Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

Sun, Yuxuan; Guo, Xueying; Song, Jinhui; Zhou, Sheng; Jiang, Zhiyuan; Liu, Xin; Niu, Zhisheng

doi:10.1109/tvt.2019.2895593

Cited by 291 publications

(135 citation statements)

References 28 publications

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“…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%

“…Thus, in fact, according to Figure 6(a), this is the most researched objective in this area. Several studies have proposed algorithms to decrease the response time of applications [93], [100], [121], [90].…”

Section: A: Decrease Response Timementioning

confidence: 99%

“…According to Mitchell [172], machine learning is a field of artificial intelligence (AI) that aims at improving a given algorithm automatically though experience. Among the collected works, support vector machine (SVM) [129], adaptive learning [100], reinforcement and deep reinforcement learning [14], [104], [108], [117], [146], [173], [174] are the machine learning techniques that stand out.…”

Section: E: Machine Learningmentioning

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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“…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%

Section: A: Decrease Response Timementioning

confidence: 99%

Section: E: Machine Learningmentioning

confidence: 99%

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Computation Offloading for Vehicular Environments: A Survey

et al. 2020

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“…Vehicular-WiFi offloading techniques such as V2I and opportunistic V2V are proposed in [33 -35] where the offloading decision is based on the offloading media, i.e., RSUs. In the cooperative downloading mechanism, RSUs play an optimal role to fetch data from the internet and distribute them to the respective vehicular users [44,45].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Reward-Based Data Offloading in Next-Generation Vehicular Networks

Raja

Ganapathisubramaniyan²,

Anbalagan

et al. 2020

IEEE Internet Things J.

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A massive increase in the number of mobile devices and data hungry vehicular network applications creates a great challenge for Mobile Network Operators (MNOs) to handle huge data in cellular infrastructure. However, due to fluctuating wireless channels and high mobility of vehicular users, it is even more challenging for MNOs to deal with vehicular users within a licensed cellular spectrum. Data offloading in vehicular environment plays a significant role in offloading the vehicle's data traffic from congested cellular network's licensed spectrum to the free unlicensed WiFi spectrum with the help of Road Side Units (RSUs). In this paper, an Intelligent Reward based Data Offloading in Next Generation Vehicular Networks (IR-DON) architecture is proposed for dynamic optimization of data traffic and selection of intelligent RSU. Within IR-DON architecture, an Intelligent Access Network Discovery and Selection Function (I-ANDSF) module with Q-Learning, a reinforcement learning algorithm is designed. I-ANDSF is modeled under Software-Defined Network (SDN) controller to solve the dynamic optimization problem by performing an efficient offloading. This increases the overall system throughput by choosing an optimal and intelligent RSU in the network selection process. Simulation results have shown the accurate network traffic classification, optimal network selection, guaranteed QoS, reduced delay and higher throughput achieved by the I-ANDSF module.

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“…The vehicular edge computing network integrates the computing resources of nearby vehicles and provides computing services. A learning-based offloading mechanism is proposed in [15].…”

Section: Related Workmentioning

confidence: 99%

Learning-Based Task Offloading for Marine Fog-Cloud Computing Networks of USV Cluster

et al. 2019

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In recent years, unmanned surface vehicles (USVs) have made important advances in civil, maritime, and military applications. With the continuous improvement of autonomy, the increasing complexity of tasks, and the emergence of various types of advanced sensors, higher requirements are imposed on the computing performance of USV clusters, especially for latency sensitive tasks. However, during the execution of marine operations, due to the relative movement of the USV cluster nodes and the network topology of the cluster, the wireless channel states are changing rapidly, and the computing resources of cluster nodes may be available or unavailable at any time. It is difficult to accurately predict in advance. Therefore, we propose an optimized offloading mechanism based on the classic multi-armed bandit (MAB) theory. This mechanism enables USV cluster nodes to dynamically make offloading decisions by learning the potential computing performance of their neighboring team nodes to minimize average computation task offloading delay. It is an optimized algorithm named Adaptive Upper Confidence Boundary (AUCB) algorithm, and corresponding simulations are designed to evaluate the performance. The algorithm enables the USV cluster to effectively adapt to the marine vehicular fog computing networks, balancing the trade-off between exploration and exploitation (EE). The simulation results show that the proposed algorithm can quickly converge to the optimal computation task offloading combination strategy under heavy and light input data loads.

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Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

Cited by 291 publications

References 28 publications

Computation Offloading for Vehicular Environments: A Survey

Computation Offloading for Vehicular Environments: A Survey

Intelligent Reward-Based Data Offloading in Next-Generation Vehicular Networks

Learning-Based Task Offloading for Marine Fog-Cloud Computing Networks of USV Cluster

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