Abstract

Remote sensor networks (RSNs) have become ubiquitous in modern applications ranging from environmental monitoring to industrial automation. However, the detection of anomalies in these networks remains a critical challenge due to the distributed nature of sensors, communication constraints, and the need for real-time processing. This paper presents a comprehensive review of deep learning approaches for anomaly detection in remote sensor networks, analyzing current methodologies, challenges, and future directions. We examine various deep learning architectures including autoencoders, recurrent neural networks, and hybrid models, evaluating their effectiveness in detecting different types of anomalies in sensor data.