A Survey on AI-Enabled Anomaly Detection with Privacy Preservation in Wireless Sensor Healthcare IoT Environment

The rapid integration of Wireless Sensor Networks (WSNs) and Internet of Things (IoT) in healthcare has enabled intelligent patient monitoring and real-time analytics. However, the handling of sensitive medical data raises significant challenges in privacy, security, and anomaly detection. Recent works have focused on using Artificial Intelligence (AI), particularly deep learning, to detect anomalies such as cyber intrusions, physiological irregularities, and device failures. Privacy-preserving mechanisms such as federated learning, differential privacy, and homomorphic encryption are widely adopted to safeguard data confidentiality during processing. Hybrid AI models—such as Deep Q-Learning, SCNN-BiLSTM, and quantum-enhanced deep learning—offer high detection accuracy with minimal data leakage. Additionally, lightweight deep learning frameworks, blockchain-based data security, and fusion-based threat detection have been shown to improve anomaly detection performance in distributed healthcare environments. These advances support real-time, secure, and scalable decision-making in IoMT and body area networks. This paper surveys these developments, comparing performance, privacy guarantees, and computational overhead. The synthesis aims to guide future research in building robust, privacy-aware AI-driven healthcare systems.