针对物联网异构传感信号，提出动态分块方法提升识别精度与效率
Dywave: Event-Aligned Dynamic Tokenization for Heterogeneous IoT Sensing Signals
- 基于小波分解识别事件边界，自适应压缩冗余片段
- 在五大数据集上准确率最高提升12%，输入长度减少75%
- 适合处理非平稳、多尺度的物联网时序数据
物联网系统持续采集来自各类传感器的异构感知信号，用于人体活动分析、情绪监测和环境感知等智能应用。这些信号具有固有的非平稳性和多尺度特性，给标准分词技术带来挑战。本文提出 Dywave，一种面向物联网传感信号的动态分词框架，通过构建与内在时间结构及物理事件对齐的紧凑输入表示，实现高效建模。Dywave 利用小波基分层分解，识别对应语义事件的时间边界，并自适应压缩冗余区间，同时保持时间连贯性。在五个真实世界物联网传感数据集上的广泛评估表明，Dywave 在活动识别、压力评估和附近物体检测任务中，相比现有最优方法准确率最高提升12%，主流序列模型输入令牌长度减少高达75%，且对领域偏移和序列长度变化更具鲁棒性。
原文摘要 · Abstract (English)
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.
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