An Energy-Efficient and Privacy-Preserving Routing Scheme for Sustainable IoT Health Systems

The growing dependence on IoT-based health systems for continuous patient monitoring and real-time diagnostics necessitates routing protocols that ensure both energy efficiency and strong privacy guarantees. In E2PR-HNet—an Energy-Efficient and Privacy-Preserving Routing scheme tailored for sustainable IoT Health Networks. E2PR-HNet integrates reinforcement learning for adaptive route selection, dynamic clustering to reduce redundant transmissions, and lightweight homomorphic encryption to secure sensitive medical data. Unlike existing protocols, it considers patient mobility and real-time context to enhance routing decisions without compromising on computational efficiency. To validate its effectiveness, compare E2PR-HNet against three state-of-the-art algorithms LEACH-C, PEGASIS, and SPEEDY within a simulated healthcare IoT environment featuring wearable and ambient biosensors. Experimental results demonstrate that E2PR-HNet outperforms state-of-the-art protocols by achieving a 30.6% improvement in network lifetime, a 35.6% reduction in average energy consumption, and a 23% enhancement in privacy preservation, as evaluated through a comparative privacy score. These gains are attributed to the use of reinforcement learning for intelligent route selection and lightweight homomorphic encryption for secure data transmission. Furthermore, it demonstrates reduced packet loss and improved latency under dynamic network conditions. These results highlight the superiority of E2PR-HNet in meeting the dual demands of sustainability and data security in next-generation e-health systems. The proposed approach not only contributes to the advancement of secure routing in IoT health applications but also lays the groundwork for future intelligent, privacy-aware health communication infrastructures.