Maximizing energy efficiency in wireless sensor networks for data transmission: A Deep Learning-Based Grouping Model approach

Wireless Sensor Networks (WSNs) are widely studied for their data collection and monitoring capabilities across diverse applications. However, the limited energy resources of sensor nodes present a significant challenge in extending the network's lifespan. To overcome this, we introduce a Deep Learning based Grouping Model Approach (DL-GMA) that optimizes energy usage in WSNs. DL-GMA employs advanced deep learning techniques, particularly Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), to enhance energy efficiency through effective cluster formation, Cluster Head (CH) selection, and CH maintenance. Evaluation using key metrics-Energy Efficiency (88.7 %), Network Stability (90.8 %), Network Scalability (87.1 %), Congestion Level (18.3 %), and Quality of Service (QoS) (93.4 %)-demonstrates the effectiveness of DL-GMA in energy utilization optimization and overall network performance. Incorporating deep learning and intelligent grouping, our approach extends WSN lifespan and improves data transmission efficiency. DL-GMA represents a significant advancement in energy optimization for WSNs, addressing the challenges of limited energy resources and maximizing the network's potential while improving data transmission efficiency.