Machine Learning-Driven Data Fusion in Wireless Sensor Networks With Virtual Replicas

Abstract

In this study, the authors delve into the world of wireless sensor networks (WSNs) and explore the potential of machine learning-driven data fusion alongside virtual replicas. This research aims to comprehensively evaluate the effectiveness of this innovative approach. By employing advanced algorithms, they merge data from various sensors within WSNs, enhancing the accuracy and reliability of information gathered. Virtual replicas serve as digital counterparts, aiding in simulation and validation processes. Through a thorough assessment, they scrutinize the impact on network performance, energy efficiency, and overall data quality. The findings shed light on the promising capabilities of machine learning-driven data fusion with virtual replicas in optimizing WSNs for diverse applications.