Internet of Things, Real-Time Decision Making, and Artificial Intelligence

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.

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Cognitive Decision-Making Algorithms, Real-Time Sensor Networks, and Internet of Things Smart Devices in Cyber-Physical Manufacturing Systems

Economics Management and Financial Markets ◽

10.22381/emfm15320204 ◽

2020 ◽

Vol 15 (3) ◽

pp. 30

Keyword(s):

Decision Making ◽

Sensor Networks ◽

Internet Of Things ◽

Real Time ◽

Manufacturing Systems ◽

Smart Devices ◽

Cognitive Decision

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BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i10.7197 ◽

2020 ◽

Vol 34 (10) ◽

pp. 13849-13850

Author(s):

Donghyeon Lee ◽

Man-Je Kim ◽

Chang Wook Ahn

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Real Time ◽

Large Scale ◽

Outcome Predictor ◽

Short Term ◽

Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

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Blockchain-Enabled Internet of Things for Unsupervised Structural Health Monitoring in Potential Building Structures

Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-6870-5.ch011 ◽

2021 ◽

pp. 158-170

Author(s):

Wael Mohammad Alenazy

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Structural Health Monitoring ◽

Internet Of Things ◽

Health Monitoring ◽

Remote Monitoring ◽

Building Structures ◽

Stress Strain ◽

Structural Health ◽

Automated Decision Making

The integration of internet of things, artificial intelligence, and blockchain enabled the monitoring of structural health with unattended and automated means. Remote monitoring mandates intelligent automated decision-making capability, which is still absent in present solutions. The proposed solution in this chapter contemplates the architecture of smart sensors, customized for individual structures, to regulate the monitoring of structural health through stress, strain, and bolted joints looseness. Long range sensors are deployed for transmitting the messages a longer distance than existing techniques. From the simulated results, different sensors record the monitoring information and transmit to the blockchain platform in terms of pressure points, temperature, pre-tension force, and the architecture deems the criticality of transactions. Blockchain platform will also be responsible for storage and accessibility of information from a decentralized medium, automation, and security.

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