Clustering Technique Based on ARSH-FATI for Data Transmission in WSN-IoT Healthcare System

This research study investigates the use of the Internet of Things in the health sector in terms of minimizing hospital visit costs, optimizing human resources, and enhancing the quality of healthcare. It tackles a crucial challenge in IoT-based e-health applications: the energy efficiency of battery-powered sensors. As the number of IoT sensors being used for the transmission of medical data is growing, concerns have been raised regarding the sustainability of sensor networks, which is highly dependent on battery life. A new technique, namely ARSH-FATI-based group selection, is proposed to extend the network lifespan. The ARSH-FATI algorithm combines global and local search strategies to maximize network longevity and balance performance parameters. In the CH selection process, the algorithm considers workload, residual energy, and communication distance parameters. Simulations with Python software show that the proposed ARSH-F ATI algorithm obtained a Packet Delivery Ratio (PDR) of 86%, which is almost a little lower value than that of the existing Particle Swarm Optimization (PSO) algorithm, which showed a PDR of 88% for a network of 400 sensor nodes. The outcomes of the proposed algorithm are promising and may provide a viable solution to improve the energy efficiency and lifespan of IoT networks in healthcare applications.