prior to modeling is a necessary methodological step for heterogeneous multi-sensor IoT datasets.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.32438",
  "author_total": 1,
  "authors": [
    {
      "name": "Mamadou Sow"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4mxv5msk1kjek.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.32438",
  "fulltext_url": null,
  "id": "4mxv5msk1kjek",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.32438",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.DC"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.ao-ph"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "2208|IoT Networks and Protocols"
    ]
  ],
  "openalex": "W7223681537",
  "pdf_url": "https://arxiv.org/pdf/2610.32438",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4mxv5msk1kjek.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:15Z",
      "native_id": "2610.32438",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.32438"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Data Quality Audit and Bivariate Temperature-Humidity Analysis of a Long-Term LoRaWAN Network: Anomaly Detection, PCA, Clustering and Machine Learning",
  "title_text": "Data Quality Audit and Bivariate Temperature-Humidity Analysis of a Long-Term LoRaWAN Network: Anomaly Detection, PCA, Clustering and Machine Learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:25Z",
  "url": "https://research.draconitic.com/paper/4mxv5msk1kjek",
  "venue": "arXiv",
  "versions": []
}