Title: Edge computing-based internet of medical things for healthcare using deep learning
Authors: Himabindu Sathyaveti; C. Gomathy
Addresses: Electronics and Communication Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, Tamilnadu, India ' Electronics and Communication Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, Tamilnadu, India
Abstract: Edge cloud computing (ECC) servers for the internet of medical things (IoMT) have made medical systems smarter by providing patients with access to cutting-edge technologies that enable choosing medical facilities. Modern medical solutions have gained popularity because of their economic and ethical benefits. To improve the quality of service (QoS), more bandwidth (BW) and edge computing (EC) are essential. Connecting the IoMT to high-processing devices like these healthcare service devices often demands low network latencies. This study reviews the most important, currently existing state-of-the-art methods, including edge-based rehabilitation systems (ERS), IoT-based infectious disease management (IoT-IDM), and ML usage in healthcare IoT applications. Finally, the performances of the IoMT-influenced edge computing-based healthcare applications are evaluated for quality-of-service metrics such as latency, energy consumption, bandwidth utilisation rate, computational cost, memory consumption, and offloaded efficiency.
Keywords: edge computing; internet of medical things; IoMT; healthcare services; deep learning.
International Journal of Embedded Systems, 2023 Vol.16 No.2, pp.117 - 125
Received: 12 Sep 2022
Received in revised form: 28 Apr 2023
Accepted: 17 May 2023
Published online: 31 Jan 2024 *