{"id":12960,"date":"2026-10-09T14:34:38","date_gmt":"2026-10-09T06:34:38","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=12960"},"modified":"2026-10-09T22:01:30","modified_gmt":"2026-10-09T14:01:30","slug":"jase-202701-36-020","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202701-36-020","title":{"rendered":"Deep Learning-Based Intelligent Resource Management for Beyond 5G IoT Networks"},"content":{"rendered":"\n
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<\/i> 2027<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 36<\/a><\/p>\n<\/div>\n\n\n\n

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Xudong Zhu and Zibo Li<\/i><\/a><\/p>\n\n\n\n

College of Mechatronics and Information Engineering, Wuxi Vocational Institute of Arts & Technology, Wuxi, China<\/p>\n<\/div>\n\n\n\n

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Received: August 8, 2026
Accepted: September 21, 2026
Publication Date: October 09, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

QoS Satisfaction Trend <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202701_36.020<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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The Internet of Things (IoT) networks in Beyond 5G (B5G) need to be managed intelligently in terms of resources to enable support for large device connection, time-varying traffic patterns, and heterogeneous Quality of Service (QoS) requirements. Optimizing the use of network resources contributes to enhanced throughput, latency, and overall network efficiency. Nevertheless, conventional resource management schemes often exhibit limited generalization to dynamic traffic conditions and overfitting, while separating traffic prediction and resource allocation may lead to inefficient resource utilization and degraded overall performance. This paper introduces a Bidirectional Long Short-Term Memory-Honey Badger Optimization (BiLSTM-HBO) based intelligent resource management for B5G IoT networks to overcome the stated challenges. The BiLSTM predicts temporal resource demand from sequential network conditions, and these predictions facilitate QoS-aware resource allocation in B5G IoT networks through the subsequent HBO optimization. The Network Slicing in 5G dataset experimental results reached the MAE of 0.38707, RMSE of 0.49787, MSE of 0.24787 and an R\u00b2 score of 0.99908. Furthermore, a throughput of 3283.68 Mbps, latency of 38.61 ms, packet loss rate of 4.91%, resource utilisation rate of 50.89% and QoS satisfaction rate of 76.61% were achieved by the proposed method.<\/p>\n\n\n\n

Keywords: Beyond 5G Networks, Internet of Things, Bidirectional Long Short-Term Memory, Honey Badger Optimization, Intelligent Resource Management<\/em><\/p>\n\n\n\n

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