In this article, we propose, LeaD , a new vibration-based communication protocol to Lea rn the unique patterns of vibration to D ecode the short messages transmitted to smart IoT devices. Unlike the existing vibration-based communication protocols that decode the short messages symbol-wise, either in binary or multi-ary, the message recipient in LeaD receives vibration signals corresponding to bits-groups. Each group consists of multiple symbols sent in a burst and the receiver decodes the group of symbols as a whole via machine learning-based approach. The fundamental behind LeaD is different combinations of symbols (1 s or 0 s) in a group will produce unique and reproducible patterns of vibration. Therefore, decoding in vibration-based communication can be modeled as a pattern classification problem. We design and implement a number of different machine learning models as the core engine of the decoding algorithm of LeaD to learn and recognize the vibration patterns. Through the intensive evaluations on large amount of datasets collected, the Convolutional Neural Network (CNN)-based model achieves the highest accuracy of decoding (i.e., lowest error rate), which is up to 97% at relatively high bits rate of 40 bits/s. While its competing vibration-based communication protocols can only achieve transmission rate of 10 bits/s and 20 bits/s with similar decoding accuracy. Furthermore, we evaluate its performance under different challenging practical settings and the results show that LeaD with CNN engine is robust to poses, distances (within valid range), and types of devices, therefore, a CNN model can be generally trained beforehand and widely applicable for different IoT devices under different circumstances. Finally, we implement LeaD on both off-the-shelf smartphone and smart watch to measure the detailed resources consumption on smart devices. The computation time and energy consumption of its different components show that LeaD is lightweight and can run in situ on low-cost smart IoT devices, e.g., smartwatches, without accumulated delay and introduces only marginal system overhead.

中文翻译：

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领导：学习解码基于振动的智能物联网通信

在本文中，我们建议，带领，一种新的基于振动的通信协议莉亚rn 独特的振动模式D对传输到智能物联网设备的短消息进行编码。与现有的基于振动的通信协议以符号方式（二进制或多进制）解码短消息不同，消息接收者在带领接收对应于比特组的振动信号。每组由突发发送的多个符号组成，接收器通过基于机器学习的方法将符号组作为一个整体解码。背后的根本带领一组中符号的不同组合（1 秒或 0 秒）将产生独特且可重复的振动模式。因此，基于振动的通信中的解码可以建模为模式分类问题。我们设计并实现了许多不同的机器学习模型作为解码算法的核心引擎带领学习和识别振动模式。通过对收集到的大量数据集进行深入评估，基于卷积神经网络（CNN）的模型实现了最高的解码精度（即最低错误率），在相对较高的 40 比特/比特率下可达 97% s。而其竞争的基于振动的通信协议只能达到 10 位/秒和 20 位/秒的传输速率，并具有相似的解码精度。此外，我们评估了它在不同具有挑战性的实际设置下的性能，结果表明带领CNN 引擎对姿态、距离（在有效范围内）和设备类型具有鲁棒性，因此，CNN 模型通常可以预先训练并广泛适用于不同情况下的不同物联网设备。最后，我们实现带领在现成的智能手机和智能手表上测量智能设备上的详细资源消耗。其不同组件的计算时间和能耗表明：带领重量轻，可以运行原位在低成本的智能物联网设备上，例如智能手表，没有累积延迟，并且只引入了边际系统开销。