Call for Paper - Upcoming Issues

A Streaming IRFCM Framework for Real-Time Anomaly Detection in IoT Sensor Networks

International Journal of Electrical and Electronics Engineering
© 2026 by SSRG - IJEEE Journal
Volume 13 Issue 2
Year of Publication : 2026
Authors : Sumrana Siddiqui, Nandita Bhanja Chaudhuri
10.14445/23488379/IJEEE-V13I2P113
pdf
How to Cite?

Sumrana Siddiqui, Nandita Bhanja Chaudhuri, "A Streaming IRFCM Framework for Real-Time Anomaly Detection in IoT Sensor Networks," SSRG International Journal of Electrical and Electronics Engineering, vol. 13,  no. 2, pp. 160-171, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I2P113

Abstract:

The proliferation of Internet of Things (IoT) devices has led to unprecedented streams of high-dimensional, noisy sensor data that require advanced clustering and anomaly detection techniques. This study proposes a novel Streaming Integrated Robust Fuzzy Clustering Module (S-IRFCM), extending the IRFCM framework to enable adaptive, real-time soft clustering and anomaly detection in large-scale IoT sensor networks. S-IRFCM integrates online dimensionality reduction, incremental centroid updating, and dynamic outlier trimming for robust, efficient, and interpretable clustering. Experimental evaluation demonstrates superior robustness in noisy, drifting environments where standard streaming baselines fail, alongside orders-of-magnitude faster processing than batch fuzzy methods. These results confirm that S-IRFCM achieves scalable, stable clustering in dynamic and noisy big data environments, providing a new paradigm for IoT-enabled analytics applications.

Keywords:

Streaming Clustering, IRFCM, IoT Anomaly Detection, Soft Clustering, Online PCA, Outlier Trimming, High-Dimensional Data, Scalability, Sensor Fault Detection, Data Streams, Adaptive Clustering.

References:

[1] Hassan Sh. Alshehri, and Fuad Bajaber, “A Cluster-based Data Aggregation in IoT Sensor Networks using the Firefly Optimization Algorithm,” Journal of Computer Networks and Communications, vol. 2024, no. 1, pp. 1-17, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Prabhat Das, and Dibya Jyoti Bora, “IoT Data Analysis using Different Clustering Algorithms,” ECS Transactions, vol. 107, no. 1, pp. 5721-5728, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Sivadi Balakrishna, and M. Thirumaran, Semantics and Clustering Techniques for IoT Sensor Data Analysis: A Comprehensive Survey, Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm, Springer, Cham, pp. 103-125, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Abhishek Bajpai, Harshita Verma, and Anita Yadav, “Optimizing Data Aggregation and Clustering in Internet of Things Networks using Principal Component Analysis and Q-Learning,” Data Science and Management, vol. 7, no. 3, pp. 189-196, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Charu C. Aggarwal et al., “A Framework for Clustering Evolving Data Streams,” Proceedings 2003 VLDB Conference, pp. 81-92, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Marcel R. Ackermann et al., “StreamKM++: A Clustering Algorithm for Data Streams,” Journal of Experimental Algorithmics (JEA), vol. 17, pp. 1-30, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[7] James C. Bezdek, Robert Ehrlich, and William Full, “FCM: The Fuzzy C-means Clustering Algorithm,” Computers and Geosciences, vol. 10, no. 2-3, pp. 191-203, 1984.
[CrossRef] [Google Scholar] [Publisher Link]
[8] R. Krishnapuram, and James M. Keller, “A Possibilistic Approach to Clustering,” IEEE Transactions on Fuzzy Systems, vol. 1, no. 2, pp. 98-110, 1993.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Moksud Alam Mallik et al., “An Efficient Fuzzy C-Least Median Clustering Algorithm,” IOP Conference Series: Materials Science and Engineering, vol. 1070, no. 1, pp. 1-11, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Nikhil R. Pal et al., “A Possibilistic Fuzzy C-Means Clustering Algorithm,” IEEE Transactions on Fuzzy Systems, vol. 13, no. 4, pp. 517-530, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Manish Gupta et al., “Outlier Detection for Temporal Data: A Survey,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 9, pp. 2250-2267, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Harsh Chhajer, and Rahul Roy, “Rationalised Experiment Design for Parameter Estimation with Sensitivity Clustering,” Scientific Reports, vol. 14, no. 1, pp. 1-15, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Tianmou Liu, Han Yu, and Rachael Hageman Blair, “Stability Estimation for Unsupervised Clustering: A Review,” Wiley Interdisciplinary Reviews: Computational Statistics, vol. 14, no. 6, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Baotong Chen et al., “Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges,” IEEE Access, vol. 6, pp. 6505-6519, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Maciej Jaworski, Piotr Duda, and Lena Pietruczuk, “On Fuzzy Clustering of Data Streams with Concept Drift,” Artificial Intelligence and Soft Computing: 11th International Conference, ICAISA, Zakopane, Poland, vol. 7268, pp. 82-91, 2012. [CrossRef] [Google Scholar] [Publisher Link]
[16] Shaoxu Song et al., “On Saving Outliers for Better Clustering over Noisy Data,” SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data, pp. 1692-1704, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Takanori Fujiwara et al., “An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 418-428, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Sara Mostafavi, and Ali Amiri, “Extending Fuzzy C-Means to Clustering Data Streams,” 20th Iranian Conference on Electrical Engineering (ICEE2012), Tehran, Iran, pp. 726-729, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[19] P. Hore et al., “Online Fuzzy C Means,” NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society, New York, USA, pp. 1-8, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Divyansh Agrawal, Intel Berkeley Research Lab Data, Kaggle, 2004. [Online]. Available: https://www.kaggle.com/datasets/divyansh22/intel-berkeley-research-lab-sensor-data
[21] Iman Sharafaldin et al., “Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy,” 2019 International Carnahan Conference on Security Technology (ICCST), Chennai, India, pp. 1-8, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Mohamed Amine Ferrag et al., “Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning,” IEEE Access, vol. 10, pp. 40281-40306, 2022.
[CrossRef] [Google Scholar] [Publisher Link]