A Wireless Sensor Network (WSN) is an efficient system for monitoring distributed areas and controlling environments; however, such networks are susceptible to malicious node attacks that bring forth network insecurity and untrustworthy data. WSNs are vulnerable to malicious nodes and cyber attackers that can interfere with data transmission, leading to compromised decision-making systems. Traditional security techniques against WSNs lack flexibility in real-time detection and data integrity because of constrained processing resources and vulnerabilities from centralized storage. This work aims to improve detection accuracy through a multi-stage strategy, which constitutes the general objective of this research. The presented model uses WSN-DS and WSN-BFSF datasets. The data are pre-processed using Localized-Global Depth Normalization for uniformity, followed by feature selection via Boosted Tern-Cat Hunting Optimization, which combines Cat Hunting Optimization and Boosted Sooty Tern techniques to reduce dimensionality. The attack detection is performed by a Parallel Triple Graph Attention-based Convolution Network, which employs Quantum Parallel Deep Convolution and Triple Graph Attention Networks. The RMRO optimizes the model's parameters to classify more accurately, and the benign data are safely stored through the Consensus-Aided PoA Decision Blockchain Engine and InterPlanetary File System. This approach achieved 99.4% accuracy, 99.3% recall, and 99.5% F1 score on the WSN-DS dataset and 99.2% accuracy, 99.1% precision, and 99.3% F1 score on the WSN-BFSF dataset while showing robustness across different combinations of sensors. Hence, the Tri-QPdCNet offers a pioneering approach toward securing WSNs from dynamic and persistent attacks by providing an improved framework for anomaly detection using a strong, scalable architecture, augmented with blockchain technology. That leads to more robust WSN infrastructures that can be more securely and smoothly deployed in real-time critical environments.

Precision agriculture relies on wireless sensor networks (WSNs) to support informed decision-making, thereby enhancing crop yields and resource management. A critical challenge in such networks is minimizing the energy consumption of sensor nodes while ensuring reliable data transmission. Sensor nodes are grouped using an optimal multi-objective clustering approach, which also chooses appropriate cluster heads (CH) for effective communication. By combining the exploration power of the Osprey Optimization Algorithm with the exploitation power of the Parrot Optimizer, a hybrid optimization approach improves CH selection. A hybrid deep learning framework, combining a convolutional autoencoder with a dual-key transformer network, is designed to monitor energy utilization and detect constraints affecting consumption. Training and testing performance of this framework is further improved using a metaheuristic based on the cooperative feeding and locomotion behavior of gooseneck barnacles. Experimental evaluation demonstrates superior performance, achieving 99.2% accuracy, 68 kbps throughput, 98% packet delivery ratio, and a network lifetime of 85 ms. With an average delay of 0.23 seconds, energy consumption is decreased to 39 J, demonstrating the effectiveness of the suggested strategy for dependable and sustainable precision agriculture applications.