DeepCuckoo: A Synergistic Approach Using Deep Learning and Bioinspired Cuckoo Search for Optimized Energy‐Efficient Cluster Head Selection in 5G and Advanced Wireless Sensor Networks

ABSTRACT The exponential growth of Internet of Things (IoT) devices has intensified the demand for efficient data transmission in wireless sensor networks (WSNs), particularly in 5G and beyond technology. This study introduces DeepCuckoo, a novel hybrid framework that synergizes deep learning with bioinspired optimization to enhance energy‐efficient cluster head (CH) selection in WSNs. Leveraging deep belief networks (DBNs) for unsupervised clustering, the framework classifies sensor nodes based on energy levels and proximity. Concurrently, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) dynamically evaluate residual energy, distance, and node capacity to optimize CH selection. Further refinement is achieved through the cuckoo search (CS) algorithm, which optimizes routing paths to minimize energy consumption and balance the network load. Simulation results demonstrate that DeepCuckoo achieves up to 55% energy savings, significantly outperforming existing methods such as EPOA‐CHS and E‐CERP. The framework also extends the network lifetime by 36% and reduces the latency by 50%, ensuring reliable data transmission in dynamic environments. By integrating adaptive learning with bioinspired optimization, this study advances the scalability and resilience of WSNs, offering a robust solution for future IoT ecosystems.