Integrating Big Data and IoT in Physics Laboratory Information Systems: A Systematic Literature Review
Abstract
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.
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