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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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/162938 |
| _version_ | 1826112683371397120 |
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| author | Shen, Li Qin |
| author2 | A S Madhukumar |
| author_facet | A S Madhukumar Shen, Li Qin |
| author_sort | Shen, Li Qin |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T03:11:04Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/162938 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:11:04Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-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 |
| 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 |
| title | 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_short | 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 |
| topic | Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/162938 |
| work_keys_str_mv | AT shenliqin adistributeddeeplearningdrivenedgecachingstrategyforindustrialiotnetworks AT shenliqin distributeddeeplearningdrivenedgecachingstrategyforindustrialiotnetworks |