Mobile Edge Computing for the Internet of Vehicles: Offloading Framework and Job Scheduling

Jiang, Feng; Liu, Zhi; Wu, Celimuge; Ji, Yusheng

doi:10.1109/mvt.2018.2879647

Cited by 165 publications

(86 citation statements)

References 14 publications

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“…Other papers used more than one technology at the same time. For example, [91] used WAVE and 4G to perform computational offloading; [92] used WAVE, 4G and 5G; while [93] used WAVE and 5G.…”

Section: C: Hybridmentioning

confidence: 99%

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Computation Offloading for Vehicular Environments: A Survey

et al. 2020

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With significant advances in communication and computing, modern day vehicles are becoming increasingly intelligent. This gives them the ability to contribute to safer roads and passenger comfort through network devices, cameras, sensors, and computational storage and processing capabilities. However, to run new and popular applications, and to enable vehicles operating autonomously requires massive computational resources. Computational resources available with the current day vehicles are not sufficient to process all these demands. In this situation, other vehicles, edge servers, and servers in remote data centers can help the vehicles by lending their computing resources. However, to take advantage of these computing resources, computation offloading techniques have to be leveraged to transfer tasks or entire applications to run on other devices. Such computation offloading can lead to improved performance and Quality of Service (QoS) for applications and for the network. However, computation offloading in a highly dynamic environment such as vehicular networks is a major challenge. Therefore, this survey aims to review and organize the computation offloading literature in vehicular environments. In addition, we demystify some concepts, propose a taxonomy with the most important aspects and classify most works in the area according to each category. We also present the main tools, scenarios, subjects, strategies, objectives, etc., used in the works. Finally, we present the main challenges and future directions to guide future research in this active research area.

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Section: C: Hybridmentioning

confidence: 99%

“…Another approach is to take advantage of beacon messages, exchanged periodically between devices, to know in advance who can perform tasks [100], [92].…”

Section: Volume 4 2020mentioning

confidence: 99%

Computation Offloading for Vehicular Environments: A Survey

et al. 2020

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“…The availability of computing capabilities at the edge of the network together with an overall management of central and edge resources opens up the possibility for several improvements in mobile networks to support vehicular use cases [17]. To take full advantage of edge computing, 5GCAR considered several enhancements from a core network perspective as well as from an access network point of view.…”

Section: Edge Computing Enhancementsmentioning

confidence: 99%

5G V2X System-Level Architecture of 5GCAR Project

et al. 2019

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One of the goals of the 5G Communication Automotive Research and innovation (5GCAR) project has been to evaluate and propose system architecture enhancements aiming at supporting the strict requirements of vehicle-to-everything (V2X) use cases. In this paper, we provide an overview of 3GPP 5G system architecture, which is used as a baseline architecture in the project, and we present the main architectural enhancements introduced by 5GCAR. The work of the project focused on the following categories: (i) end-to-end security, also including aspects of privacy; (ii) network orchestration and management; (iii) network procedures; (iv) edge computing enhancements; and (v) multi-connectivity cooperation. The enhancements introduced by 5GCAR to above-listed categories are discussed in this paper, while a more detailed analysis of some selected features is presented.

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“…An optimization problem is to find the best solution under certain conditions, and may be encountered in many applications, such as traffic signal timing, route decision, and congestion pricing strategy [34]- [36]. In order to solve the optimization problem, some general global optimizing algorithms, such as genetic algorithm (GA) [37], [38] particle swarm optimization (PSO) [39], [40] and ant colony optimization [41], [42] can be introduced to parameter identification. However, such methods are often used for offline applications.…”

Section: Copyright C 2019 the Institute Of Electronics Information Amentioning

confidence: 99%

Parameter Identification and State-of-Charge Estimation for Li-Ion Batteries Using an Improved Tree Seed Algorithm

Chen

Cai

Tan

et al. 2019

IEICE Trans. Inf. & Syst.

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Accurate estimation of the state-of-charge is a crucial need for the battery, which is the most important power source in electric vehicles. To achieve better estimation result, an accurate battery model with optimum parameters is required. In this paper, a gradient-free optimization technique, namely tree seed algorithm (TSA), is utilized to identify specific parameters of the battery model. In order to strengthen the search ability of TSA and obtain more quality results, the original algorithm is improved. On one hand, the DE/rand/2/bin mechanism is employed to maintain the colony diversity, by generating mutant individuals in each time step. On the other hand, the control parameter in the algorithm is adaptively updated during the searching process, to achieve a better balance between the exploitation and exploration capabilities. The battery state-of-charge can be estimated simultaneously by regarding it as one of the parameters. Experiments under different dynamic profiles show that the proposed method can provide reliable and accurate estimation results. The performance of conventional algorithms, such as genetic algorithm and extended Kalman filter, are also compared to demonstrate the superiority of the proposed method in terms of accuracy and robustness.

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Mobile Edge Computing for the Internet of Vehicles: Offloading Framework and Job Scheduling

Cited by 165 publications

References 14 publications

Computation Offloading for Vehicular Environments: A Survey

Computation Offloading for Vehicular Environments: A Survey

5G V2X System-Level Architecture of 5GCAR Project

Parameter Identification and State-of-Charge Estimation for Li-Ion Batteries Using an Improved Tree Seed Algorithm

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