Abstract
In recent years, the number of Internet of Things (IoT) devices/sensors has been increased to a great extent. IoT makes use of connected intelligent devices to gather the data using embedded sensors and actuator. The IoT devices generate huge amount of data which are currently being processed using cloud computing. Considering real-time patient monitoring in the healthcare industry, there is a delay caused by sending data to the cloud and receiving back to the application which causes high latency. To address this issue, fog computing plays a major role in computation, analytics, and storing sensitive data of the patient with the advantages of reduced latency, quick decision-making, improved energy efficiency, and reduced network congestion. With real-time monitoring of the critical health condition in-place by means of a smart medical device connected to a smartphone application can save a life on time. In this chapter, a fog-based scenario is considered where health data from patients are collected and transferred to the fog nodes. These data are filtered, preprocessed, and analyzed, and dynamic decisions are made using intelligent methodologies that are incorporated in the fog. The decisions are made based on the current patient state and stored continuously for long-term analysis, while abnormality alone is notified to people via mobile apps and other linked devices. Thus, we have compiled this chapter with the introduction of sensors in healthcare, key advantage of processing them in fog instead of cloud, their evolution, and future directions.
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Jeya shree, G., Padmavathi, S. (2020). A Fog-Based Approach for Real-Time Analytics of IoT-Enabled Healthcare. In: Raj, P., Chatterjee, J., Kumar, A., Balamurugan, B. (eds) Internet of Things Use Cases for the Healthcare Industry. Springer, Cham. https://doi.org/10.1007/978-3-030-37526-3_3
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DOI: https://doi.org/10.1007/978-3-030-37526-3_3
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