Swin‐Decision Transformer: A Transformer‐Based Hybrid Protocol for Adaptive Clustering and Energy‐Efficient Routing in Large‐Scale <scp>WSNs</scp>

ABSTRACT In this era, large‐scale Wireless Sensor Networks (WSNs) provide high Quality of Service (QoS) with energy awareness and scalability. Specifically, existing clustering protocols lack adaptability to evolving networks owing to static cluster formation, data‐centric greedy protocols, and their handling of clustering aggregators. To resolve these issues in WSNs, a transformer‐based hybrid protocol using a swin transformer for clustering and a decision transformer for energy‐efficient routing is proposed, which is called a Swin‐Decision Transformer (SDT). Hence, this research employs five main modules. The first module incorporates a Gated Graph Convolutional Network (GGCN) for distributed cluster formation that utilizes the topology of each sensor node. The second module utilizes a Swin Transformer to select a context‐aware Cluster Head (CH), scoring on attention for energy, Signal to Noise Ratio (SNR), and load. Furthermore, the third module uses a transformer‐based path planner to route the Mobile Data Collector (MDC) to an optimal route for CH visitation. Furthermore, the Decision Transformer is utilized to dynamically route the CH rather than define a path to the Base Station (BS), which assists in the optimization of CH‐aided positioning based on learning QoS‐optimized trajectories. Ultimately, reinforcement learning is employed in a single change loop to continuously adjust the model parameters. Thus, the simulations demonstrate that SDT improves residual energy, reduces latency, and enhances throughput compared with MDC protocols, which reduce routing overhead by up to 40%.