Clustering algorithms research for device-clustering localization

Crowdsourcing-based localization has attracted wide research concern to the metropolitan-scale positioning. However, crowdsourcing-based fingerprints collection with assorted mobile smart devices brings fingerprint confusion, which significantly degrades the localization accuracy. To solve the device diversity problem, many solutions have been raised like the Device-Clustering algorithm. Based on macro Device-Cluster (DC) rather than natural device, DC algorithm maintains less device types and slight calibration overhead. Despite high positioning accuracy, the selection of suitable clustering algorithms in DC system becomes another puzzle. In this paper, we reshape the novel Device-Clustering algorithm to enhance the indoor positioning by comparing the application of different clustering algorithms. The experimental result indicates the reliability of DC strategy in broad clustering scheme as well as the suitable locating process corresponding to distinct environment.