Edge-assisted Solutions for IoT-based Connected Healthcare Systems: A Literature Review
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Abstract
In today’s modern era, with the rapid growth of edge-assisted solutions especially Internet of Things (IoT) networks, connected healthcare progressively relies on such solutions. Connected healthcare systems refer to health systems in which all the stakeholders are connected to each other. These systems employ novel technologies such as IoT, edge computing, and Artificial Intelligence (AI) to convert conventional health systems to more effective, appropriate, and customized intelligent systems. However, emerging connected healthcare systems encounter many restrictions and require new policies. Intelligent and edge computing technologies integration including IoT are a promising solution for most of the limitations arising from using cloud computing in connected healthcare applications. In fact, edge computing and AI are essential factors of smart IoT applications. Moving the computation and processing closer to the data sources and end-users, edge computing can reduce latency, bandwidth usage, and energy consumption. Notwithstanding the importance of edge-assisted solutions for connected healthcare systems, there is not any systematic and methodological research in this scope that investigates the existing studies considering various vital and relevant factors. Thus, this survey aims to comprehensively examine the state-of-the-art research in this area. We have reviewed the qualified existing works and divided them into two main taxonomies, including patient-centric and process-centric techniques, and their benefits and issues are also provided and discussed thoroughly. Furthermore, essential factors such as available datasets and parameters like accuracy, mobility, and data rate are comprehensively described and examined. In fact, in this paper, we bridge the gap between edge computing and connected healthcare solutions by comprehensively discussing relevant vital issues and highlighting the future trends.