LeaD: Learn to Decode Vibration-based Communication for Intelligent Internet of Things

Guangrong Zhao, Bowen Du, Yiran Shen, Zhenyu Lao, L. Cui, Hongkai Wen
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引用次数: 3

Abstract

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.
LeaD:学习解码基于振动的智能物联网通信
在本文中,我们提出了一种新的基于振动的通信协议LeaD,该协议将振动的独特模式用于对发送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不同,基于符号解码的短消息,无论是二进制还是多变量,LeaD中的消息接收者接收的是与位组相对应的振动信号。每组由多个突发发送的符号组成,接收方通过基于机器学习的方法将这组符号作为一个整体进行解码。LeaD背后的基本原理是一组符号(1秒或0秒)的不同组合将产生独特的可重复的振动模式。因此,基于振动的通信中的解码可以建模为一个模式分类问题。我们设计并实现了许多不同的机器学习模型,作为LeaD解码算法的核心引擎,以学习和识别振动模式。通过对收集到的大量数据集的深入评估,基于卷积神经网络(CNN)的模型达到了最高的解码准确率(即最低的错误率),在40比特/秒的较高比特率下,解码准确率高达97%。而其竞争对手基于振动的通信协议只能达到10比特/秒和20比特/秒的传输速率,解码精度相似。此外,我们在不同具有挑战性的实际设置下评估了其性能,结果表明,带有CNN引擎的LeaD对姿态、距离(在有效范围内)和设备类型具有鲁棒性,因此,CNN模型可以预先进行一般训练,并广泛适用于不同情况下的不同物联网设备。最后,我们在现成的智能手机和智能手表上实现LeaD,以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,LeaD是轻量级的,可以在低成本的智能物联网设备(如智能手表)上原位运行,没有累积延迟,只引入边际系统开销。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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