{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T13:09:13Z","timestamp":1778677753227,"version":"3.51.4"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Intelligent warehousing environments require precise, energy-efficient control of temperature, humidity, and other environmental parameters. To address this, we propose STC-RL, a novel deep reinforcement learning framework that integrates Transformer-based temporal modeling, 3D CNN-based spatial field reconstruction, and graph neural network (GNN)-enhanced anomaly detection within a continuous- action reinforcement learning policy. Specifically, the Transformer captures long-range temporal dependencies from multi-sensor time series, while the 3D CNN generates a spatial thermal-humidity field via bilinear interpolation of sensor coordinates. The GNN encodes physical sensor topology to detect equipment failures through residual-based anomaly scoring. The RL agent operates in a continuous action space (e.g., setpoint temperature, humidifier output) and optimizes a multi-objective reward balancing environmental deviation, energy consumption, switching frequency, and anomaly alerts. Experimental results on a real 2,100 m\u00b2 cold-chain warehouse show that STC-RL reduces energy consumption by 13.1%, achieves an average AUC-ROC of 0.936 for anomaly detection, and lowers temperature\/humidity prediction RMSE to 1.02\u00b0C \/ 4.15%, outperforming six baselines. The system also cuts food spoilage by 66.7% and improves temperature compliance to 97.6%.<\/jats:p>","DOI":"10.31449\/inf.v50i12.10942","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T12:09:58Z","timestamp":1778674198000},"source":"Crossref","is-referenced-by-count":0,"title":["STC-RL: A Spatiotemporal Control Framework for Intelligent Environmental Regulation in Automated Warehousing Using IoT and Deep Learning"],"prefix":"10.31449","volume":"50","author":[{"given":"Xiaosa","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,5,13]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10942\/6673","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10942\/6673","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T12:09:58Z","timestamp":1778674198000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/10942"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,13]]},"references-count":0,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,5,13]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i12.10942","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5,13]]}}}