Secure Data Provenance and Semantic Interoperability Model for Big Data in IoT: A healthcare perspective
Keywords:
Data Provenance, Semantic Interoperability, Big Data, Internet of Things (IoT).Abstract
The Internet of Things (IoT) is a heterogeneous network involving diverse communication models. Ensuring trustworthy and reliable data is a major challenge, especially due to the lack of standards for semantic interoperability (SI). This issue is critical in applications like healthcare, where secure data provenance and effective integration are essential. Current tools are often inadequate for achieving SI across diverse IoT devices. This research proposes a novel model to ensure secure SI and robust data provenance in healthcare IoT systems, aiming to improve communication, reliability, and security across connected devices.
References
Ali, S., Wang, G., Bhuiyan, M. Z. A., & Jiang, H. (2018). Secure data provenance in cloud-centric Internet of Things via blockchain smart contracts. In 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 991-998). IEEE.
Ansari, S., Aslam, T., Poncela, J., Otero, P., & Ansari, A. (2020). Internet of things-based healthcare applications. In IoT architectures, models, and platforms for smart city applications (pp. 1-28). IGI Global Scientific Publishing.
Cook, E. L. (2020, March). Could Interoperability Between IoT And Ehr Make Healthcare More Efficient?. In Proceedings of the Appalachian Research in Business Symposium (Vol. 1).
Younas, R., & Qasim, A. (2019, April). Streaming State Validation Technique for Textual Big Data Using Apache Flink. In International Conference on Computational Linguistics and Intelligent Text Processing (pp. 632-647). Cham: Springer Nature Switzerland.
Hussain, N., Qasim, A., Mehak, G., Kolesnikova, O., Gelbukh, A., & Sidorov, G. (2025). ORUD-Detect: A Comprehensive Approach to Offensive Language Detection in Roman Urdu Using Hybrid Machine Learning–Deep Learning Models with Embedding Techniques. Information, 16(2), 139.
Imran, M., & Hlavacs, H. (2012, October). Applications of provenance data for cloud infrastructure. In 2012 Eighth International Conference on Semantics, Knowledge and Grids (pp. 16-23). IEEE.
John, B., & Wickramasinghe, N. (2019). A review of mixed reality in health care. Delivering Superior Health and Wellness Management with IoT and Analytics, 375-382.
Khan, H., Habib, S., Qasim, A., Hussain, N., Usman, M., Mahmood, A., ... & Zain, M. (2023, December). The Study of Human Action Recognition in Videos with Long Short-Term Memory Model. In Annual International Conference on Information Management and Big Data (pp. 217-230). Cham: Springer Nature Switzerland.
Kiljander, J., D’elia, A., Morandi, F., Hyttinen, P., Takalo-Mattila, J., Ylisaukko-Oja, A., ... & Cinotti, T. S. (2014). Semantic interoperability architecture for pervasive computing and internet of things. IEEE access, 2, 856-873.
Hussain, N., Qasim, A., Akhtar, Z. U. D., Qasim, A., Mehak, G., del Socorro Espindola Ulibarri, L., ... & Gelbukh, A. (2023, November). Stock Market Performance Analytics Using XGBoost. In Mexican International Conference on Artificial Intelligence (pp. 3-16). Cham: Springer Nature Switzerland.
Lomotey, R. K., Sofranko, K., & Orji, R. (2018). Enhancing privacy in wearable IoT through a provenance architecture. Multimodal Technologies and interaction, 2(2), 18.
Manzoor, M. I., Shaheen, M., Khalid, H., Anum, A., Hussain, N., & Faheem, M. R. (2018). Requirement Elicitation Methods for Cloud Providers in IT Industry. International Journal of Modern Education & Computer Science, 10(10).
Qasim, A., Mehak, G., Hussain, N., Gelbukh, A., & Sidorov, G. (2025). Detection of Depression Severity in Social Media Text Using Transformer-Based Models. Information, 16(2), 114.
Mehmood, R., Faisal, M. A., & Altowaijri, S. (2015). Future networked healthcare systems: a review and case study. In Handbook of research on redesigning the future of internet architectures (pp. 531-558). IGI Global Scientific Publishing.
Meque, A. G. M., Hussain, N., Sidorov, G., & Gelbukh, A. (2023). Machine learning-based guilt detection in text. Scientific Reports, 13(1), 11441.
Pavithra, D., & Balakrishnan, R. (2015, April). IoT based monitoring and control system for home automation. In 2015 global conference on communication technologies (GCCT) (pp. 169-173). IEEE.
Shaheen, M., Anees, T., Hussain, N., & Obaid, I. (2019, April). A Research on SOA in the IT Industry of Pakistan. In Proceedings of the 2019 5th International Conference on Computer and Technology Applications (pp. 149-154).
Shaheen, M., Awan, S. M., Hussain, N., & Gondal, Z. A. (2019). Sentiment analysis on mobile phone reviews using supervised learning techniques. International Journal of Modern Education and Computer Science, 10(7), 32.
Tash, M. S., Ahani, Z., Tonja, A., Gemeda, M., Hussain, N., & Kolesnikova, O. (2022, December). Word level language identification in code-mixed kannada-english texts using traditional machine learning algorithms. In Proceedings of the 19th International Conference on Natural Language Processing (ICON): Shared Task on Word Level Language Identification in Code-mixed Kannada-English Texts (pp. 25-28).
Hussain, N., Anees, T., Faheem, M. R., Shaheen, M., Manzoor, M. I., & Anum, A. (2018). Development of a novel approach to search resources in IoT. Development, 9(9).
Hussain, N., Qasim, A., Mehak, G., Kolesnikova, O., Gelbukh, A., & Sidorov, G. (2025). Hybrid Machine Learning and Deep Learning Approaches for Insult Detection in Roman Urdu Text. AI, 6(2), 33.
Zafar, F., Khan, A., Suhail, S., Ahmed, I., Hameed, K., Khan, H. M., ... & Anjum, A. (2017). Trustworthy data: A survey, taxonomy and future trends of secure provenance schemes. Journal of network and computer applications, 94, 50-68.
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Nisar Hussain, Amna Qasim, Muhammad Zain, Grigori Sidorov
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
