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AuthorAl-Marridi, Abeer Z.
AuthorMohamed, Amr
AuthorErbad, Aiman
AuthorAl-Ali, Abdulla
AuthorGuizani, Mohsen
Available date2020-05-14T09:55:45Z
Publication Date2019
Publication Name2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019
ResourceScopus
URIhttp://dx.doi.org/10.1109/IWCMC.2019.8766763
URIhttp://hdl.handle.net/10576/14862
AbstractIn the past few years, a rapid increase in the number of patients requiring constant monitoring, which inspires researchers to develop intelligent and sustainable remote smart healthcare services. However, the transmission of big real-time health data is a challenge since the current dynamic networks are limited by different aspects such as the bandwidth, end-to-end delay, and transmission energy. Due to this, a data reduction technique should be applied to the data before being transmitted based on the resources of the network. In this paper, we integrate efficient data reduction with wireless networking transmission to enable an adaptive compression with an acceptable distortion, while reacting to the wireless network dynamics such as channel fading and user mobility. Convolutional Auto-encoder (CAE) approach was used to implement an adaptive compression/reconstruction technique with the minimum distortion. Then, a resource allocation framework was implemented to minimize the transmission energy along with the distortion of the reconstructed signal while considering different network and applications constraints. A comparison between the results of the resource allocation framework considering both CAE and Discrete wavelet transforms (DWT) was also captured. - 2019 IEEE.
SponsorThis work was made possible by GSRA grant # GSRA5-1-0326-18026 from the Qatar National Research Fund (a member of Qatar Foundation). The work of Amr Mohamed, Aiman Erbad and Mohsen Guizani was supported by Qatar University Grant QUHI-CENG-19/20-1. The findings achieved herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectCompression
Convolutional Auto-encoder
E-Health systems
Optimization
Resource optimization
TitleEfficient EEG mobile edge computing and optimal resource allocation for smart health applications
TypeConference Paper
Pagination1261-1266
dc.accessType Abstract Only


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