Rank Based Iterative Clustering (RBIC) for indoor localization

Abstract

In the current era, technological advancements have resulted in a spike in various sensor data-intensive applications including indoor localization services. WiFi fingerprint-based localization for large indoor areas is primarily contingent on laborious site surveys. To ease the flow of research, grossly labeled data collected using a crowd-sourced method could be clustered. However, for a real-time localization approach, the challenge is to generate the clusters containing data samples that are really physically close. In this paper, a two-phase semi-supervised localization approach has been proposed, which is general enough to be applied to indoor localization datasets. In the offline phase, a Rank-Based Iterative Clustering (RBIC) algorithm has been proposed that generates a clustered dataset with a negligible chance of containing physically apart location points within a common cluster. RBIC can be viewed as a clustering ensemble model. Different clustering algorithms are selected as baseline algorithms and assigned unique ranks depending on well-known clustering scores to be fed as input to RBIC. In the online phase, the users’ location is estimated using machine learning (ML) classifiers based on the dynamic received signal strength indicator (RSSI) vector received through its handheld device transceiver. The system is evaluated with three benchmark datasets for WiFi based indoor localization. For the first dataset, JUIndoorLoc, 94% to 99% localization accuracy is achieved for individual supervised classifiers. For the second and third datasets, the ranges of obtained localization accuracies are 96% to 99% and 95% to 98% respectively.