A Comprehensive Review of an Optimized Causal Dilated Convolutional Neural Network-Based Energy-Efficient and Delay-Sensitive Routing Paths Using Mobility Prediction in Mobile WSN

Abstract

Mobile Wireless Sensor Networks (MWSNs) play a vital role in modern communication systems, supporting applications such as environmental monitoring, healthcare, military surveillance, and smart cities. However, challenges such as node mobility, limited energy resources, and delay-sensitive data transmission make efficient routing a complex task. To address these issues, recent research has explored deep learning-based routing approaches, particularly Convolutional Neural Networks (CNNs) and Causal Dilated CNNs, which effectively capture spatial and temporal dependencies in network data. This paper presents a comprehensive review of optimized routing techniques that integrate mobility prediction with intelligent decision-making mechanisms to enhance energy efficiency and reduce latency in MWSNs. These hybrid frameworks utilize machine learning and deep learning models to predict node movement patterns, optimize routing paths, and improve network stability. Mobility prediction significantly enhances routing performance by reducing packet loss, minimizing delay, and increasing overall throughput. Predictive models have demonstrated high accuracy in estimating node mobility, leading to more reliable communication. Additionally, dilated convolution techniques enable capturing long-range dependencies without increasing computational overhead, making them suitable for resource-constrained environments. Optimization strategies, including bio-inspired algorithms and clustering methods, further improve energy balancing and routing efficiency. Overall, intelligent deep learning-based routing frameworks offer a promising solution for improving performance and extending the lifetime of MWSNs.