Anomaly Detection and Performance Analysis by Using Big Data Filtering Techniques For Healthcare on IoT Edges

Abstract

The IoT is a sensors world that detects countless physical events in our environment and transforms them into data, and transfers this data to different environments or digital systems. The usage areas of Internet of things-based technologies are constantly increasing and technologies are being developed to support the IoT infrastructure. But, in order to effectively manage the large number of big-data generate in the detection layer, it should be pre-processed and done in accordance with big-data standards. For the effective management of big data, it is imperative to improving the standards of the data set, and filtering methods are being developed for a higher quality data set. For instance, using data cleaning methods is a preprocessing method that facilitates data mining operations. In this way, more manageable data is obtained by preventing the formation of interference and big data can be managed more effectively. In this study, we investigate the efficient operation of IoT and big data originating from the internet of things. Additionally, real-time anomalous data filtering is performed on IoT edges with a data set consisting of six different data produced in real- time. Furthermore, the speed and accuracy performances of classifiers are compared, and machine learning algorithms such as the random cut forest-RCF, logistic regression-LR, naive bayes-NB, and neural network-NN classifiers are used for comparison. According to the accuracy performance values, the RCF and LR classifiers are very close, but considering the speed values, it is seen that the LR classifier is more successful in IoT systems.