Abstract
The internet of things (IoT) is a technology that allows many objects used in daily life to produce a variety of data and transfer those data to other objects or systems. The application domain of this system is increasing day by day, and the technologies used for its infrastructure are also varied. However, to process the huge amount of sensor data effectively, smart and fast filtering solutions are required. As a data pre-processing task, smart data filtering improves not only the data processing speed but also the quality of data as well. In other words, big data management is facilitated by getting more effective results with little noise and meaningful data. In this study, we examined big IoT data stored on IoT edges to detect anomalies in temperature, age, gender, weight, height, and time data. In this context, the Logistic Regression algorithm was applied at both sensing and network layers for anomaly detection purposes. Furthermore, the performance of the classification algorithm in terms of speed and accuracy was reported as the output of the study.
Published Version
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