Xing Zhang, Wei Sun*, Jin Zheng*, Min Xue*, Chenjun Tang, and Roger Zimmermann
International Journal of Control, Automation, and Systems, vol. 20, no. 5, pp.1484-1499, 2022

Abstract : The era of Internet of Things (IoT) has stimulated the diversification of wireless applications, and a pragmatic way is to adopt and leverage WiFi to pinpoint the position of a mobile device. However, there still exist significant challenges in this field, such as heterogeneous crowd-sourced data distribution, external scene interference, etc. We focus on indoor WiFi fingerprint localization in multistory buildings. To confine the search scope to a specific floor, we propose a novel floor identification module. In this module we construct a WiFi fingerprint graph representation to fully explore the correlations of reference points (RP). Furthermore, a fingerprint graph attention mechanism is introduced to capture the importance of adjoining fingerprints for a more accurate floor identification. In addition, a two-panel fingerprint homogeneity graph is adopted to gauge the resemblance of localization fingerprints, and the estimated 2-D location is predicted by the integration of panel results. By comprehensively analyzing the fingerprint attributes of a crowd-sourced database, we have conducted experiments to demonstrate the localization algorithm¡¯s performance. Compared with other algorithms, the results show that the proposed method can achieve the best performance in floor identification, reaching 96.93%; In the aspect of 2-D geometric positioning, the proposed method also has better performance.

Keyword : Indoor localization, Internet of Things, location based service, WiFi fingerprint.