Edge Computing for Secured IoT Analytics on the Cloud

Edge Computing for Secured IoT Analytics on the Cloud

S. Meenakshi Sundaram, Tejaswini R. Murgod
Copyright: © 2022 |Pages: 14
DOI: 10.4018/978-1-7998-9132-1.ch010
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Abstract

This chapter provides an insight into building healthcare applications that are deployed in the cloud storage using edge computing and IoT data analytics approaches. Data is collected from environments both within or external to the hospital. The devices that are connected enable the healthcare providers to monitor patients at large distances, manage chronic disease, and manage medication dosages. The data from these devices can be added to clinical research to gain an insight into the participant's experiences. Artificial intelligence techniques like machine learning or deep learning can be employed at the edge of the networks for IoT analytics of multiple data streams in online mode. The industrial edge computing is growing rapidly from 7% in 2019 to being expected to reach approximately 16% by 2025. The total market for intelligent industrial edge computing that includes hardware, software, services has reached $11.6B in 2019 and is expected to increase to $30.8B by 2025.
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Background

There are three types of edges namely thick edge, thin edge and micro edge. The thick edge describes compute resources (typically located within a data center) which are equipped with components designed to handle compute intensive tasks / workloads (e.g., high-end CPUs, GPUs, FGPAs, etc.) such as data storage and analysis. There are two types of computational resources located at the “thick” edge, which is typically located 100m to approximately 40 km from the data source. A Cell tower data centre has rack-based computing resources that are located at the base of cell towers. On major data centers, the rack-based computing resources are located at the same physical location as the sensors generate the data.

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