A Survey of Methods and Architectures for Optimized Causal Dilated Convolutional Neural Networks-Based Energy-Efficient and Delay-Sensitive Routing Paths Using Mobility Prediction in Mobile WSN

Abstract

Mobile Wireless Sensor Networks (MWSNs) have become an essential component of modern communication systems, supporting applications such as environmental monitoring, healthcare, smart cities, industrial automation, and intelligent transportation systems. However, the dynamic nature of MWSNs, including node mobility, limited battery power, changing network topology, and latency constraints, creates major challenges for designing reliable and efficient routing protocols. Achieving both energy efficiency and delay-sensitive communication is particularly important because these factors directly influence network lifetime, stability, packet delivery, and overall system performance. Traditional routing protocols based on clustering, shortest-path algorithms, and metaheuristic optimization techniques improve energy management but often fail to adapt effectively to dynamic mobility patterns and rapidly changing network conditions. In recent years, artificial intelligence and deep learning approaches have provided new opportunities for intelligent routing optimization in MWSNs. Causal dilated convolutional neural networks (CDCNNs) have gained significant attention due to their ability to capture temporal dependencies and long-range correlations in sequential network data. These models support accurate mobility prediction and proactive routing decisions, reducing packet loss, minimizing communication delay, and improving energy utilization. AI-driven routing frameworks integrating machine learning, reinforcement learning, and optimization strategies have demonstrated superior performance compared to conventional methods. This survey reviews recent methods and architectures for energy-efficient and delay-sensitive routing in mobile WSNs, focusing on CDCNN-based mobility prediction frameworks while highlighting advancements, challenges, and future research opportunities in intelligent wireless sensor network routing.