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LeaD : Learn to decode vibration-based communication for intelligent Internet of Things
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Zhao, Guangrong, Du, Bowen, Shen, Yiran, Lao, Zhenyu, Cui, Lizhen and Wen, Hongkai (2021) LeaD : Learn to decode vibration-based communication for intelligent Internet of Things. ACM Transactions on Sensor Networks, 17 (3). pp. 1-25. doi:10.1145/3440250 ISSN 1550-4867.
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Official URL: https://doi.org/10.1145/3440250
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.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
| SWORD Depositor: | Library Publications Router | ||||||
| Journal or Publication Title: | ACM Transactions on Sensor Networks | ||||||
| Publisher: | Association for Computing Machinery (ACM) | ||||||
| ISSN: | 1550-4867 | ||||||
| Official Date: | August 2021 | ||||||
| Dates: |
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| Volume: | 17 | ||||||
| Number: | 3 | ||||||
| Page Range: | pp. 1-25 | ||||||
| DOI: | 10.1145/3440250 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Reuse Statement (publisher, data, author rights): | ** From Crossref journal articles via Jisc Publications Router ** History: ppub 21-06-2021; issued 21-06-2021. | ||||||
| Access rights to Published version: | Restricted or Subscription Access |
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