Improving Anomaly Detection in IoT-Based Solar Energy System Using SMOTE-PSO and SVM Model

Abstract

A large number of sensors based on Internet of Things (IoT) technology are now widely deployed in artificial intelligence, health care monitoring, air quality monitoring, and other fields. The sensors require high power consumption for real-time monitoring data. Some studies have suggested using solar energy for the primary power source to operate sensors. However, due to uncertain climate change, solar energy supply cannot always provide sufficient voltage to operate sensors. Consequently, some abnormal behavior events frequently occur in the IoT system using solar energy. Abnormal detection is a typical imbalanced learning problem due to the very rare amount of abnormal events. Under such data with skewed class distribution, classic classification models fail to provide reliable classification results with abnormal events. Under this condition, in this paper, deploying solar energy supply, we developed an IoT-based system using Arduino Microcontroller and Banana Pi, in which, the SMOTE-PSO algorithm is utilized to improve classification accuracies on abnormal event data in our system. Finally, two types of SVM kernel functions are used to verify classification capability in the developed IoT system.