Mobile Ad-hoc Networks (MANETs) play a crucial role in defense, disaster relief, and autonomous operations but remain highly exposed to threats such as blackhole, wormhole, and Sybil due to their decentralized topology, while traditional centralized trust mechanisms collapse under dynamic scenarios. This work presents the Blockchain-Fick Gradient Model for Secure MANET Routing and Threat Analytics (FiGRO-CoDpAT), combining blockchain consensus, gradient-based routing, and intelligent intrusion detection. The process begins with Network Initialization using Converged Blockchain Media Consensus (Co-BM-Co) for decentralized node verification. Fick’s Gradient Route Optimizer (FiGRO) then establishes congestion-free, attack-resistant routing. Following this, intrusion detection is performed through the Cosine Dual Phase Aggregator Transformer (CoDpAT), merging Cosine Convolutional Neural Network (CoCNN) and Dual Phase Aggregator Transformer (DpAT) for accurate packet analysis. Blockchain Trust Updates consistently maintain node credibility, while the Mountaineering Team Adaptive Optimizer (MtAO) enhances network efficiency in fluctuating topologies. Simulation findings prove the framework’s effectiveness, reaching an Accuracy of 99.5%, a Packet Delivery Ratio of 99.6%, a Packet Loss of only 0.4%, and a very low delay of 99.72 ms. In summary, FiGRO-CoDpAT provides secure, adaptive, and efficient communication in hostile MANET conditions.

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.