Dywave: 为异构物联网传感信号设计的事件对齐动态分词方法
Dywave: Event-Aligned Dynamic Tokenization for Heterogeneous IoT Sensing Signals
AI总结：
本文提出Dywave，一种用于异构物联网传感信号的动态分词框架，通过小波基层次分解构建紧凑的输入表示，以适应内在时间结构和底层物理事件，从而在活动识别、压力评估和附近物体检测等任务中提升准确率并提高计算效率。
摘要：
物联网系统持续收集来自无处不在传感器的异构传感信号，以支持智能应用，如人类活动分析、情绪监测和环境感知。这些信号本质上是非平稳和多尺度的，给标准分词技术带来了独特挑战。本文提出Dywave，一种为物联网传感信号设计的动态分词框架，该框架构建了与内在时间结构和底层物理事件对齐的紧凑输入表示。Dywave利用基于小波的层次分解，识别出对应底层语义事件的时间边界，并自适应地压缩冗余区间，同时保持时间一致性。在五个真实物联网传感数据集上进行的广泛评估表明，Dywave在活动识别、压力评估和附近物体检测等任务中，比最先进的方法在准确率上提高了高达12%，同时通过减少输入标记长度最多75%来提高计算效率。此外，Dywave在面对领域偏移和变化的序列长度时表现出更强的鲁棒性。
英文摘要：
Internet of Things (IoT) systems continuously collect heterogeneous sensing signals from ubiquitous sensors to support intelligent applications such as human activity analysis, emotion monitoring, and environmental perception. These signals are inherently non-stationary and multi-scale, posing unique challenges for standard tokenization techniques. This paper proposes Dywave, a dynamic tokenization framework for IoT sensing signals that constructs compact input representations aligned with intrinsic temporal structures and underlying physical events. Dywave leverages wavelet-based hierarchical decomposition, identifies meaningful temporal boundaries corresponding to underlying semantic events, and adaptively compresses redundant intervals while preserving temporal coherence. Extensive evaluations on five real-world IoT sensing datasets across activity recognition, stress assessment, and nearby object detection demonstrate that Dywave outperforms state-of-the-art methods by up to 12% in accuracy, while improving computational efficiency by reducing input token lengths by up to 75% across mainstream sequence models. Moreover, Dywave exhibits improved robustness to domain shifts and varying sequence lengths.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
