Real-time task processing method based on edge computing for spinning CPS

Shi-yong, Yin; Bao, Jinsong; Li, Jie; Zhang, Jie

doi:10.1007/s11465-019-0542-1

Cited by 18 publications

(5 citation statements)

References 29 publications

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“…Unfortunately, deploying ML in an IoT system faces challenges due to constraints of the IoT system. For example, if ML is implemented in the cloud, real-time local decisionmaking [67] is almost impossible to achieve due to underlying limited bandwidth connectivity between sensing nodes and the cloud. To address the problem, ML can be deployed on the device.…”

Section: Edge Computing Based and Machine Learning Enabled Approachesmentioning

confidence: 99%

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

Hafeez

McArdle

2021

IEEE Access

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The use of IoT has become pervasive and IoT devices are common in many domains. Industrial IoT (IIoT) utilises IoT devices and sensors to monitor machines and environments to ensure optimal performance of equipment and processes. Predictive Maintenance (PM) which monitors the health of machines to determine the probable failure of components is one IIoT technique which is receiving attention lately. To achieve effective PM, massive amounts of data are collected, processed and ultimately analysed by Machine Learning (ML) algorithms. Traditionally IoT sensors transmit their data readings to the cloud for processing and modelling. Handling and transmitting massive amounts of data between IoT devices and infrastructure has a cost. Edge Computing (EC) in which both sensors and intermediate nodes can process data provides opportunities to reduce data transmission costs and increase processing speed. This article examines IIoT for PM and discusses how and where data can be processed and analysed. Initially, this article presents sampling and data reduction techniques. These techniques allow for a reduction in the amount of data transmitted to the cloud for processing but there are potential accuracy trade-offs when ML algorithms utilise reduced datasets. An alternative approach is to move ML algorithms closer to the data to reduce data transmission. There are three main techniques that utilise the EC paradigm to perform ML and data processing on intermediary nodes. These techniques are categorized according to where data processing occurs: Device and Edge, Edge and Cloud and Device and Cloud (Federated Learning). In addition to exploring traditional approaches, these three state-of-the-art techniques are examined in this article and their benefits and weaknesses are presented. A novel architecture to demonstrate how EC can be utilized both for data reduction and PM in IIoT is also proposed.

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Section: Edge Computing Based and Machine Learning Enabled Approachesmentioning

confidence: 99%

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

Hafeez

McArdle

2021

IEEE Access

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“…Yin et al [28] designed a real-time task processing method using the collaboration of edge computing and CPS. High speed processing and response are the design objective of this collaborative architecture where task and control flow between the communication and CPS layers are handled independently.…”

Section: Related Workmentioning

confidence: 99%

A Response-Aware Traffic Offloading Scheme Using Regression Machine Learning for User-Centric Large-Scale Internet of Things

Manogaran

Srivastava

Muthu³

et al. 2021

IEEE Internet Things J.

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Resource allocation and management in an Internet of Things (IoT) paradigm requires precise request and response processing irrespective of its scalability support. Unpredictable traffic pattern and user density demands reliable offloading for handling user request traffic and service response. Considering the need for large-scale IoT in account of its interoperability and heterogeneous support, this manuscript introduces responseaware traffic offloading scheme (RTOS) for delay-sensitive user requests. This offloading scheme is supported by multivariate spline regression machine learning model for classifying traffic for reducing the failure rate. The splines are adaptive based on the classified traffic for performing independent and shared offloading. The computation process for determining the offloading model is inherited from the cyber physical system (CPS) coupled with the IoT-Cloud architecture. The information from the knowledge base and event logs are exploited for decisionmaking in employing the offloading method for the classified traffic. The simulation analysis of this scheme shows that it is effective in improving the request processing ratio and reducing processing, response time, and delay. The simulation is performed for the varying user density and traffic flows.

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“…In 5G communication technology, vision is the primary application for edge computing. Certain researchers have reported that edge computing can be used for continuous manufacturing processes such as weaving in fiber production and textile manufacturing [44,45].…”

Section: Use Of Edge Computingmentioning

confidence: 99%

Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process

2021

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Industry 4.0 has remarkably transformed many industries. Supervisory control and data acquisition (SCADA) architecture is important to enable an intelligent and connected manufacturing factory. SCADA is extensively used in many Internet of Things (IoT) applications, including data analytics and data visualization. Product quality management is important across most manufacturing industries. In this study, we extensively used SCADA to develop a cloud-based analytics module for production quality predictive maintenance (PdM) in Industry 4.0, thus targeting textile manufacturing processes. The proposed module incorporates a complete knowledge discovery in database process. Machine learning algorithms were employed to analyze preprocessed data and provide predictive suggestions for production quality management. Equipment data were analyzed using the proposed system with an average mean-squared error of ~0.0005. The trained module was implemented as an application programming interface for use in IoT applications and third-party systems. This study provides a basis for improving production quality by predicting optimized equipment settings in manufacturing processes in the textile industry.

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Real-time task processing method based on edge computing for spinning CPS

Cited by 18 publications

References 29 publications

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

A Response-Aware Traffic Offloading Scheme Using Regression Machine Learning for User-Centric Large-Scale Internet of Things

Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process

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