A Study on the Accuracy of IoT-Sensed Data Using Machine Learning
K. P. Maheswari, N. Anuradha, S. Nirmala Devi · 2023
The booming technology of Internet of Things (IoT) consists of smart devices with embedded systems. The handling of data through IoT devices is not simple, as it involves CPUs, sensors, and communication devices, among other things. Data collection takes place in a variety of environments. In such a case, it is critical that data collected by sensor devices be accurate. A method for processing data from a variety of environments is needed to determine the correctness of data. Machine learning techniques are useful for determining the degree of accuracy of data detected by IoT systems. Machine learning, which uses mathematical principles to generate behavior models without the need for programming, is a boon to use with the IoT. Another important trait is being multidisciplinary, which supports data from different environments that are easy to formulate in the format needed. In this chapter, a sample data set retrieved by the sensor device and stored in the cloud is used to analyze the degree of accuracy of the sensed data by training the data set with machine learning techniques. This chapter not only aids in determining the degree of accuracy of IoT-sensed data, but it also focuses on the efficacy of machine learning algorithms in dealing with IoT.