Hybrid approach of Ant Colony system and Recurrent Neural network for Energy Efficient Routing in Cognitive Wireless Sensor Networks

Energy-aware communication in Cognitive Wireless Sensor Networks (CWSNs) was challenging with dynamic spectrum availability, high topology change rates, and low energy. Conventional routing techniques result in excessive energy usage and reduced network lifetimes. This work introduces an adaptive AI hybrid technique integrating Enhanced Ant Colony Optimization (EACO) for desirable choices in CH and sequence prediction neural network for energy pattern prediction and proactive optimization. The Decentralized Power-Aware Clustering (DPAC) protocol also optimizes energy distribution. Regarding energy efficiency, mean throughput, and network lifetime, our model outperformed existing systems. Simulations show that EACO + RNN greatly reduces energy use, improves the reliability of packet transmission, and extends the network's life by stopping early node failures. This hybridization of predictive AI and bio-inspired optimization guarantees scalable, energy-efficient CWSNs.