G. R. Bhagwatrao - School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India
R. Lakshmanan - School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India

This paper introduces a novel approach that adeptly navigates this trade-off, significantly enhancing the deployment efficiency of remote healthcare systems. The existing methodologies in remote healthcare networks typically face challenges in balancing robust security measures with the need for high-speed data transmission. This model meticulously selects from a pool of encryption methods — AES, RSA, ECC, DSA, Blowfish, TwoFish — and hashing methods — Argon۲, SHA۱, SHA۲۵۶, SHA۵۱۲, MD۵, Bcrypt — to tailor a solution that upholds high security while enhancing speed. The rationale behind employing GCN lies in its ability to efficiently handle the complex, non-linear relationships among different encryption and hashing techniques, while Deep Dyna Q Learning dynamically adjusts hyperparameters to optimize for speed without compromising security.The results were compelling, showcasing an ۸.۵% improvement in energy efficiency, a ۴.۹% increase in speed, an ۸.۳% rise in throughput, a ۵.۹% enhancement in packet delivery ratio, and a ۳.۹% boost in communication consistency compared to existing methods. Notably, this enhanced performance was maintained even under various security threats, including Sybil, masquerading, spoofing, and spying attacks.

Remote Healthcare Systems, Graph Convolutional Networks, Deep Dyna Q Learning, Data Encryption Optimization, Network Security Enhancement