Role of Internet of things in diabetes healthcare: Network infrastructure, taxonomy, challenges, and security model

• Published in: Digital health Vol. 9; p. 20552076231179056
• Main Authors: Farooq, Muhammad Shoaib, Riaz, Shamyla, Tehseen, Rabia, Farooq, Uzma, Saleem, Khalid
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2023; Sage Publications Ltd; SAGE Publishing
• Subjects: Diabetes; Disease; Glucose; Infrastructure; Internet of Things; Original Research; Taxonomy; security attacks; Internet of things; framework; challenges; diabetes monitoring; sensor networks; healthcare; architecture
• ISSN: 2055-2076; 2055-2076
• DOI: 10.1177/20552076231179056

The Internet of things (IoT) is an emerging technology that enables ubiquitous devices to connect with the Internet. IoT technology has revolutionized the medical and healthcare industry by interconnecting smart devices and sensors. IoT-based devices and biosensors are ideal to detect diabetes disease by collecting the accurate value of glucose continuously. Diabetes is one of the well-known and major chronic diseases that has a worldwide social impact on community life. Blood glucose monitoring is a challenging task, and there is a need to propose a proper architecture of the noninvasive glucose sensing and monitoring mechanism, which could make diabetic people aware of self-management techniques. This survey presents a rigorous discussion of diabetes types and presents detection techniques based on IoT technology. In this research, an IoT-based healthcare network infrastructure has been proposed for monitoring diabetes disease based on big data analytics, cloud computing, and machine learning. The proposed infrastructure could handle the symptoms of diabetes, collect data, analyze it, and then transmit the results to the server for the next action. Besides, presented an inclusive survey on IoT-based diabetes monitoring applications, services, and proposed solutions. Furthermore, based on IoT technology the diabetes disease management taxonomy has also been presented. Finally, presented the attacks taxonomy as well as discussed challenges, and proposed a lightweight security model in order to secure the patient's health data.