Research on a Task Offloading Strategy for the Internet of Vehicles Based on Reinforcement Learning

Xiao, Shuo; Wang, Shengzhi; Zhuang, Jiayu; Wang, Tianyu; Li, Jiajia

doi:10.3390/s21186058

Cited by 10 publications

(16 citation statements)

References 33 publications

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“…However, systems that offload path planning problems have demonstrated up to 80% time saving with offloading decisions performed by using a resource-block based model [74]. Additionally, the proposed solution shows shorter learning times as well as shorter time to make a decision [75]. For the purposes of such comparison, it is essential to note that the resulting time conservation may be skewed by the underlying technology used in the implementation.…”

Section: Discussionmentioning

confidence: 99%

Automatic Path Planning Offloading Mechanism in Edge-Enabled Environments

et al. 2021

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The utilization of edge-enabled cloud computing in unmanned aerial vehicles has facilitated advances in autonomous control by employing computationally intensive algorithms frequently related to traversal among different locations in an environment. A significant problem remains in designing an effective strategy to offload tasks from the edge to the cloud. This work focuses on creating such a strategy by employing a network evaluation method built on the mean opinion score metrics in concoction with machine learning algorithms for path length prediction to assess computational complexity and classification models to perform an offloading decision on the data provided by both network metrics and solution depth prediction. The proposed system is applied to the A* path planning algorithm, and the presented results demonstrate up to 94% accuracy in offloading decisions.

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Section: Discussionmentioning

confidence: 99%

Automatic Path Planning Offloading Mechanism in Edge-Enabled Environments

et al. 2021

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“…Recently, task offloading in VNs has gained widespread attention in both industry and academia. A set of researchers are focused on optimizing the task-offloading problem from various perspectives [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. To utilize the resources that are available in the nearby vehicles and to minimize the total latency, Wang et al.…”

Section: Related Workmentioning

confidence: 99%

“…Xiao et al. [ 12 ] introduced a multi-agent reinforcement-learning-based task offloading strategy for the Internet of Vehicles (IoV). To balance between the task offloading delay and cost, they developed a three-stage Stackelberg game model.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Task Offloading for Cloud-Assisted Vehicular Edge Computing Networks: A Non-Cooperative Game Theoretic Approach

Hossain

Sultana

Layek

et al. 2022

Sensors

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Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and storage capabilities of vehicular networks (VNs) through task offloading. In VEC, the resource-constrained vehicles offload their computing tasks to the local road-side units (RSUs) for rapid computation. However, due to the high mobility of vehicles and the overloaded problem, VEC experiences a great deal of challenges when determining a location for processing the offloaded task in real time. As a result, this degrades the quality of vehicular performance. Therefore, to deal with these above-mentioned challenges, an efficient dynamic task offloading approach based on a non-cooperative game (NGTO) is proposed in this study. In the NGTO approach, each vehicle can make its own strategy on whether a task is offloaded to a multi-access edge computing (MEC) server or a cloud server to maximize its benefits. Our proposed strategy can dynamically adjust the task-offloading probability to acquire the maximum utility for each vehicle. However, we used a best response offloading strategy algorithm for the task-offloading game in order to achieve a unique and stable equilibrium. Numerous simulation experiments affirm that our proposed scheme fulfills the performance guarantees and can reduce the response time and task-failure rate by almost 47.6% and 54.6%, respectively, when compared with the local RSU computing (LRC) scheme. Moreover, the reduced rates are approximately 32.6% and 39.7%, respectively, when compared with a random offloading scheme, and approximately 26.5% and 28.4%, respectively, when compared with a collaborative offloading scheme.

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“…In recent years, with the development of wireless communication and intelligent vehicle technology, the on-board network has attracted extensive attention from the industry and academia [1][2][3][4]. Vehicle to everything (V2X) includes vehicle to vehicle (V2V), vehicle to infrastructure (V2I), vehicle to pedestrian (V2P) and vehicle to network (V2N).…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Spectrum Allocation Algorithm in Internet of Vehicles Discriminating Services

Guan

Wang

2022

Applied Sciences

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With the rapid development of global automotive industry intelligence and networking, the Internet of Vehicles (IoV) service, as a key communication technology, has been faced with an increasing spectrum of resources shortage. In this paper, we consider a spectrum utilization problem, in which a number of co-existing cellular users (CUs) and prioritized device-to-device (D2D) users are equipped in a single antenna vehicle-mounted communication network. To ensure a business-aware spectrum access mechanism with delay granted in a complex dynamic environment, we consider optimizing a metric that maintains a trade off between maximizing the total capacity of vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) links and minimizing the interference of high priority links. A low complexity priority-based spectrum allocation scheme based on the deep reinforcement learning method is developed to solve the proposed formulation. We trained our algorithm using the deep Q-learning network (DQN) over a set of public bandwidths. Simulation results show that the proposed scheme can allocate spectrum resources quickly and effectively in a high dynamic vehicle network environment. Concerning improved channel transmission rate, the V2V link rate in this scheme is 2.54 times that of the traditional random spectrum allocation scheme, and the V2I link rate is 13.5% higher than that of the traditional random spectrum allocation scheme. The average total interference received by priority links decreased by 14.2 dB compared to common links, realized service priority distinction and has good robustness to communication noise.

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Research on a Task Offloading Strategy for the Internet of Vehicles Based on Reinforcement Learning

Cited by 10 publications

References 33 publications

Automatic Path Planning Offloading Mechanism in Edge-Enabled Environments

Automatic Path Planning Offloading Mechanism in Edge-Enabled Environments

Dynamic Task Offloading for Cloud-Assisted Vehicular Edge Computing Networks: A Non-Cooperative Game Theoretic Approach

Deep Reinforcement Learning-Based Spectrum Allocation Algorithm in Internet of Vehicles Discriminating Services

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