RETRACTED ARTICLE: Deep learning-based algorithm for optimum cluster head selection in sustainable wireless communication system

The wireless sensor nodes of the network gather environmental data while using their batteries to communicate with the base station. When sharing data, the sensor nodes consume more energy, which shortens the network lifetime. Energy efficiency is a crucial factor to think about while building sensor networks. The clustering technique's main goal is to carry out the most energy-efficient data transmission possible while simultaneously increasing the network's useful life. The optimal cluster head selection in sustainable wireless communication systems is proposed in this research to find the best cluster head selection for the enhanced deep neural network approach. The cluster heads are best located using this technique, which also improves the firefly's general search activity. Throughput, energy left over, and the number of active nodes are measured when evaluating the suggested methodology. The findings indicate a rise in network longevity, which boosts the quantity of active nodes and lowers energy consumption. Through comparison, it was found that the proposed methodology beats the firefly algorithm in terms of throughput and leftover energy.