Big data-driven scheduling optimization algorithm for Cyber–Physical Systems based on a cloud platform

Niu, Chao; Wang, Lizhou

doi:10.1016/j.comcom.2021.10.020

Cited by 16 publications

(4 citation statements)

References 28 publications

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“…Utilizing cloud computing for distributed processing has been employed to tackle the intensive computational requirements and resource limitations of deep learning models. Nonetheless, cloud computing encounters challenges like restricted data transfer bandwidth and substantial latency when dealing with substantial multimedia data [40,41]. To counteract these challenges, edge computing is emerging as a viable solution.…”

Section: Mobile and Cloud Computing Techniquesmentioning

confidence: 99%

Revolutionizing Coffee Farming: A Mobile App with GPS-Enabled Reporting for Rapid and Accurate On-Site Detection of Coffee Leaf Diseases Using Integrated Deep Learning

Hitimana,

Kuradusenge,

Sinayobye

et al. 2024

Software

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Coffee leaf diseases are a significant challenge for coffee cultivation. They can reduce yields, impact bean quality, and necessitate costly disease management efforts. Manual monitoring is labor-intensive and time-consuming. This research introduces a pioneering mobile application equipped with global positioning system (GPS)-enabled reporting capabilities for on-site coffee leaf disease detection. The application integrates advanced deep learning (DL) techniques to empower farmers and agronomists with a rapid and accurate tool for identifying and managing coffee plant health. Leveraging the ubiquity of mobile devices, the app enables users to capture high-resolution images of coffee leaves directly in the field. These images are then processed in real-time using a pre-trained DL model optimized for efficient disease classification. Five models, Xception, ResNet50, Inception-v3, VGG16, and DenseNet, were experimented with on the dataset. All models showed promising performance; however, DenseNet proved to have high scores on all four-leaf classes with a training accuracy of 99.57%. The inclusion of GPS functionality allows precise geotagging of each captured image, providing valuable location-specific information. Through extensive experimentation and validation, the app demonstrates impressive accuracy rates in disease classification. The results indicate the potential of this technology to revolutionize coffee farming practices, leading to improved crop yield and overall plant health.

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Section: Mobile and Cloud Computing Techniquesmentioning

confidence: 99%

Revolutionizing Coffee Farming: A Mobile App with GPS-Enabled Reporting for Rapid and Accurate On-Site Detection of Coffee Leaf Diseases Using Integrated Deep Learning

Hitimana,

Kuradusenge,

Sinayobye

et al. 2024

Software

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show abstract

“…Then comprehensively consider cloud platform service, business and target collaboration and coordination mechanism, and build a cloud platform-based service value chain collaboration framework. Niu et al (2022) proposed a large-scale factory access task scheduling scheme under cloud-edge collaborative computing architecture by studying big data-driven space physics system, and realized big datadriven scheduling optimization of the space physics system based on cloud platform algorithm. Zhu et al (2021) studies real estate virtual e-commerce model based on big data technology.…”

Section: Intelligent Collection and Scheduling Inmentioning

confidence: 99%

A survey of intelligent load monitoring in IoT-enabled distributed smart grids

Gan

Zeng

Liu

et al. 2023

IJAHUC

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Power Load Monitoring has been a research hotspot since a few years ago. With development of artificial intelligence, construction of smart grid has become the most important part of power load monitoring. At the same time, task scheduling mechanism combined with the distributed IoT(Internet of Things) improves efficiency of smart grid. In this paper, applications of cloud/edge platform in the data acquisition, processing and scheduling of the IoT is introduced step by step, as well as applications and differences of artificial intelligence algorithm in each step, including Data Acquisition, Load Disaggregation, Load Forecasting and so on. Finally, combined with various optimization methods, future research directions are prospected, including data and network security issues, challenges faced by cloud/edge architecture, adaptive fine-grained Load Disaggregation, Load Forecasting.

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“…PID is a control algorithm integrating proportion, integral, and differential [ 28 , 29 ]. Also known as proportional integral differential control, the control schematic diagram of PID algorithm is obtained through analysis, as shown in Figure 7 .…”

Section: Monitoring and Control Principlementioning

confidence: 99%

A Sport Monitoring System Based on the Optimized Adaptive Fuzzy PID Control Algorithm in OneNet Internet of Things and Cloud Platform

Zhang

Min

2022

Computational Intelligence and Neuroscience

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Aiming at some problems existing in the existing sports monitoring system, based on the joint action of OneNet Internet of Things (IoT) and cloud platform, an optimized adaptive fuzzy PID control algorithm is adopted to monitor and analyze sports. Finally, the accuracy of the optimization model is verified through the comparison of different models, and the algorithm is used to predict and analyze sports. The research shows that (1) as the index calculation of cloud platform shows, with the increase of iteration time, the change curve of relevant indexes can be divided into four different stages, namely, rapid fluctuation stage, slow decline stage, slow fluctuation stage, and rapid decline stage. (2) The conventional calculation method (CPID) cannot well describe the change rule of the test data in the early stage of settlement. The fuzzy adaptive calculation method (NPID) also exposes some errors in the fitting and description of the test curve in the calculation process, while the improved adaptive calculation method (GPID) can describe the change characteristics and rules of the test curve well for different stages. (3) Compared with the original model, the optimization model can better describe the first and second stages of index change, indicating the accuracy of the optimization model. And the algorithm can be used to predict and analyze the changes of indicators and sports monitoring better, and the analysis results can provide relevant guidance for sports monitoring. This optimization scheme provides basis and theoretical support for the application of OneNet IoT and cloud platform.

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Big data-driven scheduling optimization algorithm for Cyber–Physical Systems based on a cloud platform

Cited by 16 publications

References 28 publications

Revolutionizing Coffee Farming: A Mobile App with GPS-Enabled Reporting for Rapid and Accurate On-Site Detection of Coffee Leaf Diseases Using Integrated Deep Learning

Revolutionizing Coffee Farming: A Mobile App with GPS-Enabled Reporting for Rapid and Accurate On-Site Detection of Coffee Leaf Diseases Using Integrated Deep Learning

A survey of intelligent load monitoring in IoT-enabled distributed smart grids

A Sport Monitoring System Based on the Optimized Adaptive Fuzzy PID Control Algorithm in OneNet Internet of Things and Cloud Platform

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