Enabling Dynamic Indoor Localization by Employing Intersection over Union as a Metric

In modern wireless networks evolving towards 6<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> generation, localization, and sensing in indoor environments play an increasingly critical role in ensuring reliability, security, and control over network users, including vehicular assets. Despite recent advancements in deep learning, using k-Nearest Neighbors (k-NN) as a positioning algorithm in Received Signal Strength Indicator (RSSI) fingerprinting-based localization still provides numerous advantages, including localization accuracy, reliability, and interpretability. In this work, we introduce Intersection over Union (IoU) as a novel similarity metric and introduce κ-enhanced k-NN, which enables dynamic neighbor selection leading to improved performance and generalization capabilities of the positioning algorithm. In the evaluation using 26 publicly available indoor positioning datasets, we clearly show the improvements in localization accuracy of the combined IoU with κ-enhanced k-NN over the relevant baselines.