Collaborative optimization of Edge-Cloud Computation Offloading in Internet of Vehicles

Li, Yureng; Xu, Sheng

doi:10.1109/icccn52240.2021.9522252

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Aiming at optimizing the time delay in the unloading process, some researchers use queuing theory, game theory, particle swarm optimization, deep learning algorithm, and other methods to build a computational task ofoad model [11][12][13][14]. Other studies focus on reducing energy consumption or the dual optimization of energy consumption and time delay [15][16][17][18][19]. Te reliability and use of computing resources have received the attention from many researchers [8,[20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Joint Task Offloading and Resource Allocation in Vehicular Edge Computing Networks for Emergency Logistics

Ling

Wang

et al. 2023

Mathematical Problems in Engineering

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As a special form of multiaccess edge computing (MEC), vehicular edge computing (VEC) plays an important role in emergency logistics by providing real-time and low-latency services for vehicles. The solution of the joint task offloading and resource allocation problem (JTORA) is the key to improving VEC efficiency. This study formulates a special model according to the multistage characteristics of the computational task in vehicular edge computing networks (VECNs) for emergency logistics. First, the JTORA problem is decomposed into three computational steps, each of which includes a task offload (TO) problem and a resource allocation (RA) problem. Then, a hybrid solution is proposed which uses a simulated annealing process to optimize the genetic algorithm (GA) and cooperate with the particle swarm optimization (PSO) algorithm, called the genetic simulated annealing and particle swarm optimization (GSA-PSO) algorithm. Furthermore, a simulation experiment is designed and the effectiveness of the GSA-PSO is verified.

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Section: Introductionmentioning

confidence: 99%

Joint Task Offloading and Resource Allocation in Vehicular Edge Computing Networks for Emergency Logistics

Ling

Wang

et al. 2023

Mathematical Problems in Engineering

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“…How to use limited computational resources to meet the explosive growth of computational demand is one of the key issues to be studied in the internet of vehicles (IoV), which is also investigated in this paper. In response to the shortage of computational resources in IoV, many studies have addressed cloud computing as a solution to deal with computationintensive services [4]. Utilizing cloud computing and cloud storage resources can greatly enhance the computing and storage capabilities of IoV, but it also brings larger latency, higher cost and energy consumption [5], [6].…”

mentioning

confidence: 99%

Federated Double Deep Q-Learning-Based Computation Offloading in Mobility-Aware Vehicle Clusters

Ye,

Zheng,

Wang

et al. 2023

IEEE Access

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On the edge side of internet of vehicles (IoV), mobile edge computing (MEC) servers with certain computational resources are deployed to provide computational service for vehicles. However, with the generation of a series of vehicle computing intensive and delay sensitive applications such as virtual reality (VR) navigation and vehicle-mounted games, the quality of service (QoS) for vehicle applications cannot be guaranteed with the limited computational resources in MECs. In this article, adjacent vehicles in IoV are converged into clusters based on topological stability. The idle computational resources of vehicles within the cluster are integrated and scheduled uniformly to serve vehicles within the cluster with computation task requirements. This reduces the dynamic changes in idle computational resources caused by rapid vehicle movement, and effectively ensured reliable feedback of computing results after offloading computing tasks. In order to reduce the task completion time, the task is decomposed into multiple subtasks with time dependencies, which can be processed in parallel. A directed acyclic graph (DAG) is used to characterize the task model. To further minimize latency in completing computing tasks, a federated double deep Q network-based computation task offloading (FDTO) strategy is proposed for vehicle clusters. The optimal computation task offloading decision is obtained based on the collaborative training and updating of double deep Q network (DDQN). Unlike traditional machine learning algorithms that require centralized training, federated learning (FL) allows only the training model parameters to be passed between vehicles, which not only protects data privacy but also reduces communication overhead. Simulation results show that the proposed strategy can effectively reduce task completion latency compared to the benchmark strategies. INDEX TERMS Internet of vehicles, directed acyclic graph, DDQN, federated learning.

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Video data offloading techniques in Mobile Edge Computing: A survey

Ma,

Ji,

et al. 2024

Physical Communication

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Collaborative optimization of Edge-Cloud Computation Offloading in Internet of Vehicles

Cited by 6 publications

References 17 publications

Joint Task Offloading and Resource Allocation in Vehicular Edge Computing Networks for Emergency Logistics

Joint Task Offloading and Resource Allocation in Vehicular Edge Computing Networks for Emergency Logistics

Federated Double Deep Q-Learning-Based Computation Offloading in Mobility-Aware Vehicle Clusters

Video data offloading techniques in Mobile Edge Computing: A survey

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