Impact of Distance Metric Selection on Local Outlier Factor-Based Outlier Detection in Time-Series Received Signal Strength Observations

Abstract

Due to received signal strength (RSS) variability in indoor environments, multiple observations are often collected sequentially over time to reduce short-term fluctuations. However, these time-series observations may contain local outliers caused by measurement disturbances and environmental variability. The Local Outlier Factor (LOF) algorithm can detect such outliers, but its performance depends on the distance metric, neighbourhood size, and LOF threshold. Existing studies provide limited comparative evaluation of distance metrics for LOF-based outlier detection in time-series RSS data. This study addresses this gap by systematically evaluating four distance metrics, namely absolute, squared, relative, and Canberra, within the LOF framework for time-series RSS outlier detection. Two RSS time-series datasets were evaluated using neighbourhood sizes of k=10,20, and 30 and LOF thresholds of T=1.0,1.5, and 2.0. Performance was assessed using Receiver Operating Characteristic Area Under the Curve (ROC-AUC), Precision-Recall Area Under the Curve (PR-AUC), Precision, Recall, and F1 score, with physically defined RSS bounds used to establish reference outliers. Dataset #1 contained 14 reference outliers, while Dataset #2 contained 175. The results show that distance metric selection affects threshold-based classification performance, although its effect varies across datasets and parameter settings. For Dataset #1, squared distance achieved perfect Precision, Recall, and F1 score at k = 10 and T = 1.5. For Dataset #2, squared distance achieved the highest F1 score of 0.2927 at k = 30 and T = 1.5, while Canberra distance achieved the highest ROC-AUC of 0.5812. The findings demonstrate that distance metric selection is an important consideration when configuring LOF for RSS outlier detection.