Improving Security and Energy Optimization Using a Hybrid Secure Deep Learning Approach for IoMT Systems
Year: 2026
Type: article
Abstract: ABSTRACT In healthcare 5.0, the Internet of Medical Things (IoMT) is driving rapid developments that require energy‐efficient and secure solutions. This paper proposes a novel hybrid approach that integrates Public Key Infrastructure (PKI) with Spiking Neural Networks (SNNs) to enhance secure communication and reduce energy consumption in IoMT‐based healthcare systems. The PKI framework provides secure device authentication and encrypted data transmission, while the biologically inspired SNNs enable low‐power, real‐time anomaly detection. Furthermore, the Zebra Optimization Algorithm (ZOA) further enhances system performance. A simulation study shows that the proposed model outperforms existing protocols from a delay perspective, throughput perspective, packet delivery ratio perspective, and residual energy perspective, showing that it is suitable for real‐time, resource‐constrained healthcare.
Source: Internet Technology Letters
DOI: 10.1002/itl2.70231
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7127280154