Deep Learning and Optimization Approaches in Dynamic Path-Controllable Deep Unfolding Network to Predict K-Barriers for Intrusion Detection Using Wireless Sensor Networks: A Review

Abstract

Wireless Sensor Networks (WSNs) play a critical role in surveillance, border monitoring, and intrusion detection applications. One of the fundamental challenges in WSN-based intrusion detection is ensuring complete coverage and fast detection of intruders using limited sensor resources. The concept of K-barriers has emerged as an effective metric to guarantee multiple layers of intrusion detection, ensuring that an intruder must cross at least K sensing barriers before reaching a protected region. Recent advancements in deep learning, particularly dynamic path-controllable deep unfolding networks, have shown significant potential in predicting K-barrier configurations and enhancing intrusion detection accuracy. These models integrate optimization techniques and neural network architectures to learn complex spatial and temporal relationships within sensor deployments. This review explores state-of-the-art deep learning and optimization approaches for K-barrier prediction and intrusion detection in WSNs. It focuses on methods such as feed-forward neural networks, CNN-LSTM hybrids, reinforcement learning, and deep unfolding techniques. The study highlights how these approaches improve detection accuracy, reduce false alarms, and optimize sensor deployment strategies. Additionally, the paper identifies key research challenges, including computational complexity, scalability, and real-time adaptability in dynamic environments. Future directions such as lightweight models and edge intelligence are also discussed. This review provides a comprehensive foundation for researchers working on intelligent intrusion detection in WSNs.