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On edge deep learning implementation: approach to achieve 5G

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Abstract

Through 5G networks, mobile edge computing (MEC) brings the power of cloud computing, storage, and analysis closer to the end user. Innovative inventions in the domain of multimedia and others such as connected cars, large-scale IoT, video streaming, and industry robotics are made possible by improved speeds and reduced delays. On the other hand, in mobile edge computing, machine learning (ML) is leveraged to predict demand changes based on cultural events, natural disasters, or daily travel patterns, and it prepares the network by automatically scaling up network resources as required. Mobile edge computing and ML together allow seamless network management automation to decrease operating costs and boost user experience. In this paper, we discuss the state of art with in mobile edge computing with deep learning to server low-latency, real time application by providing application specific resource allocation. The experimental results have indicated significant amount of improvement in respond time while executing in low latency.

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Acknowledgements

On a grateful note, we want to acknowledge our sincere thanks to Dr. Pulak Konar, Associate Professor, Department of Mathematics, The ICFAI University, Tripura (Kamalghat, Mohanpur, West Tripura, India) for his association with this manuscript with useful discussion, valuable contribution and for constant support.

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This research received no specific grant from any funding agency in the public, commercial or not-for-profit sector.

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  1. Department of Computer Science and Engineering, Amity University, Kadampukur, 24PGS(N), Kolkata, West Bengal, 700135, India

    Dhritiman Mukherje & Aman Anand

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  1. Dhritiman Mukherje
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Correspondence to Dhritiman Mukherje.

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Mukherje, D., Anand, A. On edge deep learning implementation: approach to achieve 5G. Multimed Tools Appl 82, 12229–12243 (2023). https://doi.org/10.1007/s11042-022-13712-3

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