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2022 | Book

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Mobile Edge Caching in Heterogeneous Vehicular Networks

Authors: Dr. Huaqing Wu, Assist. Prof. Feng Lyu, Dr. Xuemin Shen

Publisher: Springer International Publishing

Book Series : SpringerBriefs in Computer Science

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

To support smart vehicular services especially in the future driverless era, the vehicular networks are expected to support high-bandwidth content delivery and reliable accessibility of multifarious applications. However, the limited radio spectrum resources, the inflexibility in accommodating dynamic traffic demands, and the geographically constrained fixed infrastructure deployment of current terrestrial networks pose great challenges in ensuring ubiquitous, flexible, and reliable network connectivity. This book investigates mobile edge content caching and delivery in heterogeneous vehicular networks (HetVNets) to provide better service quality for vehicular users with resource utilization efficiency enhancement. Specifically, this book introduces the background of HetVNets and mobile edge caching, provides a comprehensive overview of mobile edge caching-assisted HetVNet techniques in supporting vehicular content delivery, and proposes/designs mobile edge content caching and delivery schemes in different HetVNet scenarios respectively to enhance vehicular content delivery performance. Afterward, this book outlines open issues and research directions in future mobile edge caching-assisted space-air-ground integrated vehicular networks.
The topics addressed in this book are crucial for both the academic community and industry, since mobile edge caching in heterogeneous networks has become an essential building block for the communication systems. The systematic principle of this book provides valuable insights on the efficient exploitation of heterogeneous network resources to fully unleash their differential merits in supporting vehicular applications. In addition, this book considers different HetVNet scenarios from terrestrial HetVNets to air-ground HetVNets and space-air-ground HetVNets, which can provide a general overview for interested readers with a comprehensive understanding of applying mobile edge caching techniques in enhancing vehicular content delivery performance, and offer a systematized view for researchers and practitioners in the field of mobile edge caching to help them design and optimize the desired vehicular content delivery systems.Provides in-depth studies on mobile edge content caching and delivery scheme design for three typical HetVNet scenarios;Comprehensively covers the analysis, design, and optimization of the mobile edge content caching-assisted HetVNets;Systematically addresses vehicle mobility, network service interruptions, and dynamic service request distribution issues in the mobile edge content caching and delivery.

Table of Contents

Frontmatter
Chapter 1. Introduction
Abstract
The advanced technology of connected and automated vehicles (CAVs) enables vehicles to interact with their internal and external environments to improve road safety, transportation efficiency, and the experience of both drivers and passengers. To empower smart vehicular services especially in the future driverless era, the CAV networks are expected to support high-bandwidth content delivery and reliable accessibility of multifarious applications. However, the limited radio spectrum resources, the inflexibility in accommodating dynamic traffic demands, and geographically constrained fixed infrastructure deployment of current terrestrial networks pose great challenges in ensuring ubiquitous, flexible, and reliable network connectivity. To address these challenges in a cost-effective way, heterogeneous vehicular networks (HetVNets) that integrate terrestrial networks with non-terrestrial networks can be leveraged to boost network capacity, enhance system robustness, and provide ubiquitous 3D wireless coverage. Furthermore, mobile edge caching technologies can be utilized in HetVNets to further mitigate backhaul traffic burden and reduce vehicular content delivery delay. In this chapter, we first provide an overview of the vehicular content delivery networks and then elaborate the mobile edge caching-assisted HetVNets with differentiated network characteristics. Finally, we present the key research problems investigated in this monograph.
Huaqing Wu, Feng Lyu, Xuemin Shen
Chapter 2. Techniques for Content Delivery Performance Enhancement
Abstract
As the performance of vehicular content delivery can be enhanced by carefully designing content delivery schemes and content caching schemes, in this chapter, we provide a comprehensive survey of techniques for vehicular content delivery performance enhancement. Particularly, we present the state-of-the-art literature review in two sections. First, we present existing works on the heterogeneous vehicular networking (HetVNet) techniques, where multiple alternative networking techniques are utilized to off-load the access networks. Specifically, Wi-Fi and TV white space-based techniques in terrestrial HetVNets, unmanned aerial vehicle (UAV)-based techniques in air–ground vehicular networks, and satellite-based techniques in space–air–ground vehicular networks are investigated to off-load the cellular access networks and improve the content delivery performance. Second, existing works on mobile edge caching-assisted content delivery for backhaul offloading are further investigated, including the content placement scheme design and content delivery scheme design in different HetVNet scenarios.
Huaqing Wu, Feng Lyu, Xuemin Shen
Chapter 3. Delay-Minimized Mobile Edge Caching in the Terrestrial HetVNet
Abstract
The caching-assisted heterogeneous vehicular networks (HetVNets) are envisioned as a promising solution to support the ever-increasing vehicular applications. In this chapter, we investigate content caching in terrestrial HetVNets where Wi-Fi roadside units (RSUs), TV white space (TVWS) stations, and cellular base stations (CBSs) are considered to cache content files. To characterize the intermittent Wi-Fi and TVWS network connections, we establish an on–off model with service interruptions to describe the vehicular content delivery process. Content coding is then leveraged to resist the impact of unstable network connections with optimized coding parameters. By jointly considering the impact of file profiles and network characteristics, we investigate the content placement in heterogeneous APs to minimize the average content delivery delay, which is formulated as an integer linear programming problem. Adopting the idea of the student admission model, the formulated problem is then transformed into a many-to-one matching problem and solved by our proposed stable matching-based caching scheme. Simulation results demonstrate that the proposed scheme can achieve near-optimal performances in terms of delivery delay and offloading ratio with low complexity.
Huaqing Wu, Feng Lyu, Xuemin Shen
Chapter 4. Optimal UAV Caching and Trajectory Design in the AGVN
Abstract
In this chapter, we investigate the UAV-assisted mobile edge caching to assist terrestrial vehicular networks in delivering high-bandwidth content files. To maximize the overall network throughput, we formulate a joint caching and trajectory optimization (JCTO) problem to jointly optimize content placement, content delivery, and UAV trajectory. Considering the intercoupled decisions and the limited UAV energy, the formulated JCTO problem is intractable directly and timely. Therefore, we propose a deep supervised learning (DSL) scheme to enable intelligent edge for real-time decision-making in the highly dynamic vehicular networks. Specifically, we first propose a clustering-based two-layered (CBTL) algorithm to solve the JCTO problem offline. With a given content placement strategy, we devise a time-based graph decomposition method to jointly optimize the content delivery and trajectory design, with which we then leverage the particle swarm optimization (PSO) algorithm to further optimize the content placement. We then design a convolutional neural network (CNN)-based DSL architecture to make fast decisions online. The network density and content request distribution with spatial–temporal dimensions are labeled as channeled images and input to the CNN-based model, and the results achieved by the CBTL algorithm are labeled as model outputs. With the CNN-based model, a function mapping the input network information to output decisions can be intelligently learnt to make timely decisions. Extensive trace-driven experiments are carried out to demonstrate the efficiency of CBTL in solving the JCTO problem and the superior learning performance with the CNN-based model.
Huaqing Wu, Feng Lyu, Xuemin Shen
Chapter 5. Load- and Mobility-Aware Cooperative Content Delivery in the SAGVN
Abstract
In this chapter, we investigate cooperative content delivery in the mobile edge caching-assisted space–air–ground integrated vehicular networks (SAGVNs), where vehicular content requests can be simultaneously served by multiple APs in space, aerial, and terrestrial networks. To minimize the overall content delivery delay while satisfying vehicular quality-of-service (QoS) requirements, we formulate a joint optimization problem of vehicle-to-AP association, bandwidth allocation, and content delivery ratio, referred to as the ABC problem. To address the tightly coupled optimization variables, we propose a load- and mobility-aware ABC (LMA-ABC) scheme to solve the joint optimization problem. Specifically, we first decompose the ABC problem to optimize the content delivery ratio. Then the impact of bandwidth allocation on the achievable delay performance is analyzed, and an effect of diminishing delay performance gain is revealed. Based on the analysis results, the LMA-ABC scheme is designed with the consideration of user fairness, load balancing, and vehicle mobility. Simulation results demonstrate that the proposed LMA-ABC scheme can significantly reduce the cooperative content delivery delay comparing to the benchmark schemes.
Huaqing Wu, Feng Lyu, Xuemin Shen
Chapter 6. Conclusions and Future Research Directions
Abstract
In this chapter, we conclude the main results and contributions of this monograph and present some future potential research directions.
Huaqing Wu, Feng Lyu, Xuemin Shen
Backmatter
Metadata
Title
Mobile Edge Caching in Heterogeneous Vehicular Networks
Authors
Dr. Huaqing Wu
Assist. Prof. Feng Lyu
Dr. Xuemin Shen
Copyright Year
2022
Electronic ISBN
978-3-030-88878-7
Print ISBN
978-3-030-88877-0
DOI
https://doi.org/10.1007/978-3-030-88878-7