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Abstract
Growing use of wearables within internet of things (IoT) creates ever-increasing multi-modal data from various smart health applications. The enormous volume of data generation creates new challenges in transmission, storage, and processing. There were challenges such as communication latency and data security associated with processing medical big data in cloud backend. Fog computing (FC) is an emerging distributed computing paradigm that solved these problems by leveraging local data processing, storage, filtering, and machine intelligence within an intermediate fog layer that resides between cloud and wearables devices. This paper focuses on doing survey on two major aspects of deploying fog computing for smart and connected health. Firstly, the role of machine learning-based edge intelligence in fog layer for data processing is investigated. A comprehensive analysis is provided during the survey, highlighting the strength and improvements in the existing literature. The paper ends with some open challenges and future research areas in the domain of fog-based healthcare.
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