The article investigates the potential of serverless architectures for efficiently processing large-scale metadata generated by Internet of Things (IoT) sensors. As IoT systems grow increasingly complex, the challenges associated with processing vast amounts of data in distributed environments become more pronounced. Key issues include ensuring data accuracy, maintaining scalability, and reducing the operational costs of data processing infrastructure. The paper proposes serverless computing as a highly adaptable solution to these challenges, focusing on its capacity for real-time processing, dynamic scaling, and seamless integration with modern cloud platforms. The research highlights the importance of dynamic calibration of IoT sensors to ensure the accuracy and reliability of collected data. Dynamic calibration addresses challenges such as environmental changes and sensor degradation, leveraging serverless systems to automate recalibration based on real-time data analysis. The authors propose an architecture based on Amazon Web Services (AWS) to demonstrate the practical application of serverless principles. This architecture incorporates AWS Lambda for computational tasks, SQS for workload distribution, and S3 for scalable data storage.
The article emphasises the advantages of serverless systems, including cost-efficiency, resource optimisation, and scalability, while acknowledging challenges such as secure integration of private data and potential errors in automated systems. The authors argue that, with proper implementation, serverless architectures can provide robust solutions for IoT metadata processing, enabling improved performance, reliability, and economic efficiency in modern IoT ecosystems.
By addressing both theoretical and practical aspects, the study offers valuable insights for researchers and practitioners seeking to harness the power of serverless systems for IoT applications. The findings underscore the transformative potential of cloud-based, serverless infrastructures in achieving efficient and scalable data management for IoT-driven industries.
- Eichstädt, S. (2020, June). From dynamic measurement uncertainty to the Internet of Things and Industry 4.0. In 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT , pp. 632-635. IEEE. DOI:https:// doi.org/10.1109/MetroInd4.0IoT48571.2020.9138250.
- Schneider, T., Helwig, N., & Schütze, A. (2018). Industrial condition monitoring with smart sensors using automated feature extraction and selection. Measurement Science and Technology, 29(9), 094002. DOI:https://doi.org/10.1088/1361-6501/aad1d4
- S. Hackel, F. Härtig, T. Schrader, A. Scheibner, J. Loewe, L. Doering, B. Gloger, J. Jagieniak, D. Hutzschenreuter, and G. Söylev-Öktem. The fundamental architecture of the DCC. Measurement: Sensors, 18:100354, 2021. DOI:https://doi.org/10.1016/j.measen.2021.100354.
- Gu, J., Liu, C., Zhuang, Y., Du, X., Zhuang, F., Ying, H., …& Guizani, M. (2020). Dynamic measurement and data calibration for aerial mobile IoT. IEEE Internet of Things Journal, 7(6), pp. 5210-5219. DOI:https://doi.org/ 10.1109/JIOT.2020.2977910
- Nummiluikki, J., Saxholm, S., Kärkkäinen, A., & Koskinen,S. (2023). Digital Calibration Certificate in an industrial application. Acta IMEKO, 12(1), pp. 1-6. DOI: 10.21014/actaimeko.v12i1.1402.
- Rajan, R. A. P. (2018, December). Serverless architecture-a revolution in cloud computing. In 2018 Tenth International Conference on Advanced Computing (ICoAC), pp. 88-93. IEEE. 23 December 2019. DOI:https://doi.org/ 10.1109/ICoAC44903.2018.8939081
- Yang, Li & Shami, Abdallah. (2022). IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective. 10.48550/arXiv.2209.08018. 105 p.DOI:10.48550/arXiv.2209.08018
- Mukherjee, S. (2019). Benefits of AWS in modern cloud. arXiv preprint arXiv:1903.03219. DOI: https://doi.org/10.48550/arXiv.1903.03219
- Wittig, A., & Wittig, M. (2023). Amazon Web Services in Action: An in-depth guide to AWS. Simon and Schuster.ISBN: 163343916X
- Aquino, G., Queiroz, R., Merrett, G., & Al-Hashimi, B. (2019). The circuit breaker pattern targeted to future iot applications. In Service-Oriented Computing: 17th International Conference, ICSOC 2019, Toulouse, France, October 28–31, 2019, Proceedings 17, pp. 390-396. Springer International Publishing. DOI:https://doi.org/ 10.1007/978-3-030-33702-5_30
- H.M. Khan, A. Khan, F. Jabeen, A. Anjum, G. Jeon (2021). Fog-enabled secure multiparty computation-based aggrega- tion scheme in smart grid. Computers & Electrical Engi- neering, Vol. 94, 107358. DOI:https://doi.org/10.1016/ j.compeleceng.2021.107358.
- Song, B., Yu, Y., Zhou, Y., Wang, Z., & Du, S. (2018).Host load prediction with long short-term memory in cloud computing. The Journal of Supercomputing, 74, pp. 6554- 6568. DOI: https://doi.org/10.1007/s11227-017-2044-4
- Le, D. M., Dang, D. H., & Nguyen, V. H. (2018). On domain driven design using annotation-based domain specific language. Computer Languages, Systems & Structures, 54, pp. 199-235. DOI:https://doi.org/10.1016/j.cl.2018.05.001