Internet of Health Things: A Fog computing Paradigm

Technologies and healthcare industries are intensely interconnected with each other. Still, the service providers are stressed to find an active way to collect, investigate, portray and utilize the data that can make a measurable difference in inpatient care. Patient-Generated Health Data is being produced every day, carrying major problems for loaded analytics infrastructure that does not have the bandwidth to grip the flow of big data in their way. Healthcare IoT requests a faster approach in the case of patient management. In real-time analytics, data is moved wholesale to the cloud, and then pertinent elements are identified and retrieved to an analytical machine before being pushed into the user interface. The process could take only a few minutes, but some patient care decisions cannot delay that much time; the solution may not be in the cloud but the fog. Fog computing is the fastest processing, and it could be a significant point in making the healthcare IoT beneficial for inpatient care, management, and monitoring. Real-time analytics needs a low latency system for the betterment of healthcare. A fusion method consisting of fuzzy logic and reinforcement learning is presented in this paper to develop an analytical model for healthcare IoT using a 3-layer architecture. This method can reduce the network latency, and simulating the proposed model is done with iFogSim.