Secure and energy-efficient data transmission framework for IoT-based healthcare applications using EMCQLR and EKECC

• Published in: Cluster computing Vol. 27; no. 3; pp. 2999 - 3016
• Main Authors: Balakrishnan, D., Rajkumar, T. Dhiliphan, Dhanasekaran, S., Murugan, B. S.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2024
• Subjects: Computer Communication Networks; Computer Science; Operating Systems; Processor Architectures; Internet of things; Healthcare; Routing; Cluster head selection; Medical data security; Elliptical curve cryptography
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-023-04130-7

Healthcare (HC) is among the most promising sectors for assessing Internet of things (IoT) based technological advances, as patients can use wearable or injected medical sensors to monitor medical parameters any place and at any time. The data gathered by IoT devices can be sent to medical professionals, and doctors have real-time access to their patient's data. Nevertheless, the IoT devices within the network have limited resources and minimal computing capability, causing energy conservation issues. Even though clustering saves energy in network nodes, existing clustering methods could be more effective because of the higher Energy Consumption (EC), poorly balanced network load, and enhanced end-to-end delays. Furthermore, the integrity and security of medical information have become significant issues for HC applications. This paper proposes a secure and energy-efficient data transmission (DT) framework for IoT-based HC (IoT-HC) systems using enhanced mayfly clustering-based Q learner routing (EMCQLR) and exponential key-based elliptical curve cryptography (EKECC) techniques. The proposed work consists of the following steps. Double hash biometric-based authentication (DHABA) is initially used for IoT user authorization that prevents the HC network from unauthorized data access. The cluster head (CH) is then selected using the enhanced mayfly optimization algorithm (EMOA) to build clusters of IoT medical sensors and collect data from the nodes. Data is routed to the sink node by checking for data duplication. The path-weighted Q reinforcement learning (PWQRL) model can perform data routing. Finally, the EKECC algorithm encrypts the data packets received from the body sensors, providing security to the patient’s data when DT is performed over an unsecured wireless channel. The performance of the suggested system is evaluated using the energy, loss ratio, throughput, success rate, and delay. The simulation outcomes show that the proposed method outperforms existing methods. Furthermore, false data elimination check and reliability metrics show that our proposed scheme ensures information privacy, message authenticity, and integrity while requiring minimal communication overhead and computation.