Scalable and Efficient Clustering for Fingerprint-Based Positioning

Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set 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 data sets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the data set features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by approximate to 7% with respect to fingerprinting with the traditional clustering models.

Automated summary

Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting requires a reference data set called a radio map to match incoming fingerprints and estimate device position. To address scalability issues, researchers divide the radio map into smaller clusters to reduce the computational load. However, these clustering models lack domain knowledge for indoor positioning. This study proposes clustering variants to optimize search and evaluates their performance, providing guidelines for efficient and accurate positioning. Results show that the proposed variants reduce execution time by half and positioning error by approximately 7% compared to traditional clustering models.