Real-Time Medical Resource Allocation in 6G-Enabled IoT Environment using Multi-Head Attention-Based Graph Convolutional Network
International Journal of Medical Toxicology and Forensic Medicine,
Vol. 16 (2026),
1 January 2026
,
Page 1-8
https://doi.org/10.22037/ijmtfm.v16.52673
Abstract
Background: The incorporation of sixth-generation (6G) networks with the Internet of Things (IoT) is transforming smart healthcare. The wide range of IoT devices and their offloading demands increase latency and bandwidth requirements, impacting performance.
Methods: This study proposes an Attention-based Graph Convolutional Approach for Efficient Medical Resource Allocation (AGCA-EMRA) methodology. The proposed model uses a conditional mutual information maximization-based feature selection approach to identify the most informative features while removing the redundant ones. For classification, a multi-head spatiotemporal attention graph convolutional network captures both spatial and temporal correlations in the data. The model is further optimized using the Ranger optimizer.
Results: An extensive experimental analysis is conducted on the IoT-Driven MR Allocation for 6G Network Dataset. The proposed AGCA-EMRA technique exhibits superior performance with an accuracy of 95.97%.
Conclusion: The comparative result analysis demonstrates the improvement of the AGDRL-ECFA method and confirms its effectiveness for intelligent cybercrime forensic investigation and decision-support applications.
- Resource utilization efficiency, Medical resource allocation, 6G-Network, Smart healthcare, Internet of things
How to Cite
References
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