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Due to the fast, dynamic, and continuous arrival of data streams in the green Internet of Things (IoT) environment, the probability distribution of data streams changes over time. In real IoT scenarios such as unmanned aerial vehicle (UAV) detection and smart light switch control, data distribution changes have reduced the trained model’s accuracy for data streams problems classification, making it challenging to detect UAV intruders and predict whether energy-saving lamps in smart buildings are on or off. In this paper, an incremental ensemble classification method is proposed to improve prediction accuracy for green IoT. Specifically, a fuzzy rule-based classifier is combined with a dynamic weighting algorithm for improving classification accuracy. Moreover, the model is updated by incrementally learning the characteristics of data streams, which can effectively handle concept drift caused by data distribution …