Data Fusion and Analysis in IoT Sensor Networks

Abstract

The Internet of Things (IoT) has rapidly expanded, resulting in the development of extensive and intricate sensor networks. These networks are essential for various applications, from smart city management to environmental monitoring and industrial automation. As these networks generate enormous amounts of data, effective data fusion and analysis become crucial for deriving actionable insights and making informed decisions. This paper delves into the fundamental aspects of data fusion and analysis within IoT sensor networks, highlighting essential methods and addressing various challenges and advancements in the field. The paper provides an in-depth exploration of several data processing methods, including data aggregation, denoising, outlier identification, and missing data imputation. Numerous fusion techniques are examined, such as identity-based fusion, related feature extraction, and direct fusion, and the importance of data fusion is highlighted even further. The study also looks into how data analysis may be combined with cutting-edge cloud, fog, and edge computing technologies, emphasizing how these technologies can help address issues with IoT sensor networks and data analysis. This study attempts to give meaningful insights and a comprehensive grasp of IoT sensor data processing, fusion, and analysis by providing a thorough overview of these methodologies.