Integrated Healthcare Monitoring System using Wireless Body Area Networks and Internet of Things

Abstract

To minimize overall healthcare costs and enhance workflows and processes, remote health monitoring solutions are needed in both clinics and at home. One of the most effective communication technologies, the Internet of Things (IoT) offers the ability of integrated data access and fusion across a variety of applications. Depending on how each person's role is defined, users and qualified professionals (like doctors and nurses in the medical industry) may be able to access data. The goal of the Internet of Things in the healthcare industry is to redefine the healthcare system by bringing together all involved authorities and cutting-edge technology makes the most of the data shared between intimately linked technologies that use the IoT platform. IoT is generally anticipated to provide an enhanced device, scheme, and application connectivity that extend over machine-to-machine communications. In the past few years, the development of wearable sensors has offered ease, simplicity, and better health. By making medical sensors smaller and less expensive, technological advancements have boosted the use of these devices. The expertise and abilities of healthcare services, such as remote health monitoring, surgery, rehabilitation, and therapy are improved through medical sensors. Fog computing techniques are also added to enhance precision medicine, obtain real-time data processing, and prevent the connection from failing. As a result, the operating environment for devices is more nimble and local. In this regard, this study suggests an architecture model for the healthcare domain that incorporates the technologies of body area networks, IoT, and Fog computing. The key contribution is to boost the capabilities and resources of IoT devices by using an intermediate Fog layer close to the edge to get beyond IoT restrictions. Experiments show that when compared to other standard architecture, the suggested model can reach a 75% faster response time. The evaluation's results supported the suggested model's ability to accomplish its objectives while maintaining application performance.