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    Front-End Intelligence for Large-Scale Application-Oriented Internet-of-Things

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    07491206.pdf
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    PDF
    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Bader, Ahmed
    Ghazzai, Hakim cc
    Kadri, Abdullah
    Alouini, Mohamed-Slim cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2016-06-14
    Online Publication Date
    2016-06-14
    Print Publication Date
    2016
    Permanent link to this record
    http://hdl.handle.net/10754/614417
    
    Metadata
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    Abstract
    The Internet-of-things (IoT) refers to the massive integration of electronic devices, vehicles, buildings, and other objects to collect and exchange data. It is the enabling technology for a plethora of applications touching various aspects of our lives such as healthcare, wearables, surveillance, home automation, smart manufacturing, and intelligent automotive systems. Existing IoT architectures are highly centralized and heavily rely on a back-end core network for all decision-making processes. This may lead to inefficiencies in terms of latency, network traffic management, computational processing, and power consumption. In this paper, we advocate the empowerment of front-end IoT devices to support the back-end network in fulfilling end-user applications requirements mainly by means of improved connectivity and efficient network management. A novel conceptual framework is presented for a new generation of IoT devices that will enable multiple new features for both the IoT administrators as well as end users. Exploiting the recent emergence of software-defined architecture, these smart IoT devices will allow fast, reliable, and intelligent management of diverse IoT-based applications. After highlighting relevant shortcomings of the existing IoT architectures, we outline some key design perspectives to enable front-end intelligence while shedding light on promising future research directions.
    Citation
    Front-End Intelligence for Large-Scale Application-Oriented Internet-of-Things 2016:1 IEEE Access
    Sponsors
    The work was made possible by NPRP grant #6-001-2-001 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Access
    DOI
    10.1109/ACCESS.2016.2580623
    Additional Links
    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7491206
    ae974a485f413a2113503eed53cd6c53
    10.1109/ACCESS.2016.2580623
    Scopus Count
    Collections
    Articles; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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