Edge Collaborative Online Task Offloading Method Based on Reinforcement Learning

Sun, Ming; Bao, Tie; Xie, Dan; Lv, Hengyi; Si, Guoliang

doi:10.3390/electronics12183741

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…MA Xue, WEN Chenglin [2] and others proposed an asynchronous quasi cloud/edge/client collaborative joint learning mechanism for fault diagnosis, established a new asynchronous quasi cloud/edge/client collaborative Federated learning mechanism, and verified the effectiveness of the algorithm through the data of rotating machinery. Sun Ming [3] proposed a new task unloading strategy based on Deep reinforcement learning CTOSDRL to solve the problem of task unloading for multi-user collaboration in the cloud.…”

Section: Literature Review 21 Development Of Cloud Edge Collaboration...mentioning

confidence: 99%

Fault Diagnosis of Transfer Learning Equipment Based on Cloud Edge Collaboration + Confrontation Network

2023

Teh. vjesn.

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With the continuous improvement of product quality, production efficiency, and complexity, higher requirements are put forward for the reliability and stability of equipment, and the difficulty of real-time diagnosis of faults and functional failures is also increasing. The traditional fault diagnosis methods based on signal processing and Convolutional neural network cannot meet the requirements of on-site online real-time fault diagnosis of equipment. One is that the vibration signals on the industrial site are superimposed on each other, nonlinear and unstable and traditional feature extraction methods take a long time, resulting in unstable extraction results. Second, massive data and fault diagnosis algorithms need rich computing and storage resources. The traditional Convolutional neural network method conflicts with the real-time response requirements of fault diagnosis. At the same time, different models of fault diagnosis models have poor generalization ability, and the diagnostic accuracy is not high or even impossible to diagnose. To solve the above problems, this paper proposes a fault diagnosis method based on industrial Internet platform, which is equipment cloud edge collaboration + adaptive countermeasure network Transfer learning. On the edge side, the vibration signals collected from key components of the model are processed using empirical mode decomposition (EEMD) to solve the problem of signal nonlinearity and stationarity. In the cloud, EEMD signals of different models are decomposed into source domain and target domain for confrontation training, which is used as the input of the improved domain adversarial network model DANN (Domain Adversarial Neural Networks), so as to improve the accuracy of fault diagnosis of different models by using cloud computing power and the improved adversarial network Transfer learning algorithm. Through the analysis of experimental data, this paper verifies that the model after the confrontation network Transfer learning is more accurate than the traditional fault diagnosis method. Through the coordination of computing resources and real-time requirements, real-time diagnosis of cloud side collaborative bearing fault is realized.

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Section: Literature Review 21 Development Of Cloud Edge Collaboration...mentioning

confidence: 99%

Fault Diagnosis of Transfer Learning Equipment Based on Cloud Edge Collaboration + Confrontation Network

2023

Teh. vjesn.

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“…With the rapid development of mobile communication, the number of devices and the amount of application data at the network edge have significantly increased [1][2][3]. However, traditional cloud computing architectures require centralized processing of all data, which cannot meet the requirements of low latency, high traffic volume, and high reliability in current wireless communications.…”

Section: Introductionmentioning

confidence: 99%

PPO-Based Joint Optimization for UAV-Assisted Edge Computing Networks

Liu,

Zhang,

2023

Applied Sciences

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In next-generation mobile communication scenarios, more and more user terminals (UEs) and edge computing servers (ECSs) are connected to the network. To ensure the experience of edge computing services, we designed an unmanned aerial vehicle (UAV)-assisted edge computing network application scenario. In the considered scenario, the UAV acts as a relay node to forward edge computing tasks when the performance of the wireless channel between UEs and ECSs degrades. In order to minimize the average delay of edge computing tasks, we design the optimization problem of joint UE–ECS matching and UAV three-dimensional hovering position deployment. Further, we transform this mixed integer nonlinear programming into a continuous-variable decision process and design the corresponding Proximal Policy Optimization (PPO)-based joint optimization algorithm. Sufficient data pertaining to latency demonstrate that the suggested algorithm can obtain a seamless reward value when the number of training steps hits three million. This verifies the algorithm’s desirable convergence property. Furthermore, the algorithm’s efficacy has been confirmed through simulation in various environments. The experimental findings ascertain that the PPO-based co-optimization algorithm consistently attains a lower average latency rate and a minimum of 8% reduction in comparison to the baseline scenarios.

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An energy-saving joint resource allocation strategy for mobile edge computing

Wei

2024

Physical Communication

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Edge Collaborative Online Task Offloading Method Based on Reinforcement Learning

Cited by 4 publications

References 26 publications

Fault Diagnosis of Transfer Learning Equipment Based on Cloud Edge Collaboration + Confrontation Network

Fault Diagnosis of Transfer Learning Equipment Based on Cloud Edge Collaboration + Confrontation Network

PPO-Based Joint Optimization for UAV-Assisted Edge Computing Networks

An energy-saving joint resource allocation strategy for mobile edge computing

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