scholarly journals Internet of Things Sensing Networks, Artificial Intelligence-based Decision-Making Algorithms, and Real-Time Process Monitoring in Sustainable Industry 4.0

2021 ◽  
Vol 9 (3) ◽  
pp. 35
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Internet Of Things ◽  
Real Time ◽  
Process Monitoring ◽  
Industry 4.0 ◽  
Time Process

scholarly journals Sustainable Industry 4.0 Wireless Networks, Machine Learning Algorithms, and Internet of Things-based Real-Time Production Logistics in Digital Twin-driven Smart Manufacturing

2021 ◽  
Vol 129 ◽  
pp. 04003
Author(s):  
Elvira Nica ◽  
Gheorghe H. Popescu ◽  
George Lăzăroiu
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Industry 4.0 ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Cyber Physical Production Systems

Research background: The aim of this paper is to synthesize and analyze existing evidence on artificial intelligence-based decision-making algorithms, industrial big data, and Internet of Things sensing networks in digital twin-driven smart manufacturing. Purpose of the article: Using and replicating data from Altair, Catapult, Deloitte, DHL, GAVS, PwC, and ZDNet we performed analyses and made estimates regarding cyber-physical system-based real-time monitoring, product decision-making information systems, and artificial intelligence data-driven Internet of Things systems in digital twin-based cyber-physical production systems. Methods: From the completed surveys, we calculated descriptive statistics of compiled data when appropriate. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. The precision of the online polls was measured using a Bayesian credibility interval. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing. Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. An Internet-based survey software program was utilized for the delivery and collection of responses. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau’s American Community Survey to reflect reliably and accurately the demographic composition of the United States. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Findings & Value added: The way Internet of Things-based decision support systems, artificial intelligence-driven big data analytics, and robotic wireless sensor networks configure digital twin-driven smart manufacturing and cyber-physical production systems in sustainable Industry 4.0.

Artificial intelligence based commuter behaviour profiling framework using Internet of things for real-time decision-making

2020 ◽  
Vol 32 (20) ◽  
pp. 16057-16071 ◽  
Author(s):  
Tharindu Bandaragoda ◽  
Achini Adikari ◽  
Rashmika Nawaratne ◽  
Dinithi Nallaperuma ◽  
Ashish Kr. Luhach ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Internet Of Things ◽  
Real Time

Leading Edge Assembly Real Time Process Monitoring Using Industrial Internet of Things (IIoT)

2019 ◽  
Author(s):  
Karol Janik ◽  
Ian Thompson ◽  
Gareth Walley ◽  
Patrick Land ◽  
Agata Suwala
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Process Monitoring ◽  
Leading Edge ◽  
Time Process ◽  
Industrial Internet Of Things ◽  
Industrial Internet
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