scholarly journals A Robust Predicted Performance Analysis Approach for Data-Driven Product Development in the Industrial Internet of Things

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2871 ◽  
Author(s):  
Hao Zheng ◽  
Yixiong Feng ◽  
Yicong Gao ◽  
Jianrong Tan
Keyword(s):  
Performance Analysis ◽  
Internet Of Things ◽  
Product Development ◽  
Data Driven ◽  
Analysis Approach ◽  
Design Solution ◽  
Support Vector ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Industrial Iot

Industrial Internet of Things (IoT) is a ubiquitous network integrating various sensing technologies and communication technologies to provide intelligent information processing and smart control abilities for the manufacturing enterprises. The aim of applying industrial IoT is to assist manufacturers manage and optimize the entire product manufacturing process to improve product quality and production efficiency. Data-driven product development is considered as one of the critical application scenarios of industrial IoT, which is used to acquire the satisfied and robust design solution according to customer demands. Performance analysis is an effective tool to identify whether the key performance have reached the requirements in data-driven product development. The existing performance analysis approaches mainly focus on the metamodel construction, however, the uncertainty and complexity in product development process are rarely considered. In response, this paper investigates a robust performance analysis approach in industrial IoT environment to help product developers forecast the performance parameters accurately. The service-oriented layered architecture of industrial IoT for product development is first described. Then a dimension reduction approach based on mutual information (MI) and outlier detection is proposed. A metamodel based on least squares support vector regression (LSSVR) is established to conduct performance prediction process. Furthermore, the predicted performance analysis method based on confidence interval estimation is developed to deal with the uncertainty to improve the robustness of the forecasting results. Finally, a case study is given to show the feasibility and effectiveness of the proposed approach.

scholarly journals Special Issue on New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes

2019 ◽  
Vol 9 (20) ◽  
pp. 4323 ◽  
Author(s):  
López de Lacalle ◽  
Posada
Keyword(s):  
Internet Of Things ◽  
Industry 4.0 ◽  
Research Community ◽  
Manufacturing Processes ◽  
Special Issue ◽  
Industrial Internet Of Things ◽  
Visual Computing ◽  
Industrial Internet ◽  
Industrial Iot ◽  
New Industry

The new advances of IIOT (Industrial Internet of Things), together with the progress in visual computing technologies, are being addressed by the research community with interesting approaches and results in the Industry 4.0 domain[...]

scholarly journals QoS-Driven Adaptive Trust Service Coordination in the Industrial Internet of Things

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2449
Author(s):  
Jin Qi ◽  
Zian Wang ◽  
Bin Xu ◽  
Mengfei Wu ◽  
Zian Gao ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Search Space ◽  
Gray Wolf ◽  
Service Coordination ◽  
Trust Evaluation ◽  
Industrial Internet Of Things ◽  
Industrial Services ◽  
Coordination Model ◽  
Industrial Internet ◽  
Industrial Iot

The adaptive coordination of trust services can provide highly dependable and personalized solutions for industrial requirements in the service-oriented industrial internet of things (IIoT) architecture to achieve efficient utilization of service resources. Although great progress has been made, trust service coordination still faces challenging problems such as trustless industry service, poor coordination, and quality of service (QoS) personalized demand. In this paper, we propose a QoS-driven and adaptive trust service coordination method to implement Pareto-efficient allocation of limited industrial service resources in the background of the IIoT. First, we established a Pareto-effective and adaptive industrial IoT trust service coordination model and introduced a blockchain-based adaptive trust evaluation mechanism to achieve trust evaluation of industrial services. Then, taking advantage of a large and complex search space for solution efficiency, we introduced and compared multi-objective gray-wolf algorithms with the particle swarm optimization (PSO) and dragonfly algorithms. The experimental results showed that by judging and blacklisting malicious raters quickly and accurately, our model can efficiently realize self-adaptive, personalized, and intelligent trust service coordination under the given constraints, improving not only the response time, but also the success rate in coordination.

scholarly journals Visibility of resources and assets in the shipyard through industrial internet of things

2021 ◽  
Vol 11 (2) ◽  
pp. 88-101
Author(s):  
Ibrahim Cil ◽  
Fahri Arisoy ◽  
Hilal Kilinc
Keyword(s):  
Internet Of Things ◽  
Transformation Process ◽  
Digital Transformation ◽  
Sensor Data ◽  
Cyber Physical System ◽  
Industrial Internet Of Things ◽  
Use Of Data ◽  
Industrial Internet ◽  
Industrial Iot ◽  
Other Information

Industrial Internet of Things is becoming one of the fundamental technologies with the potential to be widely used in shipyards as in other industries to increase information visibility. This article aims to analyze how to develop an industrial IoT-enabled system that provides visibility and tracking of assets at SEDEF Shipyard, which is in the digital transformation process. The research made use of data from previous studies and by using content analysis, the findings were discussed. Industrial IoT enables the collection and analysis of data for more informed decisions.  Based on the findings, sensor data in the shipyard are transmitted to the cloud via connected networks. These data are analysed and combined with other information and presented to the stakeholders. Industrial IoT enables this data flow and monitors processes remotely and gives the ability to quickly change plans as needed. Keywords: Shipyard, Industrial Internet of Things, Cyber-Physical System, Visibility, Assets tracking;        

scholarly journals Failure Prediction Model Using Iterative Feature Selection for Industrial Internet of Things

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 454 ◽  
Author(s):  
Jung-Hyok Kwon ◽  
Eui-Jik Kim
Keyword(s):  
Feature Selection ◽  
Internet Of Things ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Model Building ◽  
Failure Prediction ◽  
Support Vector ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
New Feature

This paper presents a failure prediction model using iterative feature selection, which aims to accurately predict the failure occurrences in industrial Internet of Things (IIoT) environments. In general, vast amounts of data are collected from various sensors in an IIoT environment, and they are analyzed to prevent failures by predicting their occurrence. However, the collected data may include data irrelevant to failures and thereby decrease the prediction accuracy. To address this problem, we propose a failure prediction model using iterative feature selection. To build the model, the relevancy between each feature (i.e., each sensor) and the failure was analyzed using the random forest algorithm, to obtain the importance of the features. Then, feature selection and model building were conducted iteratively. In each iteration, a new feature was selected considering the importance and added to the selected feature set. The failure prediction model was built for each iteration via the support vector machine (SVM). Finally, the failure prediction model having the highest prediction accuracy was selected. The experimental implementation was conducted using open-source R. The results showed that the proposed failure prediction model achieved high prediction accuracy.

scholarly journals Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6241
Author(s):  
Israel Campero-Jurado ◽  
Sergio Márquez-Sánchez ◽  
Juan Quintanar-Gómez ◽  
Sara Rodríguez ◽  
Juan M. Corchado
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Internet Of Things ◽  
Information And Communication Technologies ◽  
Health And Safety ◽  
Working Environment ◽  
Training Dataset ◽  
Support Vector ◽  
Industrial Internet Of Things ◽  
Industrial Internet

Information and communication technologies (ICTs) have contributed to advances in Occupational Health and Safety, improving the security of workers. The use of Personal Protective Equipment (PPE) based on ICTs reduces the risk of accidents in the workplace, thanks to the capacity of the equipment to make decisions on the basis of environmental factors. Paradigms such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) make it possible to generate PPE models feasibly and create devices with more advanced characteristics such as monitoring, sensing the environment and risk detection between others. The working environment is monitored continuously by these models and they notify the employees and their supervisors of any anomalies and threats. This paper presents a smart helmet prototype that monitors the conditions in the workers’ environment and performs a near real-time evaluation of risks. The data collected by sensors is sent to an AI-driven platform for analysis. The training dataset consisted of 11,755 samples and 12 different scenarios. As part of this research, a comparative study of the state-of-the-art models of supervised learning is carried out. Moreover, the use of a Deep Convolutional Neural Network (ConvNet/CNN) is proposed for the detection of possible occupational risks. The data are processed to make them suitable for the CNN and the results are compared against a Static Neural Network (NN), Naive Bayes Classifier (NB) and Support Vector Machine (SVM), where the CNN had an accuracy of 92.05% in cross-validation.

scholarly journals Reliability Evaluation Model Of Industrial Internet Of Things Systems

2020 ◽  
Vol 1 (1) ◽  
pp. 22-24
Author(s):  
Dong-Seong Kim
Keyword(s):  
Internet Of Things ◽  
Performance Indicator ◽  
Evaluation Model ◽  
Industrial Internet Of Things ◽  
Research Issues ◽  
Industrial Systems ◽  
Performance Accuracy ◽  
Industrial Internet ◽  
Industrial Iot ◽  
Robust Systems

The Industrial Internet of Things (IIoT) allows digitizing manufacturing processes and increasing the digital connectivity of smart factory and industrial systems. The reliability of a system is considered as a key performance indicator that defines how accurately and perfectly the system works. Ensuring reliability in industrial IoT exposes several challenges as well as promising opportunities for advancing technologies and systematic designs such as algorithms, architectures, and devices. It depends on several factors, for example, ensuring performance, accuracy, stability, and availability. This article provides a systematic model for evaluating the reliability of IIoT systems. This model enables elucidate several open research issues regarding designing the reliable and robust systems.

scholarly journals Design of the Wireless Network Hierarchy System of Intelligent City Industrial Data Management Based on SDN Network Architecture

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wenken Tan ◽  
Jianmin Hu
Keyword(s):  
Internet Of Things ◽  
Access Control ◽  
Data Management ◽  
Wireless Network ◽  
Smart City ◽  
Network Architecture ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Industrial Iot ◽  
Intelligent City

With the rapid development of the industrial Internet of Things and the comprehensive popularization of mobile intelligent devices, the construction of smart city and economic development of wireless network demand are increasingly high. SDN has the advantages of control separation, programmable interface, and centralized control logic. Therefore, integrating this technical concept into the smart city data management WLAN network not only can effectively solve the problems existing in the previous wireless network operation but also provide more functions according to different user needs. In this case, the traditional WLAN network is of low cost and is simple to operate, but it cannot guarantee network compatibility and performance. From a practical perspective, further network compatibility and security are a key part of industrial IoT applications. This paper designs the network architecture of smart city industrial IoT based on SDN, summarizes the access control requirements and research status of industrial IoT, and puts forward the access control requirements and objectives of industrial IoT based on SDN. The characteristics of the industrial Internet of Things are regularly associated with data resources. In the framework of SDN industrial Internet of Things, gateway protocol is simplified and topology discovery algorithm is designed. The access control policy is configured on the gateway. The access control rule can be dynamically adjusted in real time. An SDN-based intelligent city industrial Internet of Things access control function test platform was built, and the system was simulated. The proposed method is compared with other methods in terms of extension protocol and channel allocation algorithm. Experimental results verify the feasibility of the proposed scheme. Finally, on the basis of performance analysis, the practical significance of the design of a smart city wireless network hierarchical data management system based on SDN industrial Internet of Things architecture is expounded.

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