A Survey of Methods and Architectures for Deep Convolutional U-Shape Network with Jump Attention-Based Vision Transformer for Integrated Sequence Scheduling and Trajectory Planning with Obstacle Avoidance in Wireless Rechargeable Sensor Networks

Abstract

Wireless Rechargeable Sensor Networks (WRSNs) represent a significant advancement over traditional Wireless Sensor Networks by addressing energy limitations through mobile charging and intelligent scheduling. These systems improve network lifetime and efficiency but introduce complex challenges in sequence scheduling, trajectory planning, and obstacle avoidance, especially in dynamic and resource-constrained environments. Recent progress in deep learning has provided effective solutions to these problems. Convolutional Neural Networks (CNNs), particularly U-shaped architectures like U-Net, excel in spatial feature extraction and segmentation, while Vision Transformers (ViTs) enhance the modelling of long-range dependencies using attention mechanisms. Hybrid models such as TransUNet and Swin-Unet combine the strengths of both approaches, enabling improved decision-making for trajectory optimization and path planning. This survey reviews state-of-the-art techniques, categorizing them into CNN-based, transformer-based, hybrid, and reinforcement learning approaches. It also examines optimization strategies, including multi-objective algorithms and attention-based methods, which enhance adaptability and efficiency in WRSNs. Despite these advancements, challenges such as high computational complexity, scalability issues, real-time deployment constraints, and data dependency persist. The study highlights future research directions, emphasizing the need for lightweight models, integration with edge computing, and the development of hybrid intelligent optimization frameworks to achieve scalable, efficient, and real-time WRSN operations.