چارچوب هوشمند مبتنی بر یادگیری ماشین و معماری مدلرانده برای بهینهسازی سیستمهای IoT صنعتی
محورهای موضوعی : هوش مصنوعی و رباتیکمریم نورائی 1 * , حسین شیدل زاده 2
1 - عضو هیات علمی دانشگاه آزاد اسلامی، واحد بین المللی اروند
2 - گروه مهندسی کامپیوتر، واحد بین المللی اروند، دانشگاه آزاد اسلامی، آبادان، ایران
کلید واژه: بهبود کارایی, محیطهای اینترنت اشیاء صنعتی, معماری مدل رانده, یادگیری ماشین بهینه سازی,
چکیده مقاله :
با گسترش روزافزون دستگاههای متصل به اینترنت اشیاء، حجم بالای دادههای بدونبرچسب و نیاز به پردازش سریع و مدیریت بهینه منابع، چالشهای جدیدی در این حوزه ایجاد شده است. در این میان، اهمیت روشهای نیمهنظارتی برای پیشبینی و همچنین استخراج وبژگیهای کلیدی دامنه برای بهبود دقت، بیش از پیش آشکار شده است. در این پژوهش، رویکردی نوین برای بهبود کارایی سیستمهای IoT صنعتی ارائه میشود که در آن یادگیری ماشین با بهرهگیری از معماری مدلرانده (MDA) تقویت شده است. در این چارچوب، مدلهای مفهومی دامنهمحور بهعنوان پایهای برای استخراج ویژگیهای کلیدی، ساختاردهی دادهها و کاهش نویز به کار میروند. این یکپارچهسازی باعث بهبود فرآیند یادگیری ماشین به ویژه در شرایط کمبود دادههای برچسبدار میگردد. در مرحله اول، مدلهای PIM و PSM برای جداسازی منطق سیستم از جزئیات پیادهسازی ایجاد شده و با تغییرات فناوری سازگار میشوند. سپس دادههای پردازششده و ویژگیهای استخراجشده به الگوریتمهای یادگیری نیمهنظارتی Self-training، Label Propagation و Semi-Supervised SVM وارد میشوند تا از دادههای بدون برچسب نیز در فرآیند آموزش استفاده شود. ارزیابی این رویکرد در یک محیط شبیهسازی صنعتی مبتنی بر IoT (مانند سامانه پایش و کنترل انرژی) نشان میدهد که استفاده از MDA بهعنوان لایه تقویتکننده ML، دقت پیشبینی را به طور میانگین تا ۹۵% افزایش داده، همچنین پایداری مدل در برابر دادههای نویزی به طور قابل توجهی بهبود یافته است. نتایج نشان میدهد که ترکیب MDA و یادگیری ماشین نیمهنظارتی میتواند به عنوان یک چارچوب مقیاسپذیر و قابل اعتماد برای ارتقاء کارایی صنایع مبتنی بر IoT به کار گرفته شود.
By increasing proliferation of IoT devices, the large volume of unlabeled data, and the need for fast processing and optimal resource management, new challenges have emerged in this field. In the meantime, the importance of semi-supervised methods for prediction and also for extracting key domain features to improve accuracy has become more important. In this research, a new approach to improving the efficiency of industrial IoT systems is presented, in which machine learning is enhanced by using Model-Driven Architecture. In this framework, domain-oriented conceptual models are used as the basis for extracting key features, structuring data, and reducing noise. This integration improves the machine learning process, especially in conditions of lack of labeled data. In the first step, PIM and PSM models are created to separate the system logic from the implementation details and adapt to technological changes. Then, the processed data and extracted features are fed into the semi-supervised learning algorithms Self-training, Label Propagation, and Semi-Supervised SVM to also use unlabeled data in the training process. Evaluation of this approach in an IoT-based industrial simulation environment shows that using MDA as the ML reinforcement layer increases the prediction accuracy by an average of 95%, and the robustness of the model against noisy data is also significantly improved. The results indicate that the combination of MDA and semi-supervised machine learning can improve the efficiency of IoT-based industries.
[1] Moin, A., et al., A model-driven approach to machine learning and software modeling for the IoT: Generating full source code for smart Internet of Things (IoT) services and cyber-physical systems (CPS). Software and Systems Modeling, 2022. 21(3): p. 987-1014.
[2] Dral, P.O., et al., MLatom 3: a platform for machine learning-enhanced computational chemistry simulations and workflows. Journal of Chemical Theory and Computation, 2024. 20(3): p. 1193-1213.
[3] M Casares, P.A., et al., GradDFT. A software library for machine learning enhanced density functional theory. The Journal of Chemical Physics, 2024. 160(6).
[4] Nooraei Abadeh, M., A semantic axiomatic design for integrity in IoT. Transactions on Emerging Telecommunications Technologies, 2024. 35(9): p. e5032. https://doi.org/10.1002/ett.5032.
[5] Eyke, N.S., B.A. Koscher, and K.F. Jensen, Toward machine learning-enhanced high-throughput experimentation. Trends in Chemistry, 2021. 3(2): p. 120-132.
[6] Snoeck, M. and Y. Wautelet, Agile MERODE: a model-driven software engineering method for user-centric and value-based development. Software and Systems Modeling, 2022. 21(4): p. 1469-1494.
[7] Nooraei Abadeh, M. and M. Mirzaie, DiffPageRank: An efficient differential PageRank approach in MapReduce. The Journal of Supercomputing, 2021. 77(1): p. 188–211. https://doi.org/10.1007/s11227-020-03265-3.
[8] Boumahdi, F., et al., MDA4SOA+ d: A new model driven architecture to supporting decision making in SOA. Journal of King Saud University-Computer and Information Sciences, 2023. 35(5): p. 101544.
[9] Wang, Y., et al., Machine learning-enhanced flexible mechanical sensing. Nano-Micro Letters, 2023. 15(1): p. 55.
[10] Liu, J., H. Hu, W. Xu, and D. Luo, Internet of things challenges and future scope for enhanced living environments, in Advances in Computers. 2024, Elsevier. p. 201-246.
[11] Park, Y.-S., B.U. Park, and H.-J. Jeon, Advances in machine learning-enhanced nanozymes. Frontiers in Chemistry, 2024. 12: p. 1483986.
[12] Kwon, H., Z.A. Ali, and B.M. Wong, Harnessing semi-supervised machine learning to automatically predict bioactivities of per-and polyfluoroalkyl substances (PFASs). Environmental Science & Technology Letters, 2023. 10(11): p. 1017-1022.
[13] Albayati, M.G., et al., Semi-supervised machine learning for fault detection and diagnosis of a rooftop unit. Big Data Mining and Analytics, 2023. 6(2): p. 170-184.
[14] Ahmad, E., Model-based system engineering of the Internet of Things: A bibliometric literature analysis. IEEE Access, 2023. 11: p. 50642-50658.
[15] Alulema, D., et al., SI4IoT: A methodology based on models and services for the integration of IoT systems. Future Generation Computer Systems, 2023. 143: p. 132-151.
[16] Marques, G., Editor, Internet of Things: Architectures for Enhanced Living Environments. Advances in Computers. Vol. 133. 2024, Cambridge, MA: Academic Press/Elsevier.
[17] Javed, A.R., et al., Future smart cities: Requirements, emerging technologies, applications, challenges, and future aspects. Cities, 2022. 129: p. 103794.
[18] Mardani Korani, Z., et al., Model-driven engineering techniques and tools for machine learning-enabled IoT applications: A scoping review. Sensors, 2023. 23(3): p. 1458.
[19] Moin, A. Data analytics and machine learning methods, techniques and tool for model-driven engineering of smart IoT services. in 2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). 2021. IEEE, p. 287–292.
[20] Moin, A., et al., Enabling automated machine learning for model-driven AI engineering. arXiv preprint arXiv:2203.02927, 2022.
[21] Berrouyne, I., et al., A model-driven methodology to accelerate software engineering in the internet of things. IEEE Internet of Things Journal, 2022. 9(20): p. 19757-19772.
[22] Wang, H. and D.-Y. Yeung, A survey on Bayesian deep learning. ACM computing surveys (csur), 2020. 53(5): p. 1-37.
[23] Moin, A., et al. Supporting AI engineering on the IoT edge through model-driven TinyML. in 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC). 2022. IEEE.
[24] Morin, B., N. Harrand, and F. Fleurey, Model-based software engineering to tame the IoT jungle. IEEE software, 2017. 34(1): p. 30-36.
[25] Paszke, A., et al., Automatic differentiation in PyTorch, in NIPS 2017 Workshop on Autodiff: The Future of Gradient-Based Machine Learning Software and Techniques. 2017, Long Beach, CA, USA.
[26] Atzori, L., A. Iera, and G. Morabito, The internet of things: A survey. Computer networks, 2010. 54(15): p. 2787-2805.
[27] Bennett, K.P. and E. Parrado-Hernández, The interplay of optimization and machine learning research. The Journal of Machine Learning Research, 2006. 7: p. 1265-1281.
[28] Nižetić, S., et al., Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future. Journal of cleaner production, 2020. 274: p. 122877.
[29] Aouedi, O., et al., A survey on intelligent Internet of Things: Applications, security, privacy, and future directions. IEEE Communications Surveys & Tutorials, 2025. 27(2): p. 1238–1292.
[30] Moghaddasi, K. and M. Masdari, Blockchain-driven optimization of IoT in mobile edge computing environment with deep reinforcement learning and multi-criteria decision-making techniques. Cluster Computing, 2024. 27(4): p. 4385-4413.
[31] Moin, A., S. Rössler, and S. Günnemann, ThingML+ augmenting model-driven software engineering for the Internet of Things with machine learning. arXiv preprint arXiv:2009.10633, 2020.
[32] Rademacher, F., et al., Model-Driven Engineering of Microservice Architectures—The LEMMA Approach, in Ernst Denert Award for Software Engineering 2022: Practice Meets Foundations. 2024, Springer Nature Switzerland Cham. p. 105-147.
[33] Verbruggen, C. and M. Snoeck, Practitioners’ experiences with model-driven engineering: a meta-review. Software and Systems Modeling, 2023. 22(1): p. 111-129.
[34] Moghaddasi, K. and M. Masdari, Blockchain-driven optimization of IoT in mobile edge computing environment with deep reinforcement learning and multi-criteria decision-making techniques. Cluster Computing, 2024. 27(4): p. 4385–4413.
[35] Steel Industry Energy Consumption Dataset. UCI Machine Learning Repository. Available: https://archive.ics.uci.edu/dataset/851/steel+industry+energy+consumption.
[36] Shahpar, Z. and M. Badragheh, Presenting a hybrid model based on machine learning and principal component analysis for attack detection in the Internet of Things. Journal of Information and Communication Technology, 2026. 17(66–67): p. 178–188. Available from: https://jour.aicti.ir/en/Article/49811.