Blockchain‐Integrated Cluster‐Based Q‐Value Regularized Bayesian Asymmetric Quantized Neural Network for Reliable Data Transmission in Wireless Sensor Networks

ABSTRACT Wireless sensor networks (WSNs) are widely utilized in remote and inaccessible environments to monitor and collect critical data. However, ensuring secure and efficient communication from sensor nodes to the base station remains a key challenge due to energy constraints and network vulnerabilities. Even with the advancement of numerous multipath routing protocols, it is still very difficult to achieve optimal energy consumption with low error rates and minimal end‐to‐end latency. Unreliable routing and hostile intrusions tend to reduce the overall network performance and lifetime, thus prompting the requirement of an intelligent, secure, and energy‐aware routing protocol. This work advances the Adaptive Blockchain‐based Q‐value Regularized Bayesian Asymmetric Quantized Neural Network with Pine Cone Optimization (Q‐RBANNet‐PCO) for cluster‐based energy‐efficient routing in WSNs. The model starts with Cluster Head (CH) selection using the Human Memory Optimization Algorithm (HMOA)‐based Cluster Head selection and uses Q‐RBANNet with PCO for identifying secure routing paths, which are combined with a decentralized ABSP‐HC blockchain layer. The method proposed has a PDR of 99.9%, packet loss of 0.1%, transmission latency of 0.6 s, and throughput of ~20 kbps, with the network lifetime lasting up to 1400 rounds. Therefore, Q‐RBANNet‐PCO appreciably enhances security, lowers energy consumption, and enhances scalability and reliability, making it appropriate for contemporary WSN applications in dynamic and critical environments.