Swarm Intelligence based Deep Learning Method for Health Monitoring System using Internet of Things (IoT)
Ravilla Pavithra, Venkatesh Kumar S · 2023
The research conducted is significant because there is an increasing demand for reliable health monitoring, particularly in underserved and far-flung areas. However, issues with data quality, network optimization, and resource limits might hinder the efficacy of data collected by IoT devices, which otherwise provide until then unimaginable prospects. Swarm intelligence and deep learning together can improve these areas, making IoT-based health monitoring more effective. Efficient sensor placement, data transmission, and processing of massive, frequently noisy health data streams are among the biggest obstacles to widespread adoption of IoT-based health monitoring. Using swarm intelligence algorithms to optimize sensor placement and deep learning for data analysis, the proposed Internet of Things-based swarm health monitoring system (IoT-SHMS) approach addresses the aforementioned challenges. IoT-SHMS uses swarm intelligence techniques, such as Particle Swarm Optimization (PSO), to optimize sensor location and network topologies. This helps to increase the reliability of health prediction while decreasing operational expenses. Constant patient monitoring, early detection of health anomalies, and the detection of falls are a few of the numerous applications of an IoT-SHMS approach in the medical field. It additionally has applications in telemedicine, wellness monitoring, and emergency response systems to give doctors and nurses up-to-date information in a timely manner. Extensive simulation evaluations are presented to prove that IoT-SHMS works as intended. The advantage of our technology over conventional approaches is illustrated by the performance criteria of accuracy, latency, and energy efficiency. Based on these findings, IoT-SHMS appears to be a viable option for healthcare applications in the Internet of Things environment