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Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning

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Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning. / Yu, Zhengxin; Hu, Jia; Min, Geyong; Zhao, Zhiwei; Miao, Wang; Hossain, M. Shamim.

In: IEEE Transactions on Intelligent Transportation Systems, Vol. 22, No. 8, 31.08.2021, p. 5341-5351.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Yu, Z, Hu, J, Min, G, Zhao, Z, Miao, W & Hossain, MS 2021, 'Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning', IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 8, pp. 5341-5351. https://doi.org/10.1109/TITS.2020.3017474

APA

Yu, Z., Hu, J., Min, G., Zhao, Z., Miao, W., & Hossain, M. S. (2021). Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning. IEEE Transactions on Intelligent Transportation Systems, 22(8), 5341-5351. https://doi.org/10.1109/TITS.2020.3017474

Vancouver

Yu Z, Hu J, Min G, Zhao Z, Miao W, Hossain MS. Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning. IEEE Transactions on Intelligent Transportation Systems. 2021 Aug 31;22(8):5341-5351. https://doi.org/10.1109/TITS.2020.3017474

Author

Yu, Zhengxin ; Hu, Jia ; Min, Geyong ; Zhao, Zhiwei ; Miao, Wang ; Hossain, M. Shamim. / Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning. In: IEEE Transactions on Intelligent Transportation Systems. 2021 ; Vol. 22, No. 8. pp. 5341-5351.

Bibtex

@article{f70a0b57bcf74977a64717fd0efe8367,
title = "Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning",
abstract = "Content Caching at the edge of vehicular networks has been considered as a promising technology to satisfy the increasing demands of computation-intensive and latency-sensitive vehicular applications for intelligent transportation. The existing content caching schemes, when used in vehicular networks, face two distinct challenges: 1) Vehicles connected to an edge server keep moving, making the content popularity varying and hard to predict. 2) Cached content is easily out-of-date since each connected vehicle stays in the area of an edge server for a short duration. To address these challenges, we propose a Mobility-aware Proactive edge Caching scheme based on Federated learning (MPCF). This new scheme enables multiple vehicles to collaboratively learn a global model for predicting content popularity with the private training data distributed on local vehicles. MPCF also employs a Context-aware Adversarial AutoEncoder to predict the highly dynamic content popularity. Besides, MPCF integrates a mobility-aware cache replacement policy, which allows the network edges to add/evict contents in response to the mobility patterns and preferences of vehicles. MPCF can greatly improve cache performance, effectively protect users' privacy and significantly reduce communication costs. Experimental results demonstrate that MPCF outperforms other baseline caching schemes in terms of the cache hit ratio in vehicular edge networks.",
author = "Zhengxin Yu and Jia Hu and Geyong Min and Zhiwei Zhao and Wang Miao and Hossain, {M. Shamim}",
year = "2021",
month = aug,
day = "31",
doi = "10.1109/TITS.2020.3017474",
language = "English",
volume = "22",
pages = "5341--5351",
journal = "IEEE Transactions on Intelligent Transportation Systems",
issn = "1524-9050",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "8",

}

RIS

TY - JOUR

T1 - Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning

AU - Yu, Zhengxin

AU - Hu, Jia

AU - Min, Geyong

AU - Zhao, Zhiwei

AU - Miao, Wang

AU - Hossain, M. Shamim

PY - 2021/8/31

Y1 - 2021/8/31

N2 - Content Caching at the edge of vehicular networks has been considered as a promising technology to satisfy the increasing demands of computation-intensive and latency-sensitive vehicular applications for intelligent transportation. The existing content caching schemes, when used in vehicular networks, face two distinct challenges: 1) Vehicles connected to an edge server keep moving, making the content popularity varying and hard to predict. 2) Cached content is easily out-of-date since each connected vehicle stays in the area of an edge server for a short duration. To address these challenges, we propose a Mobility-aware Proactive edge Caching scheme based on Federated learning (MPCF). This new scheme enables multiple vehicles to collaboratively learn a global model for predicting content popularity with the private training data distributed on local vehicles. MPCF also employs a Context-aware Adversarial AutoEncoder to predict the highly dynamic content popularity. Besides, MPCF integrates a mobility-aware cache replacement policy, which allows the network edges to add/evict contents in response to the mobility patterns and preferences of vehicles. MPCF can greatly improve cache performance, effectively protect users' privacy and significantly reduce communication costs. Experimental results demonstrate that MPCF outperforms other baseline caching schemes in terms of the cache hit ratio in vehicular edge networks.

AB - Content Caching at the edge of vehicular networks has been considered as a promising technology to satisfy the increasing demands of computation-intensive and latency-sensitive vehicular applications for intelligent transportation. The existing content caching schemes, when used in vehicular networks, face two distinct challenges: 1) Vehicles connected to an edge server keep moving, making the content popularity varying and hard to predict. 2) Cached content is easily out-of-date since each connected vehicle stays in the area of an edge server for a short duration. To address these challenges, we propose a Mobility-aware Proactive edge Caching scheme based on Federated learning (MPCF). This new scheme enables multiple vehicles to collaboratively learn a global model for predicting content popularity with the private training data distributed on local vehicles. MPCF also employs a Context-aware Adversarial AutoEncoder to predict the highly dynamic content popularity. Besides, MPCF integrates a mobility-aware cache replacement policy, which allows the network edges to add/evict contents in response to the mobility patterns and preferences of vehicles. MPCF can greatly improve cache performance, effectively protect users' privacy and significantly reduce communication costs. Experimental results demonstrate that MPCF outperforms other baseline caching schemes in terms of the cache hit ratio in vehicular edge networks.

U2 - 10.1109/TITS.2020.3017474

DO - 10.1109/TITS.2020.3017474

M3 - Journal article

VL - 22

SP - 5341

EP - 5351

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

SN - 1524-9050

IS - 8

ER -