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Indoor localization (IL) systems are crucial for enhancing operational efficiency, safety, and user experience by allowing precise tracking of objects, robots, or individuals within various environments such as healthcare, retail, and industrial sectors. Despite their increasing importance, there remains a notable deficiency in the literature, particularly concerning systematic reviews that consolidate findings from experimental research. This work fills this crucial gap by rigorously assessing the advancements and challenges faced by Wireless Sensor Network (WSN)-based IL systems, with a focused examination of experimental studies conducted over the past five years. It delves into both radio frequency (RF) and non-RF technologies, critically evaluating a spectrum of localization methods including fingerprinting, geometric mapping, proximity, and dead-reckoning. It systematically evaluates the advantages, limitations, and current solutions of each method, based on their citation metrics and prevalence in scholarly literature. Furthermore, the paper explores innovative performance enhancement techniques, including the integration of machine learning and the hybridization of multiple technologies, to demonstrate significant improvements in IL functionalities. It also identifies and analyzes key trends, such as the choice of technologies for specific methods, typical network density configurations, and accuracy enhancements achieved through different approaches. Research gaps are highlighted, including the need for advancements in machine learning for offline and edge computing, standardization of sensor components, and improvements in interoperability and energy efficiency. The paper concludes by proposing strategic future research directions, outlining a roadmap for advancing IL research and development in this rapidly evolving field.
Nur Syazreen Ahmad (Mon,) studied this question.