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Intelligent Mobile Edge Computing: A Deep Learning Based Approach

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Advances in Computing and Data Sciences (ICACDS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1244))

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Abstract

In recent times researchers across the globe have shown keen interest towards advancements in the domain of edge computing. Mobile Edge Computing (MEC) is a new age computing paradigm wherein cloud services are made accessible at network edges via the use of mobile base stations. It is a promising technology that helps in overcoming the limitations of mobile cloud computing. MEC facilitates seamless integration of various application services, thereby proving cloud resources at the edge of the network, within the vicinity of the end-user’s locality. It can effortlessly be integrated with the upcoming 5G architecture, hence supporting the execution of resource-rich applications that require low network latency. In order to enhance the levels of intelligence at mobile base stations, deep learning algorithms can be implemented over network edges for rendering optimized communication and workload balancing. The paper discusses a conceptual architecture for creating a mobile edge computing environment involving the applicability of deep learning algorithms. The paper discusses the fundamentals of MEC along with specific applications of reinforcement and continuous learning in an edge environment. We list the benefits of MEC along with a discussion on how its amalgamation with deep learning models can prove beneficial in case of a computation offloading scenario.

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Authors and Affiliations

  1. University of Petroleum and Energy Studies, Dehradun, India

    Abhirup Khanna, Anushree Sah & Tanupriya Choudhury

Authors
  1. Abhirup Khanna
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  2. Anushree Sah
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  3. Tanupriya Choudhury
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Corresponding author

Correspondence to Abhirup Khanna .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    P. K. Gupta

  3. Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. University of Malta, Valletta, Malta

    Gianluca Valentino

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Khanna, A., Sah, A., Choudhury, T. (2020). Intelligent Mobile Edge Computing: A Deep Learning Based Approach. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Valentino, G. (eds) Advances in Computing and Data Sciences. ICACDS 2020. Communications in Computer and Information Science, vol 1244. Springer, Singapore. https://doi.org/10.1007/978-981-15-6634-9_11

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  • DOI: https://doi.org/10.1007/978-981-15-6634-9_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-6633-2

  • Online ISBN: 978-981-15-6634-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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