Towards improving context-aware applications using link quality induction and self-calibrated ultra-wide band localization systems

Abstract

Context aware applications rely heavily on accurate and reliable localization systems to enhance their functionality and user experience. This paper explores the potential of self-calibrated Ultra-Wide Band (UWB) localization systems to improve the performance of context-aware applications. Here, we propose a novel approach that integrates self-calibration techniques with UWB technology to address the challenges associated with traditional localization methods, such as manual calibration, environmental interference, limited accuracy, high energy consumption, and adaptability to changing conditions. Our approach leverages the high temporal and spatial resolution of UWB signals to achieve precise localization without requiring extensive manual calibration. Meanwhile, we use Received Signal Strength Indicator (RSSI) and Link Quality Induction (LQI) as a positioning mechanism for cost estimation. In addition, our approach is equipped with a calibration factor for indoor localization that can update the distance information between nodes in dynamic environments. We present a comprehensive evaluation of our method through simulations and real-world experiments, demonstrating its effectiveness in diverse scenarios. The results show significant improvements in localization accuracy and positioning error, which in turn enhance the performance of context-aware applications.