Task Offloading Method of Internet of Vehicles Based on Cloud-Edge Computing

Sun, Yilong; Wu, Zhi‐Yong; Shi, Dayin; Hu, Xiuwei

doi:10.1109/scc55611.2022.00052

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Lin et al [21] proposed a data offloading strategy called PKMR, which considers a predicted k-hop count limit and utilizes VVR paths for data offloading with neighboring Rsus. Sun et al [22] introduced the PVTO method, which offloads V2V tasks to MEC and optimizes the strategy using GA, SAW, and MCDM. Ko et al [23] introduced the belief-based task offloading algorithm (BTOA), where vehicles make computation and communication decisions based on beliefs while observing the resources and channel conditions in the current VEC environment.…”

Section: Related Workmentioning

confidence: 99%

Traffic-Aware Optimization of Task Offloading and Content Caching in the Internet of Vehicles

Wang,

Qiao

et al. 2023

Applied Sciences

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Emerging in-vehicle applications seek to improve travel experiences, but the rising number of vehicles results in more computational tasks and redundant content requests, leading to resource waste. Efficient compute offloading and content caching strategies are crucial for the Internet of Vehicles (IoV) to optimize performance in time latency and energy consumption. This paper proposes a joint task offloading and content caching optimization method based on forecasting traffic streams, called TOCC. First, temporal and spatial correlations are extracted from the preprocessed dataset using the Forecasting Open Source Tool (FOST) and integrated to predict the traffic stream to obtain the number of tasks in the region at the next moment. To obtain a suitable joint optimization strategy for task offloading and content caching, the multi-objective problem of minimizing delay and energy consumption is decomposed into multiple single-objective problems using an improved Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) via the Tchebycheff weight aggregation method, and a set of Pareto-optimal solutions is obtained. Finally, the experimental results verify the effectiveness of the TOCC strategy. Compared with other methods, its latency is up to 29% higher and its energy consumption is up to 83% higher.

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Section: Related Workmentioning

confidence: 99%

Traffic-Aware Optimization of Task Offloading and Content Caching in the Internet of Vehicles

Wang,

Qiao

et al. 2023

Applied Sciences

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“…To address the computational demands of vehicle tasks, early research [4][5][6] offloaded computing tasks to remote clouds, utilizing the massive computing resources available in the cloud to satisfy vehicle task requirements. However, the long transmission delay caused by the distance between the remote cloud and vehicles cannot meet the low-latency requirements of specific tasks.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin-Assisted Vehicles Edge Network Computing Offloading Scheme

Zhu,

Tan,

2023

Preprint

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Traditional vehicle edge computing research has not fully considered the differences between vehicle tasks and edge server computing resources while often ignoring the use of deep reinforcement learning (DRL), which requires a large amount of training data, and DRL is easy to fall into the problem of local optimality. Therefore, this paper proposed a digital twin (DT) -assisted vehicle edge network computation offloading method. For the problem of DRL requiring a large amount of training data, a real-time data acquisition method based on DT is proposed for vehicle tasks and edge nodes. The differentiability of computing resources proposed a computing model based on deep neural network (DNN) partitioning and early exit mechanism, making full use of the computing resources of edge servers and vehicles, reducing the computation delay and transmission delay of tasks, Aiming at the problem that the asynchronous dominant actor-critic (A3C) algorithm in DRL is easy to fall into the local optimal solution, the dynamic baseline and ε-greedy policy are introduced based on A3C and used to select the channel to reduce the transmission delay further. Experimental results show that this scheme outperforms other comparative methods and can effectively reduce the delay of task offloading in vehicular edge computing.

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