{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T21:49:01Z","timestamp":1770068941621,"version":"3.49.0"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Wire rope tension monitoring in mining hoist systems demands real-time, high-accuracy detection to mitigate catastrophic failure risks, yet existing cloud-based solutions remain constrained by 300\u2013800 ms latency and network dependence, and conventional FBG sensing lacks embedded intelligence at the edge. To address these limitations, EdgeRopeNet utilizes a compact GRU-based neural architecture with two dense layers (64 and 32 neurons) deployed on Raspberry Pi 4 edge devices (4 GB RAM), supported by fog-layer aggregation on Intel i7 hardware. Sensor data from FBG arrays undergo Savitzky\u2013Golay filtering and Min\u2013Max normalization prior to inference, enabling 19 ms real-time latency and 97.8% prediction accuracy on synthetic datasets emulating mining shaft dynamics. Performance was rigorously benchmarked against ten baselines: five traditional models (Linear Regression, SVM, Random Forest, k-NN, Na\u00efve Bayes) and five deep learning methods (CNN, LSTM, GRU, CNN\u2013LSTM hybrid, Transformer) sing an 80:20 train\u2013test split across 100 epochs with Adam optimization. EdgeRopeNet delivered 97.8% accuracy, 97.4% precision, 98.1% recall, a 97.7% F1-score, and MAE of 0.012, surpassing CNN\u2013LSTM (95.2% accuracy, MAE 0.029) and Transformer models (96.1% accuracy, MAE 0.023). Parameter-pruning reduced model size by 60% while preserving 97.4% precision and 98.1% recall, with edge inference sustained at 0.019 seconds per prediction. Overall, EdgeRopeNet achieves a 94% reduction in latency relative to cloud-based platforms while maintaining superior accuracy, providing a scalable, autonomous, and edge-resilient solution for safety-critical mining infrastructure. Keywords: Edge computing, wire rope tension monitoring, FBG sensors, lightweight neural networks, mining hoist systems, real-time calibration.<\/jats:p>","DOI":"10.31449\/inf.v50i5.12495","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T10:25:21Z","timestamp":1770027921000},"source":"Crossref","is-referenced-by-count":0,"title":["EdgeRopeNet: Lightweight Neural Network for Real-Time Wire Rope Tension Monitoring Using FBG Sensors in Edge-Fog Mining Systems"],"prefix":"10.31449","volume":"50","author":[{"given":"Ruihua","family":"Tong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peijiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingru","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoyang","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,2,2]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12495\/6423","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12495\/6423","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T10:25:21Z","timestamp":1770027921000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/12495"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,2]]},"references-count":0,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,2,2]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i5.12495","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2,2]]}}}