A clustering model for data transmission based on deep learning for energy efficiency enhancement in wireless sensor networks

Wireless Sensor Networks (WSNs) are intensively explored because they can collect and analyze data in numerous applications. However, sensor nodes' limited energy resources hinder network durability. We propo se using Deep Learning-based Grouping Model Approach (DL-GMA) to reduce energy consumption in Wireless Sensor Networks (WSNs). Advanced deep learning approaches like Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) optimize cluster formation, select Cluster Heads (CH), and maintain them to increase energy efficiency in DL-GMA. Energy Efficiency (88.7%), Network Stability (90.8%), Network Scalability (87.1%), Congestion Level (18.3%), and Quality of Service (QoS) (93.4%) show that DL-GMA optimises energy consumption and improves network performance. Deep learning and intelligent grouping lengthen WSN lifespan and enhance data transmission efficiency in our solution. DL-GMA improves wireless sensor network energy optimization by increasing data transmission efficiency and resolving energy resource constraints.