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    Intelligent Edge: An Instantaneous Detection of IoT Traffic Load

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    IntelligentEdgeICC.pdf
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    1.179Mb
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    Description:
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    Type
    Conference Paper
    Authors
    Alaslani, Maha S. cc
    Shihada, Basem cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2018-08-20
    Online Publication Date
    2018-08-20
    Print Publication Date
    2018-05
    Permanent link to this record
    http://hdl.handle.net/10754/631292
    
    Metadata
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    Abstract
    Internet of Things (IoT) can be defined as the interconnection of any device to the Internet that collects and exchanges information. With the rapidly growing heterogenetic IoT applications and its associated devices, a massive amount of data is being transmitted in the network. Often, a large spike in network traffic to a particular destination causes a widespread disruption of Internet services for end users, which can cause online businesses billions of dollars in losses. In this paper, we analyze an intelligent edge that can identify volumetric traffic and address them in real-time using an instantaneous detection method for IoT applications. This approach can easily detect a large surge and a potential variation in traffic patterns for an IoT application, which can contribute to safer and more efficient operation of the overall system. As per our results, we gave a closer insight on the advantage of having an intelligent edge to serve as a detection mechanism.
    Citation
    Alaslani M, Shihada B (2018) Intelligent Edge: An Instantaneous Detection of IoT Traffic Load. 2018 IEEE International Conference on Communications (ICC). Available: http://dx.doi.org/10.1109/ICC.2018.8423004.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2018 IEEE International Conference on Communications (ICC)
    Conference/Event name
    2018 IEEE International Conference on Communications, ICC 2018
    DOI
    10.1109/ICC.2018.8423004
    Additional Links
    https://ieeexplore.ieee.org/document/8423004
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICC.2018.8423004
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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