An energy-aware federated intelligence framework for sustainable WSN-IoT ecosystems

With the rapid development of Internet of Things (IoT), the deployment of Wireless Sensor Networks (WSNs) as vital infrastructure for environmental monitoring, industrial automation, precision agriculture, and smart city applications has increased significantly. Despite this, WSN-IoT ecosystems are still hindered by the issues of limited energy, communication overhead, scalability, preserving privacy, and learning in an intelligent decentralized fashion. To overcome these challenges, this study suggests a federated energy-aware intelligence framework namely EcoSense-AI for sustainable WSN-IoT ecosystems. It combines Energy-Weighted Federated Learning, Adaptive Differential Privacy, Energy-Aware Graph Neural Network (EA-GNN)-based topology modeling, and multi-objective resource optimization, to facilitate secure, efficient, and adaptive edge-cloud collaborative intelligence. The effectiveness of EcoSense-AI was tested with a simulated WSN-IoT network of heterogeneous sensor nodes operating with different residual energy levels, communication ranges and moving network conditions. Distributed environmental sensing data in the device, edge and cloud layer were subjected to experimental analysis. The proposed framework was compared with FedAvg and FedProx for equal training rounds, communication setup and in energy constrained deployment settings. The accuracy, energy efficiency, privacy score, communication cost reduction and network lifetime extension were used as evaluation metrics to measure performance. The experimental results show that the proposed method, EcoSense-AI outperforms the baseline methods with 98.56% accuracy, 93.2% energy efficiency, and 0.97 privacy score, and increased network lifetime by 58.9% and decreased communication cost. The proposed framework for sustainable intelligent sensing and decentralized learning in next-generation WSN-IoT applications is proved to be effective through the results.