Integrating Big Data and IoT in Physics Laboratory Information Systems: A Systematic Literature Review
DOI:
https://doi.org/10.30872/jlpf.v6i2.4986Keywords:
Automation, Big Data, Data Security, Information System, IoT, Physics LaboratoryAbstract
Physics laboratories generate large volumes of complex experimental data that require efficient and secure management systems. The integration of Big Data and IoT offers solutions through real-time monitoring, automation, and advanced analytics. However, adoption in educational and small-scale laboratories remains limited due to high costs, data security issues, and technical skill gaps. This study employs a Systematic Literature Review (SLR) method, analyzing 30 articles published between 2017 and 2024 from open-access sources such as Google Scholar and IEEE Xplore. Data extraction focuses on applications, challenges, and synergies of Big Data and IoT in physics laboratory information systems. The findings highlight key applications such as real-time environmental monitoring, automated data collection, RFID-based inventory management, and advanced data analytics. The main challenges identified include high implementation costs, system incompatibility, and a lack of skilled personnel. The synergy between IoT and Big Data enhances accuracy, operational efficiency, and decision-making. This study presents a structured framework of challenges and corresponding solutions, and also highlights underutilized practices such as LIMS integration and adaptive environmental control. The contributions include theoretical insights into the digital transformation of laboratories and practical strategies based on CERN’s case study that are applicable to educational and research labs.
Downloads
References
Adenekan, T. (2022). Analyzing the Security Landscape of RFID Tags: Challenges and Solutions. https://www.researchgate.net/publication/385172397_Analyzing_the_Security_Landscape_of_RFID_Tags_Challenges_and_Solutions
Ahmad, A. F., Sayeed, M. S., Tan, C., Tan, K., Bari, M. A., & Hossain, F. (2021). A Review on IoT with Big Data Analytics. https://doi.org/10.1109/ICoICT52021.2021.9527503
Ahsun, A., & Elly, B. (2024). Optimizing Resource Allocation for Enhanced Project Efficiency. https://www.researchgate.net/publication/385560767_Optimizing_Resource_Allocation_for_Enhanced_Project_Efficiency
Alshar’e, M. (2023). Cyber Security Framework Selection: Comparision Of Nist And Iso27001. Applied Computing Journal, 245–255. https://doi.org/10.52098/acj.202364
Boyar, K., Pham, A., Swantek, S., Ward, G., & Herman, G. (2021). Laboratory Information Management Systems (LIMS) (pp. 131–151). https://doi.org/10.1007/978-3-030-62716-4_7
Devineni, S. K., Kathiriya, S., & Shende, A. (2023). Machine Learning-Powered Anomaly Detection: Enhancing Data Security and Integrity. Journal of Artificial Intelligence & Cloud Computing, 1–9. https://doi.org/10.47363/JAICC/2023(2)184
Di Meglio, A., Jansen, K., Tavernelli, I., Alexandrou, C., Arunachalam, S., Bauer, C. W., Borras, K., Carrazza, S., Crippa, A., Croft, V., de Putter, R., Delgado, A., Dunjko, V., Egger, D. J., Fernandez-Combarro, E., Fuchs, E., Funcke, L., Gonzalez-Cuadra, D., Grossi, M., … Zhang, J. (2023). Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group. https://doi.org/10.1103/PRXQuantum.5.037001
Dinesh, D., & Smith, N. (2024). Integrating Iot, AI, And Big Data For Enhanced Operational Efficiency In Smart Factories. Educational Administration Theory and Practices, 30. https://doi.org/10.53555/sfs.v30i5.6492
Elias, J. R., Chard, R., Libera, J. A., Foster, I., & Chaudhuri, S. (2020). Manufacturing Data and Machine Learning Platform: Enabling Real-Time Monitoring and Control of Scientific Experiments via IoT. IEEE 6th World Forum on Internet of Things (WF-IoT), 1(2). https://doi.org/10.1109/WF-IoT48130.2020.9221078
Fawzy, D., & Moussa, S. (2022). The Internet of Things and Architectures of Big Data Analytics: Challenges of Intersection at Different Domains. IEEE Access, PP, 1. https://doi.org/10.1109/ACCESS.2022.3140409
Gkrimpizi, T., Peristeras, V., & Magnisalis, I. (2023). Classification of Barriers to Digital Transformation in Higher Education Institutions: Systematic Literature Review. In Education Sciences (Vol. 13, Issue 7). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/educsci13070746
Goundar, S., Bhardwaj, A., Singh, S., Singh, M., & H L, G. (2021). Big Data and Big Data Analytics: A Review of Tools and its Application (pp. 1–19). https://doi.org/10.4018/978-1-7998-6673-2.ch001
Gregorcic, B., & Linder, C. (2022). Challenges and Strategies in Physics Laboratory Work. https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-476218
Harb, H., Makhoul, A., Idrees, A., Zahwi, O., & Taam, M. (2017a). Wireless Sensor Networks: A Big Data Source in Internet of Things. International Journal of Sensors, Wireless Communications and Control, 07. https://doi.org/10.2174/2210327907666170906144926
Harb, H., Makhoul, A., Idrees, A., Zahwi, O., & Taam, M. (2017b). Wireless Sensor Networks: A Big Data Source in Internet of Things. International Journal of Sensors, Wireless Communications and Control, 07. https://doi.org/10.2174/2210327907666170906144926
Islam, Z., Bhuiyan, M. R. I., Poli, T., Hossain, R., & Mani, L. (2024). Gravitating towards Internet of Things: Prospective Applications, Challenges, and Solutions of Using IoT. International Journal of Religion, 5, 436–451. https://doi.org/10.61707/awg31130
Jain, V., Mitra, A., & Paul, S. (2025). Integrating IoT and Big Data Analytics for Enhancing Maritime Safety and Sustainability (pp. 225–256). https://doi.org/10.4018/979-8-3373-1052-7.ch009
Jarašūnienė, A., Čižiūnienė, K., & Čereška, A. (2023). Research on Impact of IoT on Warehouse Management. Sensors, 23, 2213. https://doi.org/10.3390/s23042213
Kalburgi, S. (2024). Enhancing IoT Data Processing with Big Data Analytics. https://doi.org/10.13140/RG.2.2.26319.09123
Kostadimas, D., Kasapakis, V., & Kotis, K. (2025). A Systematic Review on the Combination of VR, IoT and AI Technologies, and Their Integration in Applications. In Future Internet (Vol. 17, Issue 4). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/fi17040163
Morchid, A., El Alami, R., Raezah, A. A., & Sabbar, Y. (2024). Applications of internet of things (IoT) and sensors technology to increase food security and agricultural Sustainability: Benefits and challenges. Ain Shams Engineering Journal, 15(3). https://doi.org/10.1016/j.asej.2023.102509
Muchsin, M., Rahmawati, R. F., & Faishol, M. (2024). Radio Frequency Identification (RFID) Technology Innovation in Science Laboratory Services (pp. 124–132). https://doi.org/10.2991/978-2-38476-331-3_11
Munir, T., Akbar, M. S., Ahmed, S., Sarfraz, A., Sarfraz, Z., Sarfraz, M., Felix, M., & Cherrez-Ojeda, I. (2022). A Systematic Review of Internet of Things in Clinical Laboratories: Opportunities, Advantages, and Challenges. Sensors, 22(20). https://doi.org/10.3390/s22208051
Oluwole, O. G., Oosterwyk, C., Anderson, D., Adadey, S. M., Mnika, K., Manyisa, N., Yalcouye, A., Wonkam, E. T., Aboagye, E. T., Dia, Y., Uwibambe, E., Jonas, M., Priestley, R., Popel, K., Manyashe, T., de Cock, C., Nembaware, V., & Wonkam, A. (2022). The Implementation of Laboratory Information Management System in Multi-Site Genetics Study in Africa: The Challenges and Up-Scaling Opportunities. Journal of Molecular Pathology, 3(4), 262–272. https://doi.org/10.3390/jmp3040022
Patrício, L., Varela, L., & Silveira, Z. (2025). Implementation of a Sustainable Framework for Process Optimization Through the Integration of Robotic Process Automation and Big Data in the Evolution of Industry 4.0. Processes, 13(2). https://doi.org/10.3390/pr13020536
Said, A., Yahyaoui, A., & Abdellatif, T. (2024). HIPAA and GDPR Compliance in IoT Healthcare Systems (pp. 198–209). https://doi.org/10.1007/978-3-031-55729-3_16
Samonte, M. J., Mendoza, F., Pablo, R., & Villa, S. (2021). Internet-of-Things Based Smart Laboratory Environment Monitoring System. https://doi.org/10.1109/ICIEA52957.2021.9436758
Shen, Y. J., Zhang, Y. L., Gao, F., Yang, G. S., & Lai, X. P. (2018). Influence of temperature on the microstructure deterioration of sandstone. Energies, 11(7). https://doi.org/10.3390/en11071753
Tian, W., Vangilder, J., Condor, M., Han, X., & Zuo, W. (2019). An Accurate Fast Fluid Dynamics Model for Data Center Applications. https://doi.org/10.1109/ITHERM.2019.8757336
Unhelkar, B., Joshi, S., Sharma, M., Prakash, S., Mani, A., & Prasad, M. (2022). Enhancing supply chain performance using RFID technology and decision support systems in the industry 4.0–A systematic literature review. International Journal of Information Management, 2. https://doi.org/10.1016/j.jjimei.2022.100084
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Nadiar Pertiwi, Amelia Fatonah, Intan Mahdiah, Hamdi Akhsan, Iful Amri
This work is licensed under a Creative Commons Attribution 4.0 International License.
JLPF (Jurnal Literasi Pendidikan Fisika) is an Open Access Journal. JLPF allows the author(s) to hold the copyright and to retain the publishing rights. The authors who publish the manuscript in this journal agree to the following terms:
Jurnal Literasi Pendidikan Fisika by Physics Education Program, Universitas Mulawarman is licensed under a Creative Commons Attribution 4.0 International License. This permits anyone to:
1. Share - copy and redistribute the material in any medium or format
2. Adapt - remix, transform, and build upon the material for any purpose, even commercially.
Under the following terms:
1. Attribution - You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
2. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
.png)
