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Blockchain- and Deep Learning-Empowered Resource Optimization in Future Cellular Networks, Edge Computing, and IoT: Open Challenges and Current Solutions

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Blockchain for 5G-Enabled IoT

Abstract

Blockchain and deep learning are promising future technologies. Blockchain promotes decentralized services in the distributed systems, with enhanced security, privacy, transparency, reliability, and robustness. The deep learning provides the intelligent optimized solution to uncertain, complex problems. The empowerment of deep learning techniques to blockchain technologies can enhance the enactment of various upcoming technologies. In this chapter, we tide over the gap for deep learning techniques and outline its application for resource management in blockchain-empowered future generation cellular networks, IoT, and edge computing. We provide a brief background of the above technologies and explored the deep learning techniques for resource management in the upcoming technologies – future generation cellular networks, IoT, and edge computing. After that, we discuss the current deep learning techniques potential to facilitate the efficient deployment of deep learning with blockchain onto upcoming emerging technologies. We provided the encyclopedia review of deep learning techniques. In the end, we conclude the analysis by pinpointing the current research challenges and directions for future research.

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Authors and Affiliations

  1. Department of Computer Science, Akal University, Talwandi Saboo, Punjab, India

    Upinder Kaur

  2. Department of Computer Science, Baba Farid College, Bathinda, Punjab, India

    Shalu

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  1. Upinder Kaur
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  2. Shalu
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  1. Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India

    Sudeep Tanwar

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Kaur, U., Shalu (2021). Blockchain- and Deep Learning-Empowered Resource Optimization in Future Cellular Networks, Edge Computing, and IoT: Open Challenges and Current Solutions. In: Tanwar, S. (eds) Blockchain for 5G-Enabled IoT. Springer, Cham. https://doi.org/10.1007/978-3-030-67490-8_17

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