Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures

By Xueshi Hou, Yong Li, Min Chen, Di Wu, Depeng Jin, Sheng Chen. IEEE Transactions on Vehicular Technology, 2016

857 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/414a44b733de549f35e713b6c0dff4f3e3c0ee45

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In Automotive Engineering

In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Fog Computing: A Taxonomy, Survey and Future Directions (2016, background): “[31]) the challenges regarding privacy in Fog based vehicular computing have been pointed for further investigation.”
A Survey on Mobile Edge Computing: The Communication Perspective (2017, methodology): “This paradigm is termed as the Ad-hoc mobile cloud in literature [145]–[148].”
Mobility-Aware Edge Caching and Computing in Vehicle Networks: A Deep Reinforcement Learning (2018, background): “Recently, edge caching placements and computing offloading at the vehicles and the road side units (RSUs) have been proposed to efficiently improve the QoS for applications with intensive computations and hard deadlines [6], [7].”
Offloading in Internet of Vehicles: A Fog-Enabled Real-Time Traffic Management System (2018, background): “The idea of vehicular fog computing is proposed in [19], which utilizes parked and moving vehicles as computational and communicational infrastructures.”
Vehicular Fog Computing: Enabling Real-Time Traffic Management for Smart Cities (2019, methodology): “The concept of VFC was presented in [4], utilizing both parked and moving vehicles as computational and communication infrastructures.”
Computation Offloading Toward Edge Computing (2019, background): “Accordingly, vehicles equipped with onboard computers [65], IoT gateways associated with the cellular network [66], and even smartphones [67] are the infrastructures of edge nodes.”
Challenges and Solutions for Cellular Based V2X Communications (2021, background): “…and accurate mobility model is required to predict and evaluate the movement of surrounding vehicles so that vehicles can connect with each other and share data [97]. b) Resource utilization and management: Resource utilization and management should be addressed to take full advantage of VFC.”
Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach (2017, methodology): “[13] X.”
Networking and Communications in Autonomous Driving: A Survey (2019, neutral): “[202] present an overview of potential”
On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration (2017, neutral): “For vehicular communications and automotive, integrating MEC with mobile cloud computing [197], [198] or vehicular”
Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study (2017, background): “IoT applications (like vehicle-to-vehicle communication, virtual reality applications, online games) may require service-deployment latencies below a few tens of milliseconds [15], [16], [17], [18].”
Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks (2019, background): “viewed slowmoving and parked vehicles as infrastructure in providing computational resources for vehicles with computation-intensive tasks in the vehicular edge computing [25].”
Fog Computing and the Internet of Things: A Review (2018, background): “X. Hou et al. [42] presented an overview of vehicles as the infrastructure for communication and computation, which is known as vehicular fog computing (VFC).”
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey (2018, background): “Note that there have been already some early efforts in this field, such as the articles [100, 145, 172]; nevertheless, we are still far from having a working and resilient communication protocol for high-speed users and automotive communication for fog computing.”
Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach (2018, background): “To address these issues, mobile edge computing (MEC) [13], [14] have been studied to deploy computing resources closer to end vehicles, which can efficiently improve the quality of service (QoS) for applications that require intensive computations and low latency [15], [16].”
A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges (2017, background): “For instance, the work on vehicular applications (e.g., [70]–[72]) focuses on the fog nodes’ mobility when other works, such as healthcare-oriented ones (e.g., [68] and [80]), exclusively focused on IoT devices’ mobility.”
Vehicular Edge Computing and Networking: A Survey (2019, background): “is necessary to utilize all available underutilized resources from existing network entities. By aggregating abundant and unused resources of vehicles, the QoS of applications can be greatly enhanced [67]. Because of rich and idle resources, Parked Vehicles (PVs) have the potential of providing a po”
Mobile-Edge Computing for Vehicular Networks: A Promising Network Paradigm with Predictive Off-Loading (2017, background): “In [5], the authors designed a vehicular fog computing architecture that utilizes a collaboration of vehicles and near-user edge devices to carry out communication and computation.”
Resource Scheduling in Edge Computing: A Survey (2021, background): “For example, the parked vehicles (PVs) account for a large portion of the global vehicles and have idle time to perform computational workloads [36], [37].”