Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Efficient Anomaly Heartbeat Detection Approach for Intermediate Nodes of Internet‐of‐Things Platforms

Version 1 : Received: 25 November 2022 / Approved: 1 December 2022 / Online: 1 December 2022 (08:07:47 CET)

How to cite: Ramirez Lopez, L.J. Efficient Anomaly Heartbeat Detection Approach for Intermediate Nodes of Internet‐of‐Things Platforms. Preprints 2022, 2022120014. https://doi.org/10.20944/preprints202212.0014.v1 Ramirez Lopez, L.J. Efficient Anomaly Heartbeat Detection Approach for Intermediate Nodes of Internet‐of‐Things Platforms. Preprints 2022, 2022120014. https://doi.org/10.20944/preprints202212.0014.v1

Abstract

This work focused on the evaluation of some machine learning (ML) models and their application in e-health, using intermediate nodes within an Internet of Things (IoT) platform used for heartbeat anomaly detection. For the evaluation of ML models, a set of statistical validation metrics was selected. These metrics were applied in the training, testing and validation phases of the models. The results obtained can determine relevant factors for the selection of ML models, either based on the statistical and intrinsic efficiency of the ML models, or on their suitability to be implemented in intermediate nodes within an IoT platform. the more Lightweight models such as Simple Linear Regression, Logistic Regression, and K Nearest Neighbors, could easily operate in intermediate nodes, and they are models that require low processing and storage to work. In conclusion, the approach for intermediate nodes of Internet of Things platforms using cognitive networks decreases the processing cost in cloud computing and transfers it to the fog layer.

Keywords

Machine Learning; Fog layer; Heartrate; Performance; IoT

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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