Abstract

The Internet of Things (IoT) is rapidly becoming an integral part of our lives, and the growth of the interconnected devices, applications and services associated with it is continuously increasing. However, despite the numerous advantages of this technology, it is still prone to errors, resulting in decreased system reliability and efficiency. To overcome these issues, an automated error detection and analytical model can be beneficial. The model is based on supervised machine learning techniques which have been proven to be effective in performing anomaly detection tasks. This model is trained to detect and identify errors in IoT nodes by analyzing the streaming data from the nodes. The model employs a set of features and statistical measures such as mean, variance, trends, threshold rules and temporal patterns. These patterns are then used to detect potential errors in the data using supervised learning algorithms. The model is also capable of learning from the data and can be improved over time by deploying additional features. Once errors are detected, the model can generate an appropriate response to the problem by recommending the best course of action for rectifying the issue. This model can significantly increase the reliability of IoT nodes, leading to increased system performance and scalability.