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EdgeLSTM : towards deep and sequential edge computing for IoT applications
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Wu, Di, Xu, He, Jiang, Zhongkai, Yu, Weiren, Wei, Xuetao and Lu, Jiwu (2021) EdgeLSTM : towards deep and sequential edge computing for IoT applications. IEEE/ACM Transactions on Networking, 29 (4). pp. 1895-1908. doi:10.1109/TNET.2021.3075468 ISSN 1063-6692.
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Official URL: http://dx.doi.org/10.1109/TNET.2021.3075468
Abstract
The time series data generated by massive sensors in Internet of Things (IoT) is extremely dynamic, heterogeneous, large scale and time-dependent. It poses great challenges (e.g. accuracy, reliability, stability) on the real-time analysis and decision making for different IoT applications. In this paper, we design, implement and evaluate EdgeLSTM, a unified data-driven system to enhance IoT computing at the network edge. The EdgeLSTM leverages the grid long short-term memory (Grid LSTM) to provide an agile solution for both deep and sequential computation, therefore can address important features such as large-scale, variety, time dependency and real time in IoT data. Our system exploits the advantages of Grid LSTM network and extends it with a multiclass support vector machine by rigorous regularization and optimization approaches, which not only has strong prediction capability of time series data, but also achieves fine-grained multiple classification through the predictive error. We deploy the EdgeLSTM into four IoT applications, including data prediction, anomaly detection, network maintenance and mobility management by extensive experiments. Our evaluation results of real-world time series data with different short-term and long-term time dependency from these typical IoT applications show that our EdgeLSTM system can guarantee robust performance in IoT computing.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
Journal or Publication Title: | IEEE/ACM Transactions on Networking | ||||||||
Publisher: | IEEE | ||||||||
ISSN: | 1063-6692 | ||||||||
Official Date: | August 2021 | ||||||||
Dates: |
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Volume: | 29 | ||||||||
Number: | 4 | ||||||||
Page Range: | pp. 1895-1908 | ||||||||
DOI: | 10.1109/TNET.2021.3075468 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access |
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