DEEP LEARNING APPROACHES FOR REAL-TIME ANOMALY IDENTIFICATION DETECTION IN IOT SENSOR NETWORKS: A REVIEW

Abstract

IoT sensor networks are becoming increasingly dependent on real-time anomaly detection and predictive analytics
technology to analyze continuous data streams in order to make trustworthy decisions in a timely manner. Deep learning offers
advanced techniques that can handle the scale and complexity of such data more effectively than traditional methods. This paper
gives a complete picture of how IoT-based predictive analytics and anomaly detection are enhanced using deep learning. The
capability of extracting hierarchical information, modelling temporal-spatial relationships, and identifying subtle anomalies in
real-time is displayed by models like the autoencoders, generative adversarial networks (GANs), transformers, etc. These methods
enhance the accuracy of forecasting, the system's resilience, and its adaptability across many domains, such as healthcare, industrial
IoT, and smart infrastructure. Despite this, and especially given scalability, computational requirements, and the limited
availability of labelled data, key challenges remain. Addressing these issues is critical in ensuring strong deployment in resource
constrained IoT systems. Future directions include light-weight architectures, privacy-preserving learning and explainable models
to drive towards the reliability and intelligence of IoT driven applications.