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A Hybrid PKI and Spiking Neural Network Approach for Enhancing Security and Energy Efficiency in IoMT-Based Healthcare 5.0.

D. Chaudhari,M. Bhende,Aadam Quraishi,Azzah Alghamdi,Ismail Keshta,Mukesh Soni,Brajesh Kumar Singh,Haewon Byeon,Mohammad Shabaz

Published 2025 in SLAS technology

ABSTRACT

In the rapidly evolving field of healthcare 5.0, the Internet of Medical Things (IoMT) is expected to be an enabler that allows smart medical devices to collaborate and communicate with healthcare networks to speed up procedures, enhance care, and improve disease management. However, one of the critical issues for these networks still remains the secure and energy-efficient transmission of sensitive patient data. Thus, a novel security framework is proposed in this work, in which a Public Key Infrastructure- Energy-Efficient Routing Protocol (PKI-EERP) with a Zebra Optimization Algorithm (ZOA) is incorporated in spiking neural networks. The method combines data security robustness of the spiking neural networks to detect anomalies and check for access control purposes, with the PKI encryption to provide safe encryption and key management. The ZOA optimizes energy consumption in WSNs, and as a result transmission energy is significantly reduced up to 35% compared to other implementations, and the network lifetime is increased by about 30% through effective load balancing. It enhances both the privacy and energy efficiency that are essential for the safe and reliable operation of IoMT systems in contemporary healthcare environments, thus improving patient outcomes as well as standards of operations.

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