IoT in manufacturing: A bibliometric analysis of global research trends in computer science from 2013 to 2023
Vol 8, Issue 16, 2024
VIEWS - 698 (Abstract)
Abstract
One of the main concerns in computer science today is integrating the Internet of Things (IoT) into manufacturing processes. This trend could influence a country’s strategy and policy development regarding technological infrastructure. However, despite extensive research on the implementation of IoT in manufacturing, no study has yet focused on the growing research interest in this topic. Based on 2487 papers indexed in the Scopus database between 2013 and 2023, this bibliometric review examines current trends and patterns in IoT research in manufacturing. The literature was selected and screened using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Data visualization was created using VOSviewer. The results show a notable increase in research papers centered around IoT in manufacturing. The findings reveal patterns and trends in IoT research publications in the manufacturing sector, author collaboration networks, country collaboration networks, and both established and newly trending topics surrounding IoT in the manufacturing industry.
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- Adel, A. (2022). Future of industry 5.0 in society: human-centric solutions, challenges and prospective research areas. Journal of Cloud Computing, 11(1). https://doi.org/10.1186/s13677-022-00314-5
- Alhammadi, A., Alsyouf, I., Semeraro, C., et al. (2024). The role of industry 4.0 in advancing sustainability development: A focus review in the United Arab Emirates. Cleaner Engineering and Technology, 18, 100708. https://doi.org/10.1016/j.clet.2023.100708
- Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
- Barrios, P., Danjou, C., & Eynard, B. (2022). Literature review and methodological framework for integration of IoT and PLM in manufacturing industry. Computers in Industry, 140, 103688. https://doi.org/10.1016/j.compind.2022.103688
- Bukhsh, M., Abdullah, S., & Bajwa, I. S. (2021). A Decentralized Edge Computing Latency-Aware Task Management Method With High Availability for IoT Applications. IEEE Access, 9, 138994–139008. https://doi.org/10.1109/access.2021.3116717
- Davies, I. N., Taylor, O. E., Anireh, V. I. E., & Bennett, E. O. (2024). A Distributed Intrusion Detection System for IoT-Enabled Network and Devices using Hybrid Technique. International Journal of Computer Science and Mathematical Theory, 10(2), 141-156. https://doi.org/10.56201/ijcsmt.v10.no2.2024.pg141.156
- Fernandez‐Alles, M., & Ramos‐Rodríguez, A. (2009). Intellectual structure of human resources management research: A bibliometric analysis of the journal Human Resource Management, 1985–2005. Journal of the American Society for Information Science and Technology, 60(1), 161–175. Portico. https://doi.org/10.1002/asi.20947
- Fernández-Caramés, T. M., & Fraga-Lamas, P. (2018). A Review on the Use of Blockchain for the Internet of Things. IEEE Access, 6, 32979–33001. https://doi.org/10.1109/access.2018.2842685
- Garg, K., Goswami, C., Chhatrawat, R. S., et al. (2022). Internet of things in manufacturing: A review. Materials Today: Proceedings, 51, 286–288. https://doi.org/10.1016/j.matpr.2021.05.321
- Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23–34. https://doi.org/10.1016/j.compind.2017.04.002
- Jazdi, N. (2014). Cyber physical systems in the context of Industry 4.0. 2014 IEEE International Conference on Automation, Quality and Testing, Robotics. https://doi.org/10.1109/aqtr.2014.6857843
- Khang, A., Rath, K. C., Mishra, B. K., et al. (2024). Future Directions and Challenges in Designing Workforce Management Systems for Industry 4.0. AI-Oriented Competency Framework for Talent Management in the Digital Economy, 1–27. https://doi.org/10.1201/9781003440901-1
- Khullar, V., Singh, H. P., Miro, Y., et al. (2022). IoT Fog-Enabled Multi-Node Centralized Ecosystem for Real Time Screening and Monitoring of Health Information. Applied Sciences, 12(19), 9845. https://doi.org/10.3390/app12199845
- Krishna, R., Yaduvanshi, R. S., Singh, H., et al. (2023). Mathematical modeling and parameter analysis of quantum antenna for IoT sensor-based biomedical applications. Journal of Autonomous Intelligence, 6(2). https://doi.org/10.32629/jai.v6i2.578
- Lam, W. S., Lam, W. H., & Lee, P. F. (2023). A Bibliometric Analysis of Digital Twin in the Supply Chain. Mathematics, 11(15), 3350. https://doi.org/10.3390/math11153350
- Lampropoulos, G., Garzón, J., Misra, S., et al. (2024). The Role of Artificial Intelligence of Things in Achieving Sustainable Development Goals: State of the Art. Sensors, 24(4), 1091. https://doi.org/10.3390/s24041091
- Lee, J., Bagheri, B., & Kao, H.-A. (2015). A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. https://doi.org/10.1016/j.mfglet.2014.12.001
- Lukito, D., Suharnomo, & Perdhana, M. S. (2023). Investigating the Relationship of Change Leadership, Knowledge Acquisition, and Firm Performance in Digital Transformation Context. Calitatea, 24(194), 286–295. https://doi.org/10.47750/QAS/24.194.32
- Malhotra, P., Singh, Y., Anand, P., et al. (2021). Internet of Things: Evolution, Concerns and Security Challenges. Sensors, 21(5), 1809. https://doi.org/10.3390/s21051809
- Mammen, P. M. (2021). Federated Learning: Opportunities and Challenges (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2101.05428
- Manimuthu, A., Venkatesh, V. G., Shi, Y., et al. (2022). Design and development of automobile assembly model using federated artificial intelligence with smart contract. International Journal of Production Research, 60(1), 111–135. https://doi.org/10.1080/00207543.2021.1988750
- Mishra, M., Desul, S., Santos, C. A. G., et al. (2023). A bibliometric analysis of sustainable development goals (SDGs): a review of progress, challenges, and opportunities. Environment, Development and Sustainability, 26(5), 11101–11143. https://doi.org/10.1007/s10668-023-03225-w
- Olateju, O. O., Okon, S. U., Igwenagu, U. T. I., et al. (2024). Combating the Challenges of False Positives in AI-Driven Anomaly Detection Systems and Enhancing Data Security in the Cloud. Asian Journal of Research in Computer Science, 17(6), 264–292. https://doi.org/10.9734/ajrcos/2024/v17i6472
- Rafati, A., & Shaker, H. R. (2024). Predictive maintenance of district heating networks: A comprehensive review of methods and challenges. Thermal Science and Engineering Progress, 53, 102722. https://doi.org/10.1016/j.tsep.2024.102722
- Rana, A., Sharma, S., Nisar, K., et al. (2022). The Rise of Blockchain Internet of Things (BIoT): Secured, Device-to-Device Architecture and Simulation Scenarios. Applied Sciences, 12(15), 7694. https://doi.org/10.3390/app12157694
- Rathod, G., Sabnis, V., & Jain, J. K. (2024). Intrusion Detection System (IDS) in Cloud Computing using Machine Learning Algorithms: A Comparative Study. Grenze International Journal of Engineering & Technology (GIJET), 10(1).
- Sahoo, S. (2021). Big data analytics in manufacturing: a bibliometric analysis of research in the field of business management. International Journal of Production Research, 60(22), 6793–6821. https://doi.org/10.1080/00207543.2021.1919333
- Sangeetha, A. S., Shunmugan, S., & Murugan, G. (2020). Blockchain for IoT Enabled Supply Chain Management - A Systematic Review. 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). https://doi.org/10.1109/i-smac49090.2020.9243371
- Santhosh, N., Srinivsan, M., & Ragupathy, K. (2020). Internet of Things (IoT) in smart manufacturing. IOP Conference Series: Materials Science and Engineering, 764(1), 012025. https://doi.org/10.1088/1757-899x/764/1/012025
- Sarker, Md. S. I., & Bartok, I. (2024). Global trends of green manufacturing research in the textile industry using bibliometric analysis. Case Studies in Chemical and Environmental Engineering, 9, 100578. https://doi.org/10.1016/j.cscee.2023.100578
- Shamayleh, A., Awad, M., & Farhat, J. (2020). IoT Based Predictive Maintenance Management of Medical Equipment. Journal of Medical Systems, 44(4). https://doi.org/10.1007/s10916-020-1534-8
- Singh, N., Panigrahi, P. K., Zhang, Z., et al. (2024). Cyber-physical systems: a bibliometric analysis of literature. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-024-02380-9
- Soori, M., Arezoo, B., & Dastres, R. (2023). Internet of things for smart factories in industry 4.0, a review. Internet of Things and Cyber-Physical Systems, 3, 192–204. https://doi.org/10.1016/j.iotcps.2023.04.006
- Sundaram, S., & Zeid, A. (2023). Artificial Intelligence-Based Smart Quality Inspection for Manufacturing. Micromachines, 14(3), 570. https://doi.org/10.3390/mi14030570
- Tarigan, M., Heryadi, Y., Lukas, Wibowo, A., et al. (2021). The Internet of Things: Real-Time Monitoring System for Production Machine. 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). https://doi.org/10.1109/icitisee53823.2021.9655968
- Upasane, S. J., Hagras, H., Anisi, M. H., et al. (2023). A Type-2 Fuzzy-Based Explainable AI System for Predictive Maintenance Within the Water Pumping Industry. IEEE Transactions on Artificial Intelligence, 5(2), 490–504. https://doi.org/10.1109/tai.2023.3279808
- Wahyono, T., & Heryadi, Y. (2019). Machine Learning Applications for Anomaly Detection. Computational Intelligence in the Internet of Things, 49–83. https://doi.org/10.4018/978-1-5225-7955-7.ch003
- Wang, J., Li, X., Wang, P., et al. (2022). Bibliometric analysis of digital twin literature: a review of influencing factors and conceptual structure. Technology Analysis & Strategic Management, 36(1), 166–180. https://doi.org/10.1080/09537325.2022.2026320
- Wang, J., Ma, Y., Zhang, L., et al. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144–156. https://doi.org/10.1016/j.jmsy.2018.01.003
- Xu, L. D., He, W., & Li, S. (2014). Internet of Things in Industries: A Survey. IEEE Transactions on Industrial Informatics, 10(4), 2233–2243. https://doi.org/10.1109/tii.2014.2300753
- Yalcinkaya, E., Maffei, A., & Onori, M. (2020). Blockchain Reference System Architecture Description for the ISA95 Compliant Traditional and Smart Manufacturing Systems. Sensors, 20(22), 6456. https://doi.org/10.3390/s20226456
- Zeba, G., Dabic, M., Cicak, M., et al. (2020). Artificial Intelligence in Manufacturing: Bibliometric and Content Analysis. 2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G). https://doi.org/10.1109/ai4g50087.2020.9311087
- Zhong, R. Y., Xu, X., Klotz, E., et al. (2017). Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering, 3(5), 616–630. https://doi.org/10.1016/j.eng.2017.05.015
- Zuo, Y., & Qi, Z. (2022). A Blockchain-Based IoT Framework for Oil Field Remote Monitoring and Control. IEEE Access, 10, 2497–2514. https://doi.org/10.1109/access.2021.3139582
DOI: https://doi.org/10.24294/jipd7716
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