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

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.