Scalable and Efficient Clustering for Fingerprint-Based Positioning

Indoor Positioning based on wifi fingerprinting needs a reference dataset, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the dataset and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or involving crowdsourced data collection. Some researchers divide the radio map into smaller independent clusters, such that the search area is reduced to less dense groups than the initial database with similar features. Thus, the computational load in the operational stage is reduced both at the user devices and on servers. Nevertheless, the clustering models are machine-learning algorithms without specific domain knowledge on indoor positioning or signal propagation. This work proposes several clustering variants to optimize the coarse and fine-grained search and evaluates them over different clustering models and datasets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the dataset features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by ≈7% with respect to fingerprinting with the traditional clustering models.