A distributed deep learning-driven edge caching strategy for industrial IoT networks

The Industrial Internet-of-Things (IIoT) refers to the use of interconnected networks of industrial-grade devices to enhance productivities and improve the efficiency of industrial processes. IIoT networks have low tolerance for delay and require timely wireless content access. As such, this study a...

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Main Author: Shen, Li Qin
Other Authors: A S Madhukumar
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/162938
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1629382022-11-14T06:14:28Z A distributed deep learning-driven edge caching strategy for industrial IoT networks Shen, Li Qin A S Madhukumar School of Computer Science and Engineering ASMadhukumar@ntu.edu.sg Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence The Industrial Internet-of-Things (IIoT) refers to the use of interconnected networks of industrial-grade devices to enhance productivities and improve the efficiency of industrial processes. IIoT networks have low tolerance for delay and require timely wireless content access. As such, this study aims to investigate the use of an edge computing model, Multi-access Edge Computing (MEC), and a distributed deep learning-driven edge caching strategy to jointly support ultra-reliable, low-latency content access in IIoT networks. Specifically, the proposed distributed framework harnesses on the computing power of edge servers to run deep learning models concurrently. Using simulated network traffic data, the distributed deep learning-driven edge caching strategy was evaluated based on two key performance indicators, cache hit rate and latency. Simulation and real-time results show that the proposed strategy is able to attain 5-15% higher cache hit rates and 10-22% lower latencies compared to traditional benchmark frameworks, including least recently used, and least frequently used. Bachelor of Engineering (Computer Science) 2022-11-14T06:14:28Z 2022-11-14T06:14:28Z 2022 Final Year Project (FYP) Shen, L. Q. (2022). A distributed deep learning-driven edge caching strategy for industrial IoT networks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162938 https://hdl.handle.net/10356/162938 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
spellingShingle Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Shen, Li Qin
A distributed deep learning-driven edge caching strategy for industrial IoT networks
description The Industrial Internet-of-Things (IIoT) refers to the use of interconnected networks of industrial-grade devices to enhance productivities and improve the efficiency of industrial processes. IIoT networks have low tolerance for delay and require timely wireless content access. As such, this study aims to investigate the use of an edge computing model, Multi-access Edge Computing (MEC), and a distributed deep learning-driven edge caching strategy to jointly support ultra-reliable, low-latency content access in IIoT networks. Specifically, the proposed distributed framework harnesses on the computing power of edge servers to run deep learning models concurrently. Using simulated network traffic data, the distributed deep learning-driven edge caching strategy was evaluated based on two key performance indicators, cache hit rate and latency. Simulation and real-time results show that the proposed strategy is able to attain 5-15% higher cache hit rates and 10-22% lower latencies compared to traditional benchmark frameworks, including least recently used, and least frequently used.
author2 A S Madhukumar
author_facet A S Madhukumar
Shen, Li Qin
format Final Year Project
author Shen, Li Qin
author_sort Shen, Li Qin
title A distributed deep learning-driven edge caching strategy for industrial IoT networks
title_short A distributed deep learning-driven edge caching strategy for industrial IoT networks
title_full A distributed deep learning-driven edge caching strategy for industrial IoT networks
title_fullStr A distributed deep learning-driven edge caching strategy for industrial IoT networks
title_full_unstemmed A distributed deep learning-driven edge caching strategy for industrial IoT networks
title_sort distributed deep learning-driven edge caching strategy for industrial iot networks
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/162938
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