Kalman Filters in IoT: A Bibliometric Analysis

This bibliometric analysis focuses on the evolution and trends of the Kalman Filters (KFs) studies in the Internet of Things (IoT) from the year 2009 to 2023. This being a data-intensive study, it uses the information from major academic databases, which it adapts to explore key terms, publication patterns, and the interdisciplinary nature of the research. The paper comes out with a sharp increase of the research in 2015, which corresponds to growing of the research interest to the application of KFs for IoT. Key points of the study accentuate the role of KFs in IoT, especially in respect of the betterment of the systems for indoor positioning, global positioning, and sensor data fusion. This software proves the KFs are in the spotlight in IoT by helping to improve localization accuracy and data processing. The research highlights progress in filtering methods, for example, extended and unscented Kalman filters, evidently to improve state estimation and predictive analytics in dynamic contexts. Furthermore, KFs research scope includes novel areas such as machine learning and deep learning, which indicates the possibility of using this technology as a tool for advancing IoT. The rapid growth of technology in this area also presents challenges, a part of those are the data privacy and security problems in complex IoT environments. The paper emphasizes the major part KFs are playing in driving IoT technology and also stresses the type of interdisciplinary studies that are needed to navigate the changing landscape of IoT applications.