TS-based Mobility-aware Multi-hierarchical Caching Model with Vehicle Clustering and Content Popularity Prediction

Authors

DOI:

https://doi.org/10.26636/jtit.2024.3.1616

Keywords:

clustering, Internet of Vehicles, machine learning, mobile edge computing, multi-hierarchical caching, Thompson sampling learning

Abstract

This research is concerned with the fusion of artificial intelligence (AI) and machine learning within multi-hierarchical caching systems, specifically targeting vehicular and edge caching domains. This study introduces an innovative architecture harmonizing Thompson sampling learning-based caching policies with advanced vehicle clustering and content-popularity prediction methods (TS-MMCM). Simulations show substantial performance improvements and a big impact of the proposed approach on system efficiency in dynamic network environments. The proposal demonstrates a notable gain in cache hit rates and decreased latency levels, highlighting the potential of AI to improve caching techniques in dynamic network environments.

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References

B. Radouane, G. Lyamine, K. Ahmed, and B. Kamel, "Scalable Mobile Computing: From Cloud Computing to Mobile Edge Computing", 2022 5th International Conference on Networking, Information Systems and Security: Envisage Intelligent Systems in 5G/6G-based Interconnected Digital Worlds (NISS), Bandung, Indonesia, 2022. DOI: https://doi.org/10.1109/NISS55057.2022.10085600
View in Google Scholar

W. Jiang et al., "Multi-agent Reinforcement Learning for Efficient Content Caching in Mobile D2D Networks", IEEE Transactions on Wireless Communications, vol. 18, no. 3, pp. 1610-1622, 2019. DOI: https://doi.org/10.1109/TWC.2019.2894403
View in Google Scholar

Z. Yang, Y. Liu, Y. Chen, and L. Jiao, "Learning Automata Based Q-learning for Content Placement in Cooperative Caching", IEEE Transactions on Communications, vol. 68, no. 6, pp. 3667-3680, 2020. DOI: https://doi.org/10.1109/TCOMM.2020.2982136
View in Google Scholar

X. Fang, T. Zhang, Y. Liu, and Z. Zeng, "Multi-agent Cooperative Alternating Q-learning Caching in D2D-enabled Cellular Networks", IEEE Global Communication Conference (GLOBECOM), Waikoloa, USA, 2019. DOI: https://doi.org/10.1109/GLOBECOM38437.2019.9014053
View in Google Scholar

Y. Qian et al., "Reinforcement Learning-based Optimal Computing and Caching in Mobile Edge Network", IEEE Journal on Selected Areas in Communications, vol. 38, no. 10, pp. 2343-2355, 2020. DOI: https://doi.org/10.1109/JSAC.2020.3000396
View in Google Scholar

P. Liu, Y. Zhang, T. Fu, and J. Hu, "Intelligent Mobile Edge Caching for Popular Contents in Vehicular Cloud Toward 6G", IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 5265-5274, 2021. DOI: https://doi.org/10.1109/TVT.2021.3076304
View in Google Scholar

W. Qi et al., "Extensive Edge Intelligence for Future Vehicular Networks in 6G", IEEE Wireless Communications, vol. 28, no. 4, pp. 128-135, 2021. DOI: https://doi.org/10.1109/MWC.001.2000393
View in Google Scholar

J. Shi et al., "A Novel Deep Q-learning-based Air-assisted Vehicular Caching Scheme for Safe Autonomous Driving", IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4348-4358, 2021. DOI: https://doi.org/10.1109/TITS.2020.3018720
View in Google Scholar

Z. Zhang, C.-H. Lung, M. St-Hilaire, and I. Lambadaris, "Smart Proactive Caching: Empower the Video Delivery for Autonomous Vehicles in ICN-based Networks", IEEE Transactions on Vehicular Technology, vol. 69, no. 7, pp. 7955-7965, 2020. DOI: https://doi.org/10.1109/TVT.2020.2994181
View in Google Scholar

Y. Liu and B. Mao, "On a Novel Content Edge Caching Approach Based on Multi-Agent Federated Reinforcement Learning in Internet of Vehicles", 2023 32nd Wireless and Optical Communications Conference (WOCC), Newark, USA, 2023. DOI: https://doi.org/10.1109/WOCC58016.2023.10139417
View in Google Scholar

A. Ndikumana et al., "Deep Learning Based Caching for Self-driving Cars in Multi-access Edge Computing", IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 5, pp. 2862-2877, 2021. DOI: https://doi.org/10.1109/TITS.2020.2976572
View in Google Scholar

Z. Zhu et al., "Proactive Caching in Auto Driving Scene via Deep Reinforcement Learning", 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP), Xi’an, China, 2019. DOI: https://doi.org/10.1109/WCSP.2019.8928131
View in Google Scholar

A. Ndikumana and C.S. Hong, "Self-driving Car Meets Multi-access Edge Computing for Deep Learning-based Caching", 2019 International Conference on Information Networking (ICOIN), Kuala Lumpur, Malaysia, 2019. DOI: https://doi.org/10.1109/ICOIN.2019.8718113
View in Google Scholar

W. Jiang, G. Feng, S. Qin, and Y.-C. Liang, "Learning-based Cooperative Content Caching Policy for Mobile Edge Computing", ICC 2019 - 2019 IEEE International Conference on Communications (ICC), Shanghai, China, 2019. DOI: https://doi.org/10.1109/ICC.2019.8761121
View in Google Scholar

C. Zhang et al., "Toward Edge-assisted Video Content Intelligent Caching with Long Short-term Memory Learning", IEEE Access, vol. 7, pp. 152832-152846, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2947067
View in Google Scholar

Y. Ye, M. Xiao, and M. Skoglund, "Mobility-aware Content Preference Learning in Decentralized Caching Networks", IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 1, pp. 62-73, 2020. DOI: https://doi.org/10.1109/TCCN.2019.2937519
View in Google Scholar

Y. Zhang et al., "Cooperative Edge Caching: A Multi-agent Deep Learning Based Approach", IEEE Access, vol. 8, pp. 133212-133224, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3010329
View in Google Scholar

X. Xu, M. Tao, and C. Shen, "Collaborative Multi-agent Multi-armed Bandit Learning for Small-cell Caching", IEEE Transactions on Wireless Communications, vol. 19, no. 4, pp. 2570-2585, 2020. DOI: https://doi.org/10.1109/TWC.2020.2966599
View in Google Scholar

R. Wang et al., "Cooperative Caching Strategy with Content Request Prediction in Internet of Vehicles", IEEE Internet of Things Journal, vol. 8, no. 11, pp. 8964-8975, 2021. DOI: https://doi.org/10.1109/JIOT.2021.3056084
View in Google Scholar

X. Bi and L. Zhao, "Collaborative Caching Strategy for RL-based Content Downloading Algorithm in Clustered Vehicular Networks", IEEE Internet of Things Journal, vol. 10, no. 11, pp. 9585-9596, 2023. DOI: https://doi.org/10.1109/JIOT.2023.3235661
View in Google Scholar

T. Kanungo et al., "An Efficient K-means Clustering Algorithm: Analysis and Implementation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881-892, 2002. DOI: https://doi.org/10.1109/TPAMI.2002.1017616
View in Google Scholar

D. Zhang et al., "New Multi-hop Clustering Algorithm for Vehicular Ad Hoc Networks", IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 4, pp. 1517-1530, 2019. DOI: https://doi.org/10.1109/TITS.2018.2853165
View in Google Scholar

C. Zhang et al., "Deep Transfer Learning for Intelligent Cellular Traffic Prediction Based on Cross-domain Big Data", IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1389-1401, 2019. DOI: https://doi.org/10.1109/JSAC.2019.2904363
View in Google Scholar

C. Olah, Understanding LSTM Networks, Github, 2015 [Online] Available: http://colah.github.io/posts/2015-08-Understanding-LSTMs/.
View in Google Scholar

C. Wang, Z. Zhao, Q. Sun, and H. Zhang, "Deep Learning-based Intelligent Dual Connectivity for Mobility Management in Dense Network", 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), Chicago, USA, 2018. DOI: https://doi.org/10.1109/VTCFall.2018.8690554
View in Google Scholar

G. Barlacchi et al., "Multi-source Dataset of Urban Life in the City of Milan and the Province of Trentino", Scientific Data, no. 2, art. no. 150055, 2015. DOI: https://doi.org/10.1038/sdata.2015.55
View in Google Scholar

Y. Cui, Y. Liang, and R. Wang, "Resource Allocation Algorithm with Multi-platform Intelligent Offloading in D2D-enabled Vehicular Networks", IEEE Access, vol. 7, pp. 21246-21253, 2019. DOI: https://doi.org/10.1109/ACCESS.2018.2882000
View in Google Scholar

S. Agrawal and N. Goyal, "Thompson Sampling for Contextual Bandits with Linear Payoffs", ArXiv, 2012.
View in Google Scholar

Y. Chen, J. Liu, Y. Shi, and M. Sheng, "Prefetch and Cache Replacement Based on Thompson Sampling for Satellite IoT Network", ICC 2021 - IEEE International Conference on Communications, Montreal, Canada, 2021. DOI: https://doi.org/10.1109/ICC42927.2021.9500508
View in Google Scholar

L. Zhao et al., "Intelligent Content Caching Strategy in Autonomous Driving Toward 6G", IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 9786-9796, 2022. DOI: https://doi.org/10.1109/TITS.2021.3114199
View in Google Scholar

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Published

2024-09-30

Issue

No. 3 (2024)

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ARTICLES FROM THIS ISSUE

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Copyright (c) 2024 Radouane Baghiani, Lyamine Guezouli, Ahmed Korichi

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
R. Baghiani, L. Guezouli, and A. Korichi, “TS-based Mobility-aware Multi-hierarchical Caching Model with Vehicle Clustering and Content Popularity Prediction”, JTIT, vol. 97, no. 3, pp. 65–78, Sep. 2024, doi: 10.26636/jtit.2024.3.1616.

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