Edge computing in smart health care systems: Review, challenges, and research directions

Abstract

Today, patients are demanding a newer and more sophisticated healthcare system, one that is more personalized and matches the speed of modern life. For the latency and energy efficiency requirements to be met for a real-time collection and analysis of health data, an edge computing environment is the answer, combined with 5G speeds and modern computing techniques. Previous healthcare surveys have focused on new fog architecture and sensor types, which leaves untouched the aspect of optimal computing techniques, such as encryption, authentication, and classification that are used on the devices deployed in an edge computing architecture. This paper aims first to survey the current and emerging edge computing architectures and techniques for healthcare applications, as well as identify requirements and challenges of devices for various use cases. Edge computing applications primarily focus on the classification of health data involving vital sign monitoring and fall detection. Other low latency applications perform specific symptom monitoring for diseases, such as gait abnormalities in Parkinson's disease patients. We also present our exhaustive review on edge computing data operations that include transmission, encryption, authentication, classification, reduction and prediction. Even with these advantages, edge computing has some associated challenges, including requirements for sophisticated privacy and data reduction methods to allow comparable performance to their Cloud based counterparts, but with lower computational complexity. Future research directions in edge computing for healthcare have been identified to offer a higher quality of life for users if addressed.

Edge Intelligence and Internet of Things in Healthcare: A Survey

“…This study focuses on evolving and revolutionary techniques for IoT healthcare systems. Table 2 presents a comparison between the content of previous surveys on IoT healthcare systems [27][28][29][30][31][32][33][34][35][36][37], [101] and that of the present study.…”

“…In the smart healthcare domain, mobile cloud architectures that incur high cost for data transmission and cover a limited area are gradually being transformed to mobile edge computing architectures that employ edge ML, have the characteristics of lower latency and higher coverage, and are more reliable than cloud-based models [17]. In this article, we provide a review of the edge-based IoT healthcare frameworks that focus on health surveillance.…”

Health-BlockEdge: Blockchain-Edge Framework for Reliable Low-Latency Digital Healthcare Applications

“…Edge computing brings computational and processing resources near to the end-users and devices to perform necessary real-time data analysis and decision-making functions and to improve resource efficiency by reducing the amount of data transferred between the end systems and centralized cloud servers [ 15 , 21 ]. In addition, the inclusion of the edge network improves the data privacy, security, and reliability of the systems running on the edge architecture [ 22 ]. Multi-access Edge Computing (MEC) is the European Telecommunications Standards Institute’s (ETSI) standard for unleashing the full potential of low-latency 5G radio communications in the mobile access network [ 23 ].…”

Trends in IoT based solutions for health care: Moving AI to the edge

“…A transition from the mobile cloud computing model (MCC), characterized by high data transmission costs and limited coverage towards a mobile edge computing model (MEC) [22] with lowlatency and reliable edge ML models is then progressively happening in the smart healthcare domain.…”