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Deep Learning: Edge-Cloud Data Analytics for IoT | IEEE Conference Publication | IEEE Xplore

Deep Learning: Edge-Cloud Data Analytics for IoT


Abstract:

Sensors, wearables, mobile and other Internet of Thing (IoT) devices are becoming increasingly integrated in all aspects of our lives. They are capable of collecting mass...Show More

Abstract:

Sensors, wearables, mobile and other Internet of Thing (IoT) devices are becoming increasingly integrated in all aspects of our lives. They are capable of collecting massive quantities of data that are typically transmitted to the cloud for processing. However, this results in increased network traffic and latencies. Edge computing has a potential to remedy these challenges by moving computation physically closer to the network edge where data are generated. However, edge computing does not have sufficient resources for complex data analytics tasks. Consequently, this paper investigates merging cloud and edge computing for IoT data analytics and presents a deep learning-based approach for data reduction on the edge with the machine learning on the cloud. The encoder part of the autoencoder is located on the edge to reduce data dimensions. Reduced data are sent to the cloud where there are used directly for machine learning or expanded to original features using the decoder part of the autoencoder. The proposed approach has been evaluated on the human activity recognition tasks. Results show that 50% data reduction did not have a significant impact on the classification accuracy and 77% reduction only caused 1% change.
Date of Conference: 05-08 May 2019
Date Added to IEEE Xplore: 11 October 2019
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ISSN Information:

Conference Location: Edmonton, AB, Canada

References

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