Enhancing Energy Efficiency in Wireless Sensor Networks Through Deep Learning Approaches

Wireless Sensor Networks (WSNs) have gained widespread adoption in various applications, including environmental monitoring, healthcare, and industrial automation. However, the energy constraints of sensor nodes pose significant challenges, limiting the network’s operational lifespan. Traditional energy optimization techniques often fall short in dynamically managing energy consumption while ensuring network performance. In recent years, deep learning has emerged as a powerful tool for optimizing energy efficiency in WSNs, offering intelligent decision-making capabilities for adaptive energy management. This paper explores the role of deep learning in energy optimization for WSNs, covering various techniques, datasets, experimental setups, and performance evaluation metrics. A comparative analysis of different deep learning models highlights their effectiveness in minimizing energy consumption while maintaining essential network parameters such as packet delivery ratio, latency, and throughput. Additionally, key challenges and future research directions are discussed, emphasizing the need for lightweight, scalable, and secure deep learning models. The findings suggest that integrating advanced deep learning techniques with edge computing and federated learning can significantly enhance WSN performance and sustainability