Energy-efficient clustering and routing for IoT-enabled healthcare using adaptive fuzzy logic and hybrid optimization.

Leveraging Internet of Things technology in healthcare, including wireless sensor networks and next-generation networks, enhances the seamless integration of medical equipment and enables intelligent interaction among devices. This advancement plays a crucial role in assisting healthcare professionals in improving patient outcomes. However, ensuring efficient data transmission and communication in Internet of Things-based healthcare systems is essential to meet the critical requirements of real-time monitoring and emergency response. This paper proposes an adaptive model to optimize cluster head selection and routing in internet of things-enabled healthcare applications. The cluster head selection process employs an adaptive fuzzy logic mechanism that incorporates factors such as energy levels, SN density, mobility, and link stability to handle uncertainties in SN characteristics and dynamically adapt to changing network conditions in healthcare environments. Furthermore, a hybrid optimization method is introduced that combines particle swarm optimization and a genetic algorithm to discover optimal routing paths, leveraging Particle Swarm Optimization fast convergence and Genetic Algorithm global search capability to minimize energy consumption and delay. Extensive simulations have been conducted in MATLAB and Google Collaboration to evaluate the proposed model in terms of packet delivery ratio, average delay, throughput, and energy efficiency. The results demonstrate significant improvements over the existing methods. Specifically, the proposed model achieves a PDR of 92.5%, an average minimum delay of 0.10 s, a throughput of 61.5 bps and an energy efficiency of 9.1 J/bit. These findings highlight the effectiveness of the proposed model in optimizing communication reliability, reducing energy consumption, and improving overall network performance in IoT-based healthcare applications.