A Multi-condition WiFi Fingerprinting Dataset for Indoor Positioning - Dương, Bảo Ninh (Springer, 2022)
In recent years, indoor positioning is getting more attention, and WiFi fingerprinting is one promising method to track the position of a person. Until now, there is a lack of public datasets that can be used to compare fairly among different positioning algorithms. In this paper, a multi-condition WiFi fingerprinting dataset is introduced and can be accessed freely for further researches. The raw data were collected over four months in an office room that has a total of 205 reference points. Moreover, this dataset focuses on the different environmental conditions such as the density of people, the subject direction, the period in a day, etc. Various conditions were set up not only in the offline phase but also in the online phase. To support the understanding of the dataset, some materials and software are also provided.