IRO Journals
Journal of Trends in Computer Science and Smart Technology

MVRODC: Multi-Variate Regression based Outlier Detection and Classification on IoT Sensor Data — A Temporal Node Level Approach

Open Access
Published: 05 June, 2026
Pages: 429-451
  • Veera Brahmam M. , Veera Brahmam M.
    School of Computer Science and Engineering, VIT-AP University, Amaravathi, Andhra Pradesh, India
    School of Computer Science and Engineering, VIT-AP University, Amaravathi, Andhra Pradesh, India
  • Gopikrishnan S. Gopikrishnan S.
    School of Computer Science and Engineering, VIT-AP University, Amaravathi, Andhra Pradesh, India
    School of Computer Science and Engineering, VIT-AP University, Amaravathi, Andhra Pradesh, India
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How to Cite

M., Veera Brahmam, and Gopikrishnan S. 2026. “MVRODC: Multi-Variate Regression Based Outlier Detection and Classification on IoT Sensor Data — A Temporal Node Level Approach”. Journal of Trends in Computer Science and Smart Technology 8 (3): 429-51. https://doi.org/10.36548/jtcsst.2026.3.001.
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Keywords

Internet of Things
Outliers
Errors
Events
Detection Rate
Multiple Linear Regression

Abstract

The reliability and correctness of data detected by sensors are essential for the efficient use of Internet of Things (IoT) and Wireless Sensor Network (WSN) technologies. However, sensor readings are often affected by errors due to hardware failure or actual environmental events. This causes outliers that can affect decision-making and system efficiency. To address these problems, the present study proposes a novel outlier detection and classification technique called Multivariate Regression-based Outlier Detection and Classification (MVRODC). MVRODC uses similarity measures derived from Multiple Linear Regression (MLR) along with an adaptive buffer to model temporal relationships. This ensures that outliers are detected and classified into two different categories in real-time into two categories:  errors and actual events. Inter-sensor feature correlations across multiple sensor streams (temperature, humidity, air quality, and light) are exploited along with temporal prediction consistency to enable robust real-time outlier detection and classification. The MVRODC technique ensures that relevant outliers caused by actual events are retained, allowing for the detection of environmental changes while ignoring erroneous data. This filtering technique saves energy because sending erroneous data consumes as much energy as sending legitimate data. Experimentally, MVRODC performs better than existing outlier detection techniques, achieving superior results in terms of detection rate, false alarm rate, accuracy, error detection rate, and event detection rate.

References

  1. Zafeirelli, Sofia, and Dimitris Kavroudakis. "Comparison of Outlier Detection Approaches in a Smart Cities Sensor Data Context." International Journal on Smart Sensing and Intelligent Systems 1 (2024).
  2. Garca, J. C., Luis A. Rivera, and Jonny Perez. "A Literature Review on Outlier Detection in Wireless Sensor Networks." Journal of Advances in Information Technology 15, no. 3 (2024).
  3. Xue, Puning, Luyang Shi, Zhigang Zhou, Jing Liu, and Xin Chen. "An Online Fault Detection and Diagnosis Method of Sensors in District Heating Substations Based on Long Short-Term Memory Network and Adaptive Threshold Selection Algorithm." Energy and Buildings 308 (2024): 114009.
  4. Alduais, Nayef Abdulwahab Mohammed, Jiwa Abdullah, and Ansar Jamil. "RDCM: An Efficient Real-Time Data Collection Model for IoT/WSN Edge with Multivariate Sensors." IEEE Access 7 (2019): 89063-89082.
  5. Al Samara, Mustafa, Ismail Bennis, Abdelhafid Abouaissa, and Pascal Lorenz. "Enhanced Efficient Outlier Detection and Classification Approach for WSNs." Simulation Modelling Practice and Theory 120 (2022): 102618.
  6. Al Samara, Mustafa, Ismail Bennis, Abdelhafid Abouaissa, and Pascal Lorenz. "Complete Outlier Detection and Classification Framework for WSNs Based on OPTICS." Journal of Network and Computer Applications 211 (2023): 103563.
  7. Hu, Zhichao, Xiangzhan Yu, Likun Liu, Yu Zhang, and Haining Yu. "ASOD: An Adaptive Stream Outlier Detection Method Using Online Strategy." Journal of Cloud Computing 13, no. 1 (2024): 120.
  8. Dani, Yasi, Agus Yodi Gunawan, Masayu Leylia Khodra, and Sapto Wahyu Indratno. "Detecting Outliers Using Modified Recursive Pca Algorithm for Dynamic Streaming Data." In MENDEL, vol. 29, no. 2, 2023, 237-244.
  9. Mazarei, Arefeh, Ricardo Sousa, João Mendes-Moreira, Slavo Molchanov, and Hugo Miguel Ferreira. "Online Boxplot Derived Outlier Detection." International journal of data science and analytics 19, no. 1 (2025): 83-97.
  10. Shu, Zhinian, and Xiaorong Li. "The Detection Method of Continuous Outliers in Complex Network Data Streams Based on C-LSTM." International Journal of System Assurance Engineering and Management 15, no. 9 (2024): 4582-4593.
  11. Abhaya, Abhaya, and Bidyut Kr Patra. "An Efficient Method for Autoencoder Based Outlier Detection." Expert Systems with Applications 213 (2023): 118904.
  12. Antonius, Franciskus, J. C. Sekhar, Vuda Sreenivasa Rao, Rahul Pradhan, S. Narendran, Ricardo Fernando Cosio Borda, and Susan Silvera-Arcos. "Unleashing the Power of Bat Optimized CNN-BiLSTM Model for Advanced Network Anomaly Detection: Enhancing Security and Performance in IoT Environments." Alexandria Engineering Journal 84 (2023): 333-342.
  13. Samara, Mustafa Al, Ismail Bennis, Abdelhafid Abouaissa, and Pascal Lorenz. "Sa-o2dca: Seasonal Adapted Online Outlier Detection and Classification Approach for WSN." Journal of Network and Systems Management 32, no. 2 (2024): 31.
  14. Malki, Amer, El-Sayed Atlam, and Ibrahim Gad. "Machine Learning Approach of Detecting Anomalies and Forecasting Time-Series of IoT Devices." Alexandria Engineering Journal 61, no. 11 (2022): 8973-8986.
  15. Lai, Trinh Thuc, Tuan Phong Tran, Jaehyuk Cho, and Myungsik Yoo. "DoS Attack Detection Using Online Learning Techniques in Wireless Sensor Networks." Alexandria Engineering Journal 85 (2023): 307-319.
  16. Rodríguez, Martha, Diana P. Tobón, and Danny Múnera. "A Framework for Anomaly Classification in Industrial Internet of Things Systems." Internet of Things 29 (2025): 101446.
  17. Krleža, Dalibor, Boris Vrdoljak, and Mario Brčić. "Statistical Hierarchical Clustering Algorithm for Outlier Detection in Evolving Data Streams." Machine Learning 110, no. 1 (2021): 139-184.
  18. Gupta, Umang, Vandana Bhattacharjee, and Partha Sarathi Bishnu. "Outlier Detection in Wireless Sensor Networks Based on Neighbourhood." Wireless Personal Communications 116, no. 1 (2021): 443-454.
  19. Singh, Manmohan, and Rajendra Pamula. "ADINOF: Adaptive Density Summarizing Incremental Natural Outlier Detection in Data Stream." Neural Computing and Applications 33, no. 15 (2021): 9607-9623.
  20. Pekşen, Muhammed Fatih, Ulaş Yurtsever, and Yılmaz Uyaroğlu. "Enhancing Electrical Panel Anomaly Detection for Predictive Maintenance with Machine Learning and IoT." Alexandria Engineering Journal 96 (2024): 112-123.
  21. Salilew, Waleligne Molla, Zainal Ambri Abdul Karim, and Tamiru Alemu Lemma. "Investigation of Fault Detection and Isolation Accuracy of Different Machine Learning Techniques with Different Data Processing Methods for Gas Turbine." Alexandria Engineering Journal 61, no. 12 (2022): 12635-12651.
  22. Wei, Yuanyuan, Julian Jang-Jaccard, Wen Xu, Fariza Sabrina, Seyit Camtepe, and Mikael Boulic. "LSTM-Autoencoder-Based Anomaly Detection for Indoor Air Quality Time-Series Data." IEEE Sensors Journal 23, no. 4 (2023): 3787-3800.
  23. Rollo, Federica, Chiara Bachechi, and Laura Po. "Anomaly Detection and Repairing for Improving Air Quality Monitoring." Sensors 23, no. 2 (2023): 640.
  24. Brahmam, M. Veera, and S. Gopikrishnan. "NODSTAC: Novel Outlier Detection Technique Based on Spatial, Temporal and Attribute Correlations on IoT Bigdata." The Computer Journal 67, no. 3 (2024): 947-960.
  25. Intel Berkeley Research Lab. “Intel Lab Data.” (2004).
  26. Chander, Bhanu, and G. Kumaravelan. "Outlier Detection Strategies for WSNs: A Survey." Journal of King Saud University-Computer and Information Sciences 34, no. 8 (2022): 5684-5707.
  27. Zhu, Rui, Xiaoling Ji, Danyang Yu, Zhiyuan Tan, Liang Zhao, Jiajia Li, and Xiufeng Xia. "KNN-Based Approximate Outlier Detection Algorithm over IoT Streaming Data." IEEE Access 8 (2020): 42749-42759.