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

Abstract

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.

Keywords

• Deep Learning
• Wireless Sensor Networks (WSNs)
• Recurrent Neural Network (RNN)
• Network Lifetime
• Quality of Service (QoS)
• Long Short-Term Memory (LSTM)