Open Access

ARTICLE

Enhancing IoT Resilience at the Edge: A Resource-Efficient Framework for Real-Time Anomaly Detection in Streaming Data

Kirubavathi G.^1,*, Arjun Pulliyasseri¹, Aswathi Rajesh¹, Amal Ajayan¹, Sultan Alfarhood^2,*, Mejdl Safran², Meshal Alfarhood², Jungpil Shin³

1 Department of Mathematics, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India
2 Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia
3 School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, 965-0006, Japan

* Corresponding Authors: Kirubavathi G.. Email: ; Sultan Alfarhood. Email:

Computer Modeling in Engineering & Sciences 2025, 143(3), 3005-3031. https://doi.org/10.32604/cmes.2025.065698

Received 19 March 2025; Accepted 29 May 2025; Issue published 30 June 2025

Abstract

The exponential expansion of the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Transportation Management of Things (TMoT) produces vast amounts of real-time streaming data. Ensuring system dependability, operational efficiency, and security depends on the identification of anomalies in these dynamic and resource-constrained systems. Due to their high computational requirements and inability to efficiently process continuous data streams, traditional anomaly detection techniques often fail in IoT systems. This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems. Extensive experiments were carried out on multiple real-world datasets, achieving an average accuracy score of 96.06% with an execution time close to 7.5 milliseconds for each individual streaming data point, demonstrating its potential for real-time, resource-constrained applications. The model uses Principal Component Analysis (PCA) for dimensionality reduction and a Z-score technique for anomaly detection. It maintains a low computational footprint with a sliding window mechanism, enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data. The system uses a Multivariate Linear Regression (MLR) based imputation technique that estimates missing or corrupted sensor values, preserving data integrity prior to anomaly detection. The suggested solution is appropriate for many uses in smart cities, industrial automation, environmental monitoring, IoT security, and intelligent transportation systems, and is particularly well-suited for resource-constrained edge devices.

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Keywords

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