IoT-Based Localization in Urban LoRaWAN Networks Using CNN–LSTM: Supporting Dataset and Model
收藏Zenodo2025-11-12 更新2026-05-26 收录
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https://zenodo.org/doi/10.5281/zenodo.17581861
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资源简介：
This dataset and trained model accompany the article “IoT-Based Localization in Urban LoRaWAN Networks Using CNN–LSTM Deep Learning Model” accepted for publication in the Journal of Universal Computer Science (JUCS).
The dataset includes two preprocessed RSSI feature sets (processed_dataset_final and processed_dataset_quantile) derived from the LoRaWAN measurements collected in Antwerp, Belgium, covering 72 static gateways and multiple mobile nodes.
The provided trained model file (cnn_lstm_v4.keras) corresponds to the final version (CNN–LSTM v4) described in the paper, optimized using QuantileTransformer and RobustScaler preprocessing, Mish activation, batch normalization, and AdamW optimizer.
Researchers may use these files to reproduce, validate, or extend the reported results in urban IoT localization tasks.
提供机构：
Zenodo
创建时间：
2025-11-12
