Literature Review On Energy Efficiency Wireless Sensor Net Works

Résumé: Energy efficiency in WSNs is a problem of resource optimization among sensor nodes that needs to meet the operational constraints for maximum network lifetime and performance. In essence, achieving optimal energy efficiency in WSNs means bing capable of balancing power consumptions, quality of data, and reliability of networks within various deployment scenarios. This issue has become increasingly complicated due to the rapid growth of IoT applications, heterogeneous sensor technologies, and dynamic operation environments. Because of this, it has become a topic of top priority for researchers who are working on the development of proficient methods so that energy management can be done easily and effectively by WSNs operators and designers. The present research work includes a bibliographic study on different approaches followed in WSN energy optimization, focusing on the most recent advances that have incorporated ML and Graph-based techniques. In the following, we will talk about some classic energy-saving approaches, modern ML techniques including Reinforcement Learning (RL), and the potential of Graph Convolutional Networks (GCNs) and Federated Learning (FL) in addressing energy efficiency challenges in WSNs.

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