Integration of Blockchain, IoT and Machine Learning for Multistage Quality Control and Enhancing Security in Smart Manufacturing

Shahbazi, Zeinab; Byun, Yung-Cheol

doi:10.3390/s21041467

Cited by 104 publications

(61 citation statements)

References 67 publications

(43 reference statements)

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“…There has been a recent study to evaluate multi-level quality control based on various machine learning and blockchain-based solutions [30]. The authors found that XGBoost performs well by comparing the accuracy, precision, and recall of XGBoost and KNN algorithms.…”

Section: Machine Learningmentioning

confidence: 99%

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

et al. 2021

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With sustainable growth highlighted as a key to success in Industry 4.0, manufacturing companies attempt to optimize production efficiency. In this study, we investigated whether machine learning has explanatory power for quality prediction problems in the injection molding industry. One concern in the injection molding industry is how to predict, and what affects, the quality of the molding products. While this is a large concern, prior studies have not yet examined such issues especially using machine learning techniques. The objective of this article, therefore, is to utilize several machine learning algorithms to test and compare their performances in quality prediction. Using several machine learning algorithms such as tree-based algorithms, regression-based algorithms, and autoencoder, we confirmed that machine learning models capture the complex relationship and that autoencoder outperforms comparing accuracy, precision, recall, and F1-score. Feature importance tests also revealed that temperature and time are influential factors that affect the quality. These findings have strong implications for enhancing sustainability in the injection molding industry. Sustainable management in Industry 4.0 requires adapting artificial intelligence techniques. In this manner, this article may be helpful for businesses that are considering the significance of machine learning algorithms in their manufacturing processes.

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Section: Machine Learningmentioning

confidence: 99%

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

et al. 2021

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“…This section presents a detailed explanation of the existing studies in smart home security based on IoT [25][26][27][28][29][30][31], blockchain, and machine learning [10,[32][33][34][35][36][37][38][39][40]. Due to the flexibility of the blockchain framework, a smart home ecosystem can shape easily.…”

Section: Related Workmentioning

confidence: 99%

Smart Home Gateway Based on Integration of Deep Reinforcement Learning and Blockchain Framework

2021

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The development of information and communication technology in terms of sensor technologies cause the Internet of Things (IoT) step toward smart homes for prevalent sensing and management of resources. The gateway connections contain various IoT devices in smart homes representing the security based on the centralized structure. To address the security purposes in this system, the blockchain framework is considered a smart home gateway to overcome the possible attacks and apply Deep Reinforcement Learning (DRL). The proposed blockchain-based smart home approach carefully evaluated the reliability and security in terms of accessibility, privacy, and integrity. To overcome traditional centralized architecture, blockchain is employed in the data store and exchange blocks. The data integrity inside and outside of the smart home cause the ability of network members to authenticate. The presented network implemented in the Ethereum blockchain, and the measurements are in terms of security, response time, and accuracy. The experimental results show that the proposed solution contains a better outperform than recent existing works. DRL is a learning-based algorithm which has the most effective aspects of the proposed approach to improve the performance of system based on the right values and combining with blockchain in terms of security of smart home based on the smart devices to overcome sharing and hacking the privacy. We have compared our proposed system with the other state-of-the-art and test this system in two types of datasets as NSL-KDD and KDD-CUP-99. DRL with an accuracy of 96.9% performs higher and has a stronger output compared with Artificial Neural Networks with an accuracy of 80.05% in the second stage, which contains 16% differences in terms of improving the accuracy of smart homes.

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“…However, security is the biggest issue in application partitioning when it runs on different computing nodes and shares data between connected computing nodes for execution [4]. Many security schemes have been suggested in MECCA to support application data in the network [5]. For instance, RSA, CBC, SHA-256, and MD5 are based on both symmetric and asymmetric schemes.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, RSA, CBC, SHA-256, and MD5 are based on both symmetric and asymmetric schemes. Many studies [5][6][7][8][9][10] have adopted these methods in MECCA for the application-partitioning problem in a distributed network. The primary goal of this study was to undertake encryption and decryption on the local machine to ensure the security of data before offloading to the edge-cloud network.…”

Section: Introductionmentioning

confidence: 99%

“…Dynamic application partitioning, which involves separating mobile application runtimes between local execution and remote-cloud execution to optimize total cost, is a vital component of the offloading system [5]. Edge-cloud data centers are a subset of remotecloud data centers and have fewer computational capabilities, lower processing speed, and less memory capacity than remote-cloud data centers [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Application Partitioning and Task-Scheduling Secure Schemes for Biosensor Healthcare Workload in Mobile Edge Cloud

Lakhan

Grønli

et al. 2021

Electronics

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Currently, the use of biosensor-enabled mobile healthcare workflow applications in mobile edge-cloud-enabled systems is increasing progressively. These applications are heavyweight and divided between a thin client mobile device and a thick server edge cloud for execution. Application partitioning is a mechanism in which applications are divided based on resource and energy parameters. However, existing application-partitioning schemes widely ignore security aspects for healthcare applications. This study devises a dynamic application-partitioning workload task-scheduling-secure (DAPWTS) algorithm framework that consists of different schemes, such as min-cut algorithm, searching node, energy-enabled scheduling, failure scheduling, and security schemes. The goal is to minimize the energy consumption of nodes and divide the application between local nodes and edge nodes by applying the secure min-cut algorithm. Furthermore, the study devises the secure-min-cut algorithm, which aims to migrate data between nodes in a secure form during application partitioning in the system. After partitioning the applications, the node-search algorithm searches optimally to run applications under their deadlines. The energy and failure schemes maintain the energy consumption of the nodes and the failure of the system. Simulation results show that DAPWTS outperforms existing baseline approaches by 30% in terms of energy consumption, deadline, and failure of applications in the system.

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Integration of Blockchain, IoT and Machine Learning for Multistage Quality Control and Enhancing Security in Smart Manufacturing

Cited by 104 publications

References 67 publications

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

Smart Home Gateway Based on Integration of Deep Reinforcement Learning and Blockchain Framework

Dynamic Application Partitioning and Task-Scheduling Secure Schemes for Biosensor Healthcare Workload in Mobile Edge Cloud

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