Proactive content delivery for vehicles over cellular networks: The fundamental benefits of computing and caching

Jiao, Jiping; Hong, Xuemin; Shi, Jianghong

doi:10.1109/cc.2018.8424606

Cited by 6 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reference Objective Key Points Data Collecting and Processing [93] Request Answering Propose a filter-based request answering framework [94] Data collection Vehicular virtual cloud is introduced as virtual edge server [95] Data monitoring and gathering A fog-based two-level threshold scheme is designed for preventing the transmissions of unnecessary data [96] Data analytics Present a real-time big data analytics framework Data Dissemination [97] Message dissemination Design a message dissemination scheme assisted by MEC [98] Context-aware service Propose a context-aware data-driven intelligent framework [99] Multicast routing Devise an energy-efficient multicast routing scheme with SDN and fog computing Content Delivery [100] Content delivery Study the tradeoff problem of communication, computing and cache for proactive caching [101] realtime streaming The streaming contents are allocated in advance from computing service providers [102] Content distribution Investigate the prefetching and distribution of contents…”

Section: Themementioning

confidence: 99%

“…Propose a robust and distributed incentive mechanism for content caching and dissemination in a collaborative method Performance Optimization [105] Bandwidth optimization A vehicle flow model is introduced into the optimization framework [106] Congestion avoidance Present a fog-based congestion avoidance strategy [107] Data scheduling Design two dynamic scheduling algorithms to schedule data [108] Data processing Propose a cooperative fog computing mechanism for big data processing [109] Energy-efficient Integrate big data analytical into VEC nodes, there is no need to access the remote cloud for content acquiring, shortening the delivery time. The work of [100] provides a theoretical architecture to enable content delivery by balancing computation, caching and communication resources.…”

Section: Themementioning

confidence: 99%

See 1 more Smart Citation

Vehicular Edge Computing and Networking: A Survey

et al. 2020

View full text Add to dashboard Cite

As one key enabler of Intelligent Transportation System (ITS), Vehicular Ad Hoc Network (VANET) has received remarkable interest from academia and industry. The emerging vehicular applications and the exponential growing data have naturally led to the increased needs of communication, computation and storage resources, and also to strict performance requirements on response time and network bandwidth. In order to deal with these challenges, Mobile Edge Computing (MEC) is regarded as a promising solution. MEC pushes powerful computational and storage capacities from the remote cloud to the edge of networks in close proximity of vehicular users, which enables low latency and reduced bandwidth consumption. Driven by the benefits of MEC, many efforts have been devoted to integrating vehicular networks into MEC, thereby forming a novel paradigm named as Vehicular Edge Computing (VEC). In this paper, we provide a comprehensive survey of stateof-art research on VEC. First of all, we provide an overview of VEC, including the introduction, architecture, key enablers, advantages, challenges as well as several attractive application scenarios. Then, we describe several typical research topics where VEC is applied. After that, we present a careful literature review on existing research work in VEC by classification. Finally, we identify open research issues and discuss future research directions.

show abstract

Section: Themementioning

confidence: 99%

Section: Themementioning

confidence: 99%

Vehicular Edge Computing and Networking: A Survey

et al. 2020

View full text Add to dashboard Cite

show abstract

“…For cache hit rate improvement, Costa, 69 Baccour, 70 and Song 72 utilized hierarchical structure, Markov chain, and integer problem, respectively. For the next parameter, Jiao, 71 with tree topology, improved prediction accuracy. Zheng, 68 with a heuristic approach, covered mobility support.…”

Section: Proactive Content Caching and Content Caching In The Edge Co...mentioning

confidence: 99%

Proactive content caching in edge computing environment: A review

2021

View full text Add to dashboard Cite

Edge computing environment provides processing capability and computing at the network edge and close to users. Edge equipment includes small data centers that locally perform process and content delivery. Therefore, edge equipment management has received much attention due to the rapid growth of information and resources limitations. The content caching and proactive caching techniques are management methods of edge equipment resources. We recently witnessed the development of proactive caching mechanisms that have a crucial enabler in improving heavy traffic, energy, and bandwidth. Also, it has huge potential to increase the quick response time to users' requests that today, these services are demanded by many users and applications. This article prepares a systematic literature review content caching approach in the edge computing environment. The purpose of this study is to survey the research done on the proactive caching strategies in the edge computing environment to identify subjects that must be emphasized more in current and future research paths. This research has studied 71 articles divided into three classes: model‐based, machine‐learning‐based, and heuristic‐based. Next, we discuss content caching approaches based on critical factors such as performance metrics, case studies, utilized techniques, assessment tools, advantages, and disadvantages. Finally, open issues and challenges are presented, and the survey is concluded.

show abstract

“…The works in [26], [28], [29] and [31] not only focus on the content caching, but also consider the content delivery. Authors in [26] designed a non-parametric estimator to learn the intensity function of requests, and then proposed a learning-based caching algorithm in D2D-enabled networks.…”

Section: Related Workmentioning

confidence: 99%

“…Li et al constructed a fog-community architecture for content caching in D2D enabled F-RAN from the social view point [29]. A theoretical framework is proposed in [31] to characterize the tradeoff among computing, cache and communication resources for content delivery in the mobile edge network.…”

Section: Related Workmentioning

confidence: 99%

Dueling Deep-Q-Network Based Delay-Aware Cache Update Policy for Mobile Users in Fog Radio Access Networks

et al. 2020

View full text Add to dashboard Cite

Fog radio access networks (F-RANs) can effectively alleviate fronthaul loads and reduce content transmission delay by migrating cloud services to the network edge. This paper addresses a cooperative caching scenario in F-RAN, where each mobile user can acquire the requested contents from any one of its associated fog-computing-based access points (F-APs). However, caching disparate contents in different F-APs will lead to different content delivery delays, since mobile users suffer from diverse channel fadings and interferences when they download contents from different F-APs. Considering limited caching storage in each F-AP, diverse user preferences, unpredictable user mobility and time-varying channel states, an average transmission delay minimization problem is formulated. With the aid of dueling deep-Q-network framework, a delay-aware cache update policy is proposed for mobile users in F-RAN. The proposed cache update policy will decide to replace the stored contents in F-APs with the proper contents at each time slot. Compared with first in first out, least recently used and least frequently used caching policies, simulation experiments are performed to evaluate the performance of the proposed algorithm. Simulation results illustrate that the proposed caching policy yields better average hit ratio and lower average transmission delay than other traditional caching policies. INDEX TERMS Caching, fog radio access network, hit ratio, mobility, reinforcement learning.

show abstract

Proactive content delivery for vehicles over cellular networks: The fundamental benefits of computing and caching

Cited by 6 publications

References 24 publications

Vehicular Edge Computing and Networking: A Survey

Vehicular Edge Computing and Networking: A Survey

Proactive content caching in edge computing environment: A review

Dueling Deep-Q-Network Based Delay-Aware Cache Update Policy for Mobile Users in Fog Radio Access Networks

Contact Info

Product

Resources

About