Vehicular Edge Computing and Networking: A Survey

By Lei Liu, Qingqi Pei, Sabita Maharjan, Yan Zhang. Journal on spesial topics in mobile networks and applications, 2019

546 citations recorded by the source corpus.

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 state-of-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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/5973e94435ad78899e8d94c786e02193c539c76c

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In Artificial Intelligence

In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Federated Learning in Vehicular Networks (2020, background): “Furthermore, previous works [2, 3] approach FL from the communications point-of-view.”
Federated Learning in Edge Computing: A Systematic Survey (2022, background): “Furthermore, IoT devices are equipped with heterogeneous and advanced sensors for various crowdsensing applications such as smart industry [2], healthcare [3], and Unmanned Areal Vehicle (UAV) [4] applications.”
Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach (2020, background): “storage and data resources to close proximity of vehicular users [4].”