Efficient QoS‐Aware and Secure Routing in WSN With IoT Devices Using Snow Geese Optimized Gates‐Controlled Deep Unfolding Single‐Head Vision Transformer Network

ABSTRACT Wireless Sensor Networks (WSNs) are a vital component in the Internet of Things (IoT) infrastructure for the purpose of real‐time data gathering from varied and dispersed sensing areas. Security, energy efficiency, and maintenance of Quality of Service (QoS) in the event of resource scarcity are, however, a vital challenge. The conventional routing structures do not possess adaptive intelligence and lightweight cryptography capabilities to adapt to dynamic, high‐density IoT scenarios. To overcome this, the current work proposes a novel architecture called Efficient QoS‐Aware and Secure Routing in WSN with IoT Devices Using Snow Geese Optimized Gates‐controlled Deep Unfolding Single‐Head Vision Transformer Network (SGO‐GcDUN‐SiHViT). The architecture starts with node deployment using a Bi‐Concentric Hexagonal (Bi‐Hex) model and utilizing Honey Badger–Horse Herd Optimization Algorithm (HB‐HHOA) for energy‐efficient clustering. Sensor information is fused by the Fuzzy Min‐Max Network (FM‐MN) and encrypted using Lightweight Attribute‐based Encryption (LAE). For improved route discovery, a deep learning model based on Gates‐controlled Deep Unfolding Network (GcDUN) and Single‐Head Vision Transformer (SiHViT) is optimized by Snow Geese Optimization (SGO). Finally, the Private Blockchain Voting Mechanism (PBVM) is employed for secure IoT user authentication. The experimental results show that the proposed model yields a high hash rate of 932 ops/s, low encryption time of 1.3 s, security strength of 99.3%, and routing overhead as low as 11.2%. Moreover, improvements in all aforementioned variables were statistically confirmed using ANOVA, showing p = 0.0001, and Cohen's d , which was 1.52, proving the superiority of the system in secure and efficient IoT‐WSN communication.