Recent Advances in an Optimized Causal Dilated Convolutional Neural Networks-Based Energy-Efficient and Delay-Sensitive Routing Paths Using Mobility Prediction in Mobile WSN: A Systematic Review

Abstract

Wireless Sensor Networks (WSNs) have become a fundamental component of modern communication systems, particularly in applications such as environmental monitoring, smart cities, and healthcare. However, the dynamic topology, limited energy resources, and high mobility in Mobile WSNs (MWSNs) pose significant challenges in achieving energy-efficient and delay-sensitive routing. Traditional routing protocols often fail to adapt to dynamic mobility patterns and network conditions. Recent advancements in Artificial Intelligence (AI), particularly deep learning techniques such as causal dilated convolutional neural networks (CD-CNNs), have shown promising potential in addressing these challenges. This paper presents a systematic review of AI-based routing techniques focusing on energy efficiency, delay minimization, and mobility prediction in MWSNs between 2020 and 2023. The review emphasizes hybrid deep learning architectures that integrate causal and dilated convolutions to capture temporal dependencies and long-range correlations in network traffic and node mobility. Studies indicate that deep learning models, especially CNN and temporal convolutional networks, can effectively extract spatial–temporal features and improve routing decisions. For instance, convolutional models combined with attention mechanisms and recurrent structures enhance prediction accuracy and optimize resource utilization. Furthermore, mobility prediction plays a crucial role in improving routing reliability by forecasting node movement and preventing link failures. The integration of predictive models with routing protocols significantly reduces packet loss, latency, and energy consumption. Despite these advancements, challenges such as computational overhead, scalability, and real-time adaptability remain critical. This review provides a comprehensive analysis of recent techniques, highlights research gaps, and suggests future directions for developing efficient AI-driven routing protocols in mobile WSN environments.