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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., Pradeep Reynold A.
Copyright: © 2022 |Pages: 12
ISBN13: 9781799891321|ISBN10: 1799891321|ISBN13 Softcover: 9781799891338|EISBN13: 9781799891345
DOI: 10.4018/978-1-7998-9132-1.ch008
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MLA

D., Jeya Mala, and Pradeep Reynold A. "Edge Analytics With Machine Learning Technique for Medical IoT Applications." Integrating AI in IoT Analytics on the Cloud for Healthcare Applications, edited by D. Jeya Mala, IGI Global, 2022, pp. 131-142. https://doi.org/10.4018/978-1-7998-9132-1.ch008

APA

D., J. M. & A., P. R. (2022). Edge Analytics With Machine Learning Technique for Medical IoT Applications. In D. Jeya Mala (Ed.), Integrating AI in IoT Analytics on the Cloud for Healthcare Applications (pp. 131-142). IGI Global. https://doi.org/10.4018/978-1-7998-9132-1.ch008

Chicago

D., Jeya Mala, and Pradeep Reynold A. "Edge Analytics With Machine Learning Technique for Medical IoT Applications." In Integrating AI in IoT Analytics on the Cloud for Healthcare Applications, edited by D. Jeya Mala, 131-142. Hershey, PA: IGI Global, 2022. https://doi.org/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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