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

Yu, Zhengxin; Hu, Jia; Min, Geyong; Zhao, Zhiwei; Miao, Wang; Hossain, M. Shamim

doi:10.1109/tits.2020.3017474

Cited by 213 publications

(62 citation statements)

References 31 publications

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“…In addition, in the field of Internet of Vehicles, the mobility cache also plays a big role. Yu et al propose a mobile proactive edge caching strategy based on federated learning (mpcf) [27]. This new strategy allows multiple vehicles to use private training data on local vehicles to jointly learn a model for predicting content popularity.…”

Section: Mobility Caching In Icn Networkmentioning

confidence: 99%

An Active Path-Associated Cache Scheme for Mobile Scenes

2022

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With the widespread growth of mass content, information-centric networks (ICN) have become one of the research hotspots of future network architecture. One of the important features of ICN is ubiquitous in-network caching. In recent years, the explosive growth of mobile devices has brought content dynamics, which poses a new challenge to the original ICN caching mechanism. This paper focuses on the WiFi mobile scenario of ICN. We design a new path-associated active caching scheme to shorten the time delay of users obtaining content to enhance the user experience. In this article, based on the WiFi scenario, we first propose a solution for neighbor access point selection from a theoretical perspective, considering the caching cost and transition probability. The cache content is then forwarded based on the selected neighbor set. For cached content, we propose content freshness according to mobile characteristics and consider content popularity at the same time. For cache nodes, we focus on the size of the remaining cache space and the number of hops from the cache to the user. We have implemented this strategy based on the value of caching on the forwarding path. The simulation results show that our caching strategy has a significant improvement in performance compared with active caching and other caching strategies.

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Section: Mobility Caching In Icn Networkmentioning

confidence: 99%

An Active Path-Associated Cache Scheme for Mobile Scenes

2022

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“…He et al [32] designed a federated edge learning system, in which a so-called importance-aware joint data selection and resource allocation algorithm was developed to improve the learning speed, and further solve the learning efficiency maximization problem in mobile computing. Yu et al [33] utilized the content caching scheme for vehicular application development in edge networks. They applied the federated learning technique to build a global model to predict the content popularity while protecting the privacy of training data in local vehicles.…”

Section: B Federated Learning With Vehicular Networkmentioning

confidence: 99%

Two-Layer Federated Learning With Heterogeneous Model Aggregation for 6G Supported Internet of Vehicles

Zhou

Liang

She

et al. 2021

IEEE Trans. Veh. Technol.

204

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The vision of the upcoming 6G technologies that have fast data rate, low latency, and ultra-dense network, draws great attentions to the Internet of Vehicles (IoV) and Vehicle-to-Everything (V2X) communication for intelligent transportation systems. There is an urgent need for distributed machine learning techniques that can take advantages of massive interconnected networks with explosive amount of heterogeneous data generated at the network edge. In this study, a two-layer federated learning model is proposed to take advantages of the distributed end-edgecloud architecture typical in 6G environment, and to achieve a more efficient and more accurate learning while ensuring data privacy protection and reducing communication overheads. A novel multi-layer heterogeneous model selection and aggregation scheme is designed as a part of the federated learning process to better utilize the local and global contexts of individual vehicles and road side units (RSUs) in 6G supported vehicular networks. This context-aware distributed learning mechanism is then developed and applied to address intelligent object detection, which is one of the most critical challenges in modern intelligent transportation systems with autonomous vehicles. Evaluation results showed that the proposed method, which demonstrates a higher learning accuracy with better precision, recall and F1 score, outperforms other state-of-the-art methods under 6G network configuration by achieving faster convergence, and scales better with larger numbers of RSUs involved in the learning process. Index Terms-Federated learning, End-edge-cloud computing, Internet of vehicles, Heterogeneous data, 6G technology I. INTRODUCTION EAMLESS and ubiquitous communication infrastructure has been a key enabler of modern intelligent services. While

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“…In (Dong et al, 2019), the authors have proposed a hierarchical velocity profile optimization strategy to influence driving behavior for the ultimate goal of reducing fuel consumption with the presence of several traffic lights. In (Yu et al, 2020), the authors have presented a mobility-aware approach based on a proactive edge-caching scheme with federated learning to improve cache performance and protect vehicles' privacy. In (D'Angelo et al, 2020), the authors have described a multidimensional approach for the detection of attacks on CVs based on the messages exchanged between vehicles.…”

Section: Related Workmentioning

confidence: 99%

Toward an Intelligent Driving Behavior Adjustment Based on Legal Personalized Policies Within the Context of Connected Vehicles

Outay

Jabeur

Haddad

et al. 2021

Front. Built Environ.

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The advent of Connected Vehicles (CVs) is creating new opportunities within the transportation sector. It is, indeed, expected to improve road traffic safety, enhance mobility, reduce fuel consumption and gas emissions, as well as foster economic growth via investments and jobs. However, to motivate the deployment of CVs and maximize their related benefits, policymakers must create appropriate neutral legal frameworks. These frameworks should promote the innovation of current road infrastructures, support cooperation and interoperability between transportation systems, and encourage fair competition between companies while upholding consumer privacy as well as data protection. We argue that policymakers should also support innovative mobility services toward a better accommodation of individual drivers and vehicles. Within this scope, we are proposing in this paper an intelligent approach that promotes the implementation of personalized road policies based on driving behaviors, driving performance, and the ongoing road traffic situation. These policies, which are dynamic in space and time, ultimately aim to increase drivers’ awareness by encouraging behavioral self-regulation. To meet our goals, we are using software agents that autonomously manage the driving behaviors according to well-defined transitions between driving states while enabling appropriate message exchanges between CVs. We run software simulations as well as field tests and obtained promising results that would reflect the relevance of implementing our vision of personalized policies.

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

Cited by 213 publications

References 31 publications

An Active Path-Associated Cache Scheme for Mobile Scenes

An Active Path-Associated Cache Scheme for Mobile Scenes

Two-Layer Federated Learning With Heterogeneous Model Aggregation for 6G Supported Internet of Vehicles

Toward an Intelligent Driving Behavior Adjustment Based on Legal Personalized Policies Within the Context of Connected Vehicles

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