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Deep Learning in Computer Vision Through Mobile Edge Computing for IoT

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Mobile Edge Computing

Abstract

The success of Artificial Intelligence (AI) through Deep Learning (DL) and Computer Vision has inspired many researchers to work on many real-life and human-centered tasks. These current AI systems are in use to augment the intelligence of IoT. IoT devices are equipped with very low computing and fewer storage resources. In the case of visual computing, a massive number of images or video data are needed to be processed, which seems to be not feasible for an IoT device. Therefore, those data are needed to transfer to a cloud machine for computation. However, in this case, bandwidth scarcity is a huge problem. Real-time computation and security and privacy of data are also very challenging issues. To handle this problem, Mobile Edge Computing (MEC) is used in IoT to perform the real-time computation locally. Combining state-of-the-art computer vision algorithms such as DL, especially Deep Convolutional Neural Network (CNN) based algorithms and MEC, can be a smart solution for onsite visual computing. This chapter scholarly discussed how deep CNN through MEC could be a potential technique for IoT based solutions. It also discuss how a deep transfer learning procedure can be applied in this method. This chapter proposes how different layers of deep CNN can be split up among Edge devices, Fog gateway, and Cloud servers to do visual computing at IoTs. Relevant technical backgrounds, current state-of-the-art, and future scopes are also emphasized in this chapter.

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Notes

  1. 1.

    https://fas.org/irp/nic/disruptive.pdf accessed on 11/02/2020.

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Authors and Affiliations

  1. Department of Computer Science, University of Gour Banga, Malda, West Bengal, India

    Abu Sufian, Anirudha Ghosh & Farhana Sultana

  2. Department of Computer Science, Gour Mahavidyalaya, Malda, West Bengal, India

    Ekram Alam

  3. Centre of Mobile Cloud Computing, Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India

    Debashis De

  4. Department of Sciences and Informatics, Muroran Institute of Technology, Hokkaido, Japan

    Mianxiong Dong

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Editors and Affiliations

  1. Department of Computer Science, Mahishadal Raj College, Mahishadal, West Bengal, India

    Anwesha Mukherjee

  2. Centre of Mobile Cloud Computing Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India

    Debashis De

  3. Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, West Bengal, India

    Soumya K. Ghosh

  4. Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

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Sufian, A., Alam, E., Ghosh, A., Sultana, F., De, D., Dong, M. (2021). Deep Learning in Computer Vision Through Mobile Edge Computing for IoT. In: Mukherjee, A., De, D., Ghosh, S.K., Buyya, R. (eds) Mobile Edge Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-69893-5_18

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