Affiliation:
1. School of Software, Shandong University & College of Computer Science and Technology, Harbin Engineering University
2. Department of Computer Science, University of Warwick
3. School of Software & C-FAIR, Shandong University
4. College of Computer Science and Technology, Harbin Engineering University
5. School of Software and C-FAIR, Shandong University
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.
Funder
National Natural Science Foundation of China
National Key R&D Program
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications
Reference51 articles.
1. A survey on near field communication (NFC) technology;Coskun Vedat;Wirel. Person. Commun.,2013
Cited by
3 articles.
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