The Internet of Things generates massive data streams, with edge computing emerging as a key enabler for online IoT applications and 5G networks. Edge solutions facilitate real-time machine learning inference, but also require continuous adaptation to concept drifts. Ensemble-based solutions improve predictive performance, but incur higher resource consumption, latency, and memory demands. This paper presents DFDT: Dynamic Fast Decision Tree, a novel algorithm designed for energy-efficient memory-constrained data stream mining. DFDT improves hoeffding tree growth efficiency by dynamically adjusting grace periods, tie thresholds, and split evaluations based on incoming data. It incorporates stricter evaluation rules (based on entropy, information gain, and leaf instance count), adaptive expansion modes, and a leaf deactivation mechanism to manage memory, allowing more computation on frequently visited nodes while conserving energy on others. Experiments show that the proposed framework can achieve increased predictive performance (0.43 vs 0.29 ranking) with constrained memory and a fraction of the runtime of VFDT or SVFDT.

本研究解决了边缘计算中物联网数据流挖掘的高资源消耗和适应性不足的问题。提出的动态快速决策树（DFDT）算法通过动态调整参数以提高存储和计算效率，提出了更严格的评估规则以优化内存管理。实验结果表明，DFDT在有限内存和运行时间下显著提升了预测性能，显示出其在物联网应用中的潜在影响。

DFDT：边缘设备上物联网数据流挖掘的动态快速决策树