Enhancement of Health Care Services Based on Cloud Computing in IOT Environment Using Hybrid Swarm Intelligence

Healthcare services (HCS) based on cloud computing and the Internet of Things are a great opportunity for the development of medical information technology. Task scheduling in cloud computing is one of the most critical problems facing health care services, as it affects the time required to fulfill user requests and the cost and quality of service delivery. The proposed HCS model structure consists of major components such as user devices, user requests, cloud broker, IoT endpoints, and HCS cloud. This paper proposes a new method to improve task scheduling in healthcare services based on cloud computing in the IoT environment (cloud-IoT). Specifically, A hybrid optimization algorithm HPSOSSA is proposed that combines the best existing swarm intelligence algorithms and integrates the advantages of particle swarm optimization (PSO) and the Salp Swarm Algorithm (SSA). The proposed model was implemented using the Cloudsim simulation package run on Eclipse with specific parameters. The proposed hybrid algorithm was compared to the most popular optimization algorithms that were previously used, such as Ant Colony Optimization (ACO), PSO, SSA, and hybrid PSO-GA. The experimental results showed that HPSOSSA in all cases outperforms the other existing algorithms in terms of makespan, waiting time, and resource utilization.

Automated summary

This study proposes a novel hybrid optimization algorithm HPSOSSA to improve task scheduling in healthcare services based on cloud computing in the IoT environment. The experimental results show that HPSOSSA outperforms existing optimization algorithms such as ACO, PSO, SSA, and hybrid PSO-GA in terms of makespan, waiting time, and resource utilization in all cases.