Secure IoT communications for smart healthcare monitoring system

Healthcare monitoring systems in hospitals and other health centers have witnessed throughout the last decade a tremendous growth and wireless healthcare monitoring devices with various technologies have become of great interest in many nations around the world. The proposed paper aims at integrating artificial intelligence technology, such as the neural networks and fuzzy system in a secure healthcare monitoring system in order to enable the system to work as a smart healthcare model that decides the priority by itself depending on the collected health parameters from the senor nodes. The proposed model consists of a trust environment that is responsible for collecting authenticated physiological data from patient's body which is then sent through GSM module to Azure IoT Hub where raw data is converted into linguistic representation, with the help of logic-based algorithm, which is trained in fuzzy-based inference system (FBIS) to get the status of the patient. The proposed system, then, provides reliable, accurate, secure and real-time patient monitoring. The following sections describe how the FBIS can be integrated with a secure healthcare monitoring system to get the states of the patient and send it to the medical advisory for the preliminary precautions. (C) 2019 Elsevier B.V. All rights reserved.

Automated summary

The study aims to integrate artificial intelligence technology, such as neural networks and fuzzy systems, into a secure healthcare monitoring system to create a smart healthcare model that provides reliable, accurate, secure, and real-time patient monitoring.