Deployment and Dimensioning of Fog Computing-Based Internet of Vehicle Infrastructure for Autonomous Driving

Yu, Cunqian; Lin, Bin; Guo, Ping; Zhang, Wei; Li, Sen; He, Rongxi

doi:10.1109/jiot.2018.2875750

Cited by 76 publications

(29 citation statements)

References 26 publications

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“…In this section, we propose an optimal method for low-traffic scenarios, the pseudo code of which is given in Algorithm 1. Our method is composed of two stages: in stage 1, we find all candidates from VC graph G S by analyzing the adjacency matrices of graph jobs and related VCs shown in step 2 and step 3; in stage 2, we select one candidate that can minimize the value of the objective function given in (4). In this method, going through all possible candidates ensures identification of the optimal solution for the graph job allocation; however, the computational complexity will be O(n!C(K, n)), where n represents the number of components in the graph job, K = sj |κ j | indicates the number of available slots in the related VC, and C(K, n) stands for the K-choose-n operation.…”

Section: Optimal Graph Job Allocation Mechanismmentioning

confidence: 99%

Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV

Liwang

Hosseinalipour

Gao

et al. 2020

IEEE Internet Things J.

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Section: Optimal Graph Job Allocation Mechanismmentioning

confidence: 99%

Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV

Liwang

Hosseinalipour

Gao

et al. 2020

IEEE Internet Things J.

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“…Meanwhile, the vehicle's fog ability was utilized to compensate the vehicle's service cost through monetary reward, thus helping to delay the sensitive computing service. Yu et al [6] discussed the optimal deployment and dimensionality (ODD) of fog computing-based IoV infrastructure for autonomous driving. Two different architectural patterns, namely, coupling pattern and decoupling pattern, were proposed, and the ODD problem was transformed into two integer linear programming formulas to reduce the deployment cost.…”

Section: Related Work a Fog Computing In Iovmentioning

confidence: 99%

“…In particular, VFC utilizes a large number of cooperative enduser clients or near-user edge devices to perform huge amounts of communication and computation [3], which differs from other existing technologies in its proximity to end users, dense geographic distribution, and support for mobility [4], [5]. In order to enhance the computing and storage capabilities of the network edge, recently, a new network structure, named fog computing-based IoV (FC-IoV) [6], is proposed, which deploys fog servers at downtown intersections and accidentprone roads to enhance the computing and storage capabilities of the network edge.…”

Section: Introductionmentioning

confidence: 99%

Fog-Computing-Based Approximate Spatial Keyword Queries With Numeric Attributes in IoV

Zhu

Mao

et al. 2020

IEEE Internet Things J.

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Due to the popularity of on-board geographic devices, a large number of spatial-textual objects are generated in Internet of Vehicles (IoV). This development calls for Approximate Spatial Keyword Queries with numeric Attributes in IoV (A 2 SKIV), which takes into account the locations, textual descriptions, and numeric attributes of spatial-textual objects. Considering huge amounts of objects involved in the query processing, this paper comes up with the ideal of utilizing vehicles as fog-computing resource, and proposes the network structure called FCV, and based on which the fog-based Top-k A 2 SKIV query is explored and formulated. In order to effectively support network distance pruning, textual semantic pruning, and numerical attribute pruning simultaneously, a two-level spatialtextual hybrid index STAG-tree is designed. Based on STAGtree, an efficient Top-k A 2 SKIV query processing algorithm is presented. Simulation results show that, our STAG-based approach is about 1.87x (17.1x, resp.) faster in search time than the compared ILM (DBM, resp.) method, and our approach is scalable.

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“…For such large test campaigns projects, efficiently and effectively dealing with data is of paramount importance. The scientific literature has already provided an account of data management in large-scale automotive research projects (e.g., [7]), however, this information needs to be updated in the light of the evolution of the automated functions and of the data processing and management tools and architectures (e.g., cloud computing [8]). In this context, we are particularly interested in understanding how to design and develop a data management toolchain for automotive test data (both qualitative and quantitative, both vehicular and subjective), with a goal to support a wide spectrum research investigation.…”

Section: Introductionmentioning

confidence: 99%

The L3Pilot Data Management Toolchain for a Level 3 Vehicle Automation Pilot

Hiller

Koskinen²,

Berta

et al. 2020

Electronics

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As industrial research in automated driving is rapidly advancing, it is of paramount importance to analyze field data from extensive road tests. This paper investigates the design and development of a toolchain to process and manage experimental data to answer a set of research questions about the evaluation of automated driving functions at various levels, from technical system functioning to overall impact assessment. We have faced this challenge in L3Pilot, the first comprehensive test of automated driving functions (ADFs) on public roads in Europe. L3Pilot is testing ADFs in vehicles made by 13 companies. The tested functions are mainly of Society of Automotive Engineers (SAE) automation level 3, some of them of level 4. In this context, the presented toolchain supports various confidentiality levels, and allows cross-vehicle owner seamless data management, with the efficient storage of data and their iterative processing with a variety of analysis and evaluation tools. Most of the toolchain modules have been developed to a prototype version in a desktop/cloud environment, exploiting state-of-the-art technology. This has allowed us to efficiently set up what could become a comprehensive edge-to-cloud reference architecture for managing data in automated vehicle tests. The project has been released as open source, the data format into which all vehicular signals, recorded in proprietary formats, were converted, in order to support efficient processing through multiple tools, scalability and data quality checking. We expect that this format should enhance research on automated driving testing, as it provides a shared framework for dealing with data from collection to analysis. We are confident that this format, and the information provided in this article, can represent a reference for the design of future architectures to implement in vehicles.

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Deployment and Dimensioning of Fog Computing-Based Internet of Vehicle Infrastructure for Autonomous Driving

Cited by 76 publications

References 26 publications

Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV

Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV

Fog-Computing-Based Approximate Spatial Keyword Queries With Numeric Attributes in IoV

The L3Pilot Data Management Toolchain for a Level 3 Vehicle Automation Pilot

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