Enhanced Mayfly Optimization with Active Elite Approach Based Cluster Head Selection for Energy Efficient IoT based Healthcare Monitoring System

Abstract

In order to conserve energy in the Internet of Things (IoT) network and also effectively handle the integrity and security issues in medical information, a framework for transmitting data that is both secure and energy efficient was proposed. It was used Enhanced Mayfly Clustering-based Q Learner Routing (EMCQLR) and Exponential Key-based Elliptical Curve Cryptography (EKECC) techniques. In EMCQLR, Enhanced Mayfly optimization Algorithm (EMOA) was used to select the Cluster Head (CH) for data collection from the nodes and form clusters of IoT medical sensors. This paper proposes a new approach called EMOA with Active Elite Approach (EMOA-AEA) to deal with the issues of slow convergence speed and the tendency of EMOA to fall into local optimum. The EMOA-AEA algorithm establishes a definite area around the most optimal mayfly in the present population, which is used to identify the top-performing CH. This region’s search radius is then adjusted as needed. Elite mayflies are subsequently produced within this designated zone, and if their fitness level surpasses that of the most exceptional mayfly, the finest cluster head from these new elite mayflies is selected to replace the current population’s top mayfly. After the selection of cluster head, Path-Weighted Q Reinforcement Learning (PWQRL) is used for data routing. At last, EKECC algorithm encrypts the medical records to provide data security. The experimental outcomes prove that the EMOA-AEA method surpasses the existing method in terms of network lifetime, average energy consumption, and throughput.