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  <title>CrowdLOC-S: Crowdsourced Seamless Localization Framework Based on CNN-LSTM-MLP Enhanced Quality Indicator (2024)</title>
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  <h1>CrowdLOC-S: Crowdsourced Seamless Localization Framework Based on CNN-LSTM-MLP Enhanced Quality Indicator</h1>
  <p><strong>Year:</strong> 2024</p>
  <p><strong>Venue:</strong> Expert Systems with Applications, Volume 243, Article 122852</p>
  <p><strong>DOI:</strong> <a href="https://doi.org/10.1016/j.eswa.2023.122852">10.1016/j.eswa.2023.122852</a></p>

  <h2>Local Availability</h2>
  <p>No open-access full-text location was exposed through OpenAlex at retrieval time. Attempts to fetch the ScienceDirect PDF endpoint from the current environment were blocked on 2026-04-16, so this local page records the paper's metadata and summary.</p>

  <h2>Summary</h2>
  <p>This paper presents CrowdLOC-S, a seamless indoor/outdoor localization framework that combines crowdsourced Wi-Fi fingerprinting, GNSS, and low-cost MEMS sensors. Its design includes a data-and-model dual-driven trajectory estimator, a hybrid 1D-CNN, Bi-LSTM, and MLP quality indicator for crowdsourced trajectory evaluation and Wi-Fi database construction, transfer learning for scene switching and error prediction, and EKF-based multi-source fusion for unified meter-level positioning across complex urban environments.</p>

  <h2>Source Links</h2>
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    <li><a href="https://doi.org/10.1016/j.eswa.2023.122852">DOI landing page</a></li>
    <li><a href="https://www.sciencedirect.com/science/article/abs/pii/S0957417423033547">ScienceDirect abstract page</a></li>
    <li><a href="https://research.polyu.edu.hk/en/publications/crowdloc-s-crowdsourced-seamless-localization-framework-based-on-/">PolyU Scholars Hub entry</a></li>
    <li><a href="https://www.sciencedirect.com/science/article/pii/S0957417423033547/pdfft?isDTMRedir=true&amp;download=true">ScienceDirect PDF endpoint</a></li>
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