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Edge Analytics With Machine Learning Technique for Medical IoT Applications

Edge Analytics With Machine Learning Technique for Medical IoT Applications

Jeya Mala D. (Jain University, Bangalore, India) and Pradeep Reynold A. (GEMS Polytechnic College, India)
Copyright: © 2022 |Pages: 12
DOI: 10.4018/978-1-7998-9132-1.ch008
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Abstract

Edge analytics are tools and algorithms that are deployed in the internal storage of IoT devices or IoT gateways that collect, process, and analyze the data locally rather than transmitting it to the cloud for analysis. Edge analytics is applied in a wide range of applications in which immediate decision making is required. In the case of general IoT data analytics on the cloud, the data need to be collected from the IoT devices and to be sent to the cloud for further processing and decision making. In life-critical applications such as healthcare, the time taken to send the data to the cloud and then getting back the processed data to take decisions will not be acceptable. Hence, in these kinds of MIoT applications, it is essential to have analytics to be done on the edge in order to avoid such delays. Hence, this chapter is providing an abstract view on the application of machine learning in MIoT so that the data analytics provides fruitful results to the stakeholders.
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Iot Analytics

Before going to the detailed discussion on IoT Analytics, let us first know about the entire process involved in it. The data collected by the sensors from the things that are smartphones, production machinery, electronic appliances will be securely communicated using the Internet of Things platform. As per the whitepaper given by Dataflair (2018), this IoT platform is acting as the backbone in IoT Analytics, as it collects and combines data from multiple devices and other platforms connected through the internet. This platform then applies analytics to take decision making and further processing and transfer of data.

IoT Analytics offers a futuristic dimension to the smart application areas such as medical and healthcare applications in which the devices are connected through the IoT platform.

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