Idea and Application to Explain Active Learning for Low-Carbon Sustainable AIoT

The Internet of Things (AIoT) is supporting the revolution of many industries. However, AIoT systems require a large amount of computing resources and electricity consumption as support, which leads to significant carbon emissions and energy consumption, which is not conducive to sustainable energy development. Reducing the demand for data in artificial intelligence through active learning (AL) is an effective solution. In this study, based on the interpretability of neural networks, we propose an interpretable AL algorithm. By improving the traditional heatmap display method, we use predicted probability entropy and posterior probability entropy to form an information class activation map information visualization method, thereby providing an explanation for the sources of information in AL. Meanwhile, we propose a Similarity-Loss AL sampling strategy to evaluate the information content of samples. The experimental results show that our proposed method has achieved good results in terms of interpretability and optimization of sampling in AL. In addition, the proposed Similarity-Loss sampling strategy has achieved the highest performance in current AL scenarios, contributing to achieving low-carbon and sustainable AIoT.