Next Generation AI Powered Framework for Autonomous Energy Optimization and Real Time Anomaly Detection in IoT Driven Wireless Sensor Networks

The exponential growth of Internet of Things (IoT) ecosystems and Wireless Sensor Networks (WSNs) has created unprecedented challenges in energy efficiency, real-time reliability, and anomaly detection. LEACH, HEED, and Support Vector Machines (SVMs) are traditional routing and fault detection systems which provide partial solutions, but cannot be scaled to larger networks, nor adapted to changing conditions, nor consume less energy. To cover such problems, we suggest Next-Generation AI-Powered Framework of Autonomous Energy Optimization and Real-Time Anomaly Detection in IoT-Based WSNs. The design uses a layered architecture (sensor-edge-fog-cloud) to give it modularity and scalability. Preprocessing of data is done by the methods of normalization and imputation to assure high quality input. An Energy-Aware Graph Neural Network (GNN) is used to achieve energy optimization which is combined with dynamic duty cycling, adaptive power control, and clustering. To detect anomalies, a hybrid model of LSTM Autoencoder-Bayesian Network model is used, which involves the use of both the temporal pattern recognition and probabilistic reasoning. It is shown that the network lifetime is increased by 45 - 50 % and the anomaly detection engine has a 97.8 % accuracy, 97.2 % precision, and 97.0 % F1-score, which is far superior to base methods. Medium-scale deployments have latency that is maintained to less than 120 ms to be responsive. The trade-off optimization engine strikes a balance between the energy use and the detection performance by means of Pareto-front strategies. Moreover, Federated Learning using Blockchain ensures privacy-preservation, safe updates of the model and attains accuracy of 97.6 % with a 99 % trust validation. The suggested framework provides a scalable, energy-saving, and secure platform in mission-critical IoT applications, such as smart grids, medical, and industrial automation.