Challenges in Adaptive and Explainable Anomaly Detection for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) serve as the backbone of modern intelligent ecosystems, finding utility in diverse sectors ranging from clinical healthcare to industrial automation and define surveillance. However, their decentralized structure and reliance on open communication channels render them susceptible to sophisticated cyber threats, hardware malfunctions, and data corruption. While traditional statistical models often struggle with the dynamic nature of these threats, modern deep learning architectures—such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks—have bolstered detection capabilities. Despite these gains, significant hurdles remain regarding high false-alarm rates, poor adaptability to non-stationary data, and the "black-box" nature of AI decisions. This paper explores the integration of Adaptive Learning and Explainable AI (XAI) as essential strategies to develop transparent, resilient, and energy-efficient security frameworks for next-generation WSNs.