Advances in Indoor Positioning Systems: Integrating IoT and Machine Learning for Enhanced Accuracy

The proliferation of Internet of Things (IoT) devices and the advancement of machine learning techniques have significantly influenced the evolution of indoor positioning systems (IPS). Traditional methods often struggle with accuracy due to challenges inherent in indoor environments, such as multipath signal interference and dynamic obstructions. The integration of IoT and machine learning offers promising solutions for these challenges by enhancing data acquisition, processing capabilities, and adaptive learning from complex datasets. This review article examines recent developments in IPS, focusing on the synergistic combination of IoT infrastructure with machine learning algorithms to improve localization accuracy. We discuss various IoT-enabled sensing technologies, including Wi-Fi, Bluetooth, and ultra-wideband, and how these technologies serve as input sources for machine learning models. Additionally, we explore the roles of supervised learning, unsupervised learning, and hybrid approaches in refining positioning accuracy. Next, we analyze the potential of deep learning architectures in capturing complex spatial-temporal patterns, making real-time adaptive adjustments possible. Furthermore, we scrutinize the implementation challenges and limitations of current systems, proposing future directions for research that emphasize interdisciplinary approaches and collaborative frameworks. Enhanced understanding and innovation in this domain could lead to breakthroughs in numerous applications, from asset tracking in logistics to enabling smart building infrastructures.