Anomaly Detection in Healthcare Monitoring Survey
Ayman A. Ali, Ahmed Ashraf, Kamel Hussien Rahouma · Practice, progress, and proficiency in sustainability · 2025
This chapter delves into the realm of anomaly detection in Wireless Sensor Networks (WSNs) and the Internet of Things (IoT), emphasizing their pivotal role in bolstering security. Focusing on diverse domains such as healthcare, environmental monitoring, and process industries, the chapter consolidates findings from various studies employing innovative anomaly detection techniques. One notable approach integrates supervised and unsupervised methods for continuous patient monitoring, showcasing successful anomaly detection in physiological variables using an autoencoder and XGBoost algorithm. The survey extends its scope to large-scale environmental sensing systems, where the proposed Anomaly Detection Framework demonstrates effectiveness in detecting emission events. Moreover, the paper explores sustainability initiatives, utilizing contextual anomaly detection in collaboration with Power smiths. The proposed algorithm, validated in simulation environments using historical data, exhibits promising real-time performance. An array of anomaly detection algorithms is presented, addressing challenges in diverse domains. These include a variance-based algorithm for sensor data, BRBAR for handling uncertain sensor data, anomaly detection in medical data, outlier detection in big sensor data, integration of SVM and YASA for activity recognition, density estimation for anomaly detection, and biomedical signal analysis. The survey concludes by highlighting future research directions, emphasizing the importance of addressing challenges in WSNs and IoT, such as resource constraints and collaboration with prevention-based techniques. Ongoing research aims to incorporate data stream mining techniques, apply anomaly detection methods to specific industries, and explore benchmark data selection for comprehensive evaluations. The taxonomy presented in the survey categorizes techniques, models, and architectures, providing a valuable guide for researchers and practitioners navigating the intricate landscape of anomaly detection in sensor systems. Open research inquiries pave the way for future investigations, contributing to the continuous evolution and improvement of anomaly detection methodologies.