The rapid growth of the Industrial Internet of Healthcare Things (IIoHT) has improved healthcare with an intelligent, connected medical system. This progress also brings challenges in managing tasks securely and efficiently, especially in dynamic and limited resource settings. This study introduces a secure and fault-tolerant task scheduling model for IIoHT. Dynamic Stepwise Tiny Encryption (DSTE) is used for secure task encryption and transmission. A Quantum Portfolio Optimization (QPO) algorithm further enhances task-resource mapping efficiency using quantum annealing. End-to-end data integrity is maintained through secure transmission and decryption processes. Predictive analytics enable early warnings and medical interventions. Continuous monitoring and adaptive learning ensure improved system performance and model accuracy over time. The model focuses on real-time task processing, strong fault recovery, and built-in security. Using Python 3.12.7 and Pytorch, ECG signals detect critical or normal patient conditions with Message Queuing Telemetry Transport (MQTT) for fast data transfer and encryption for safety. Tests show high performance, with 99.90% accuracy, 99.80% F1-score. The proposed DSTE algorithm also proves efficient, taking just 0.00003528 seconds for encryption. Overall, the model ensures reliable, secure, and intelligent task scheduling for IIoHT healthcare systems.
Koteswaramma et al. (Fri,) studied this question.
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