Artificial Intelligence Techniques for Dynamic Path-Controllable Deep Unfolding Network to Predict the K-Barriers for Intrusion Detection using Wireless Sensor Networks: Trends and Challenges

Abstract

Wireless Sensor Networks (WSNs) have emerged as a critical technology for surveillance, border monitoring, and security-sensitive applications. However, due to their distributed architecture, limited processing capability, and deployment in hostile or unattended environments, WSNs are highly vulnerable to intrusions, malicious attacks, and data tampering. To address these challenges, Intrusion Detection Systems (IDS) play a vital role in maintaining network integrity, ensuring reliability, and enabling real-time threat detection and mitigation. In recent years, Artificial Intelligence (AI) techniques, particularly deep learning and optimization-based models, have significantly enhanced IDS performance in WSNs by enabling intelligent pattern recognition and adaptive decision-making. A promising research direction in this field is K-barrier prediction, which determines the number of disjoint sensing barriers required to effectively detect intruders crossing a monitored region. Accurate estimation of K-barriers improves coverage reliability and strengthens intrusion detection capability. Advanced models such as Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and hybrid deep learning architectures have been widely explored for efficient K-barrier prediction using features like sensor density, sensing radius, transmission range, and deployment topology. Additionally, Dynamic Path-Controllable Deep Unfolding Networks integrate iterative optimization with deep learning, enabling adaptive learning and computational efficiency. This paper reviews AI-driven intrusion detection techniques for WSNs, compares recent methodologies, and discusses challenges including energy efficiency, scalability, data imbalance, and real-time constraints, while highlighting future directions such as federated learning, edge intelligence, and explainable AI.