A SELF-ORGANIZED MULTI-AGENT SYSTEM WITH INDUSTRY 4.0 COORDINATION AND BIG DATA BASED FEEDBACK
Keywords:
Industry 4.0, Smart Factory, Cyber-physical System, Multi-agent System, Deadlock PreventionAbstract
The widespread use of cyber-physical systems heralds the arrival of Industry 4.0, the next phase of industrialization. The drive for vertical factory integration is a defining feature of Industry 4.0, which aims to build ``smart factories`` capable of utilizing adaptable and dynamic production technology. Here we showcase a smart factory architecture that combines smart shop-floor items like machines, conveyors, and goods with smart industrial networks, clouds, and supervisory control interfaces. We next explain a cloud-based coordinator and categorize smart objects into several kinds of agents. Agents` distributed cooperation and autonomous decision-making are the driving forces behind the process`s great flexibility. To top it all off, the central coordinator`s input and coordination are key to this self-organized system`s great efficiency. In conclusion, the self-organizing multi-agent system that takes input into account and uses big data to coordinate its activities is what defines a smart factory. On the basis of this paradigm, we provide an intelligent method for agents to negotiate and work together. More than that, the research shows that deadlocks can be avoided by enhancing the coordinator`s and agents` decision-making abilities through the use of complementing techniques. You can see how well the proposed negotiating mechanism and techniques to avoid impasse work in the simulation results.
References
Industry 4.0: Making the Smart Factory Work. Bosch Rexroth Poland. https://www.boschrexroth.com/pl/pl/trendy/industry-4-0/features-of-smart-manufacturing/ the-smart-factory/bosch-rexroth-s-contribution-9. Accessed: 10 October 2020
H. Burkhart and R. Millen 1989 Performance-measurement tools in a multiprocessor environment IEEE Trans. Comput. 38 725–37
Franklin S and Graesser A 1997 Is It an agent, or just a program?: A taxonomy for autonomous agents Intelligent Agents III Agent Theories, Architectures, and Languages ed J P Müller, M J Wooldridge and N R Jennings (Berlin, Heidelberg: Springer Berlin Heidelberg) pp 21–35
Castelfranchi C 1997 To be or not to be an “agent” Intelligent Agents III Agent Theories, Architectures, and Languages ed J P Müller, M J Wooldridge and N R Jennings (Berlin, Heidelberg: Springer Berlin Heidelberg) pp 37–9
Castelfranchi C 2010 Bye-Bye Agents? Not IEEE Internet Comput. 14 93–6
Wooldridge M J and Jennings N R 1994 Agent Theories, Architectures and Languages: A Survey ECAI94 Workshop on Agent Theories Architectures and Languages (01/01/94) ed M J Woolridge and N R Jennings pp 1–32
Georgeff M, Pell B, Pollack M, Tambe M and Wooldridge M 1998 The Belief-Desire-Intention Model of Agency Intelligent Agents V: Agents Theories, Architectures, and Languages Int. Workshop on Agent Theories, Architectures, and Languages (Springer, Berlin, Heidelberg) pp 1–10
Unland R 2015 Chapter 2 - Industrial Agents Industrial Agents ed P Leitão and S Karnouskos (Boston: Morgan Kaufmann) pp 23–44
Luck M, Griffiths N and d’Inverno M 1997 From agent theory to agent construction: A case study Intelligent Agents III Agent Theories, Architectures, and Languages ed J P Müller, M J Wooldridge and N R Jennings (Berlin, Heidelberg: Springer Berlin Heidelberg) pp 49–63
Gonaygunta, Hari, Factors Influencing the Adoption of Machine Learning Algorithms to Detect Cyber Threats in the Banking Industry, DAI-A 85/7(E), Dissertation Abstracts International, Ann Arbor, United States, ISBN 9798381387865, 142, 2023.
Nadella, G. S. (2023). Validating the Overall Impact of IS on Educators in U.S. High Schools Using IS-Impact Model – A Quantitative PLS-SEM Approach, DAI-A 85/7(E), Dissertation Abstracts International, Ann Arbor, ISBN 9798381388480, 189, 2023.
Hari Gonaygunta (2023) Machine Learning Algorithms for Detection of Cyber Threats using Logistic Regression, 10.47893/ijssan.2023.1229.
Hari Gonaygunta, Pawankumar Sharma, (2021) Role of AI in product management automation and effectiveness, https://doi.org/10.2139/ssrn.4637857
R Pulimamidi, GP Buddha, Applications of Artificial Intelligence Based Technologies in The Healthcare Industry, Tuijin Jishu/Journal of Propulsion Technology 44 (3), 4513-4519.
R Pulimamidi, GP Buddha, AI-Enabled Health Systems: Transforming Personalized Medicine And Wellness, Tuijin Jishu/Journal of Propulsion Technology 44 (3), 4520-4526.
R Pulimamidi, P Ravichandran, Connected Health: Revolutionizing Patient Care Through Artificial Intelligence Innovations, Tuijin Jishu/Journal of Propulsion Technology 44 (3), 3940-3947.
R Pulimamidi, P Ravichandran, Enhancing Healthcare Delivery: AI Applications In Remote Patient Monitoring, Tuijin Jishu/Journal of Propulsion Technology 44 (3), 3948-3954.
P Kanagala, Effective cyber security system to secure optical data based on deep learning approach for healthcare application, Optik 272, 170315.
P Kanagala, Implementing cryptographic-based DH approach for enterprise network, Optik 272, 170252.
P Kanagala, R Jayaraman, FAA-Cloud approach to minimize computation overhead using fuzzy-based crypto security, Soft Computing, 1-11.
P Kanagala, R Jayaraman, Effective encryption approach to improving the secure cloud framework through fuzzy-based encrypted cryptography, Soft Computing, 1-10.
Ramya Manikyam, J. Todd McDonald, William R. Mahoney, Todd R. Andel, and Samuel H. Russ. 2016.Comparing the effectiveness of commercial obfuscators against MATE attacks. In Proceedings of the 6th Workshop on Software Security, Protection, and Reverse Engineering (SSPREW’16)
R. Manikyam. 2019.Program protection using software based hardware abstraction.Ph.D. Dissertation.University of South Alabama.
B. Nagaraj, A. Kalaivani, S. B. R, S. Akila, H. K. Sachdev, and S. K. N, “The Emerging Role of Artificial intelligence in STEM Higher Education: A Critical review,” International Research Journal of Multidisciplinary Technovation, pp. 1–19, Aug. 2023, doi: 10.54392/irjmt2351.
D. Sivabalaselvamani, K. Nanthini, Bharath Kumar Nagaraj, K. H. Gokul Kannan, K. Hariharan, M. Mallingeshwaran, Healthcare Monitoring and Analysis Using ThingSpeak IoT Platform: Capturing and Analyzing Sensor Data for Enhanced Patient Care, IGI Global eEditorial Discovery, Pages: 25, 2024. DOI: 10.4018/979-8-3693-1694-8.ch008
Sri Charan Yarlagadda, Role of Artificial Intelligence, Automation, and Machine Learning in Sustainable Plastics Packaging markets: Progress, Trends, and Directions, International Journal on Recent and Innovation Trends in Computing and
Communication, Vol:11, Issue 9s, Pages: 818–828, 2023.
Sri Charan Yarlagadda, The Use of Artificial Intelligence and Machine Learning in Creating a Roadmap Towards a Circular Economy for Plastics, International Journal on Recent and Innovation Trends in Computing and Communication, Vol:11, Issue 9s, Pages: 829-836, 2023.
Amol Kulkarni, Amazon Athena Serverless Architecture and Troubleshooting, International Journal of Computer Trends and Technology, Vol, 71, issue, 5, pages 57-61, 2023.
Amazon Redshift Performance Tuning and Optimization,International Journal of Computer Trends and Technology, vol, 71, issue, 2, pages, 40-44, 2023
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Mohan Kunkulagunta (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
