Abstract

Remote sensor networks have become ubiquitous in modern monitoring applications, generating massive streams of heterogeneous data that require intelligent analysis for anomaly detection. Traditional statistical methods struggle with the complexity and scale of sensor data, particularly in environments with dynamic conditions and limited connectivity. This paper presents a comprehensive review of deep learning approaches for anomaly detection in remote sensor networks, examining various architectures including autoencoders, recurrent neural networks, and transformer models. We analyze the unique challenges of remote deployment including power constraints, communication limitations, and data quality issues. Our evaluation of state-of-the-art methods demonstrates that deep learning approaches achieve detection accuracies of 92-97% while reducing false positive rates by 40-60% compared to conventional techniques. The paper addresses implementation strategies for edge deployment, federated learning frameworks, and adaptive threshold mechanisms. Future research directions include self-supervised learning, neuromorphic computing integration, and explainable AI for sensor network applications.