Data classification processing method for the Power IoT based on cloud-side collaborative architecture

Fan, Chunlei; Lu, Yuan; Leng, Xiaojie; Luan, Weiping; Gu, Jiande; Yang, Wei

doi:10.1109/itaic49862.2020.9339138

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…However, the NSGA-III algorithm also has certain limitations. Due to the high randomness in the selection of initial reference points, the initial state of the population will not be excellent (13)…”

Section: Problem Modelingmentioning

confidence: 99%

“…Edge computing has led to the development of a more advantageous cloud-edge collaborative architecture. If edge node has computing resources far from being sufficient to complete the task offloaded by the end-user, it will further offload the task to the cloud [13], which avoids the extensive transmission delay caused by uploading tasks from some end users far from the cloud and enables real-time interaction. Combining the advantages of cloud computing and edge computing, cloud-edge collaborative computing makes it more efficient to process massive computing tasks, quicker to execute real-time tasks and more effective to handle delay-sensitive tasks [14].…”

Section: Introductionmentioning

confidence: 99%

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A cloud-edge collaborative task scheduling method based on model segmentation

Zhang,

Chen,

et al. 2024

J Cloud Comp

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With the continuous development and combined application of cloud computing and artificial intelligence, some new methods have emerged to reduce task execution time for training neural network models in a cloud-edge collaborative environment. The most attractive method is neural network model segmentation. However, many factors affect the segmentation point, such as resource allocation, system energy consumption, load balancing, and network Bandwidth allocation. Some segmentation methods consider the shortest task execution time, which ignores the utilization of resources at the edge and can result in resource waste. Additionally, these factors are difficult to measure, which presents a challenge in calculating the best segmentation point to achieve the goal of maximum resource utilization and minimum task execution time. To solve this problem, this paper proposes a cloud-edge collaborative task scheduling method based on model segmentation (CECMS). This method first analyzes the factors affecting the segmentation point of the model and then obtains accurate factors that affect the segmentation point calculation through the pre-execution method. Furthermore, a multi-objective solution algorithm is improved to calculate the optimal model segmentation point. And tasks are separately offloaded to the edge and cloud based on the optimal model segmentation point. Finally, the experiments are conducted to verify the effectiveness of this method. Finally, the effectiveness of the CECMS method was verified through simulation experiments. Compared with the Dynamic Adaptive DNN Surgery (DADS) method and an adaptive DNN inference acceleration framework algorithm with end–edge–cloud collaborative computing algorithm (ADC), CECMS achieves the same effectiveness as DADS and ADC in optimizing task execution time by comprehensively considering the utilization of edge resources and minimizing task execution time, while also effectively ensuring resource utilization.

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Section: Problem Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A cloud-edge collaborative task scheduling method based on model segmentation

Zhang,

Chen,

et al. 2024

J Cloud Comp

View full text Add to dashboard Cite

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Cloud-Edge Collaboration Based Data Mining for Power Distribution Networks

2022

Communications and Networking

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Real‐time fire and smoke detection with transfer learning based on cloud‐edge collaborative architecture

Yang,

Qian,

2024

IET Image Processing

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Recent years have seen increased interest in object detection‐based applications for fire detection in digital images and videos from edge devices. The environment's complexity and variability often lead to interference from factors such as fire and smoke characteristics, background noise, and camera settings like angle, sharpness, and exposure, which hampers the effectiveness of fire detection applications. Limited picture data for fire and smoke scenes further challenges model accuracy and robustness, resulting in high false detection and leakage rates. To address the need for efficient detection and adaptability to various environments, this paper focuses on (1) proposing a cloud‐edge collaborative architecture for real‐time fire and smoke detection, incorporating an iterative transfer learning strategy based on user feedback to enhance adaptability; (2) improving the detection capabilities of the base model YOLOv8 by enhancing the data augmentation method and introducing the coordinate attention mechanism to improve global feature extraction. The improved algorithm shows a 2‐point accuracy increase. After three iterations of transfer learning in the production environment, accuracy improves from 93.3% to 96.4%, and mAP0.5:0.95 increases by nearly 5 points. This program effectively addresses false detection issues in fire and smoke detection systems, demonstrating practical applicability.

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Data classification processing method for the Power IoT based on cloud-side collaborative architecture

Cited by 7 publications

References 13 publications

A cloud-edge collaborative task scheduling method based on model segmentation

A cloud-edge collaborative task scheduling method based on model segmentation

Cloud-Edge Collaboration Based Data Mining for Power Distribution Networks

Real‐time fire and smoke detection with transfer learning based on cloud‐edge collaborative architecture

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