Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets
This work addresses data preprocessing for indoor positioning systems, which is crucial for improving QoS in wearable and IoT devices, but it appears incremental as it builds on existing correlation-based methods.
The paper tackles the problem of data quality in indoor positioning Wi-Fi fingerprinting datasets by proposing a novel data cleansing algorithm based on correlation among fingerprints, resulting in an average removal of 14% of fingerprints, a reduction in 2D positioning error by 2.7% and 3D error by 5.3%, and an increase in prediction speed by 14%.
Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this paper, we offer a novel and straightforward data cleansing algorithm for WLAN fingerprinting radio maps. This algorithm is based on the correlation among fingerprints using the Received Signal Strength (RSS) values and the Access Points (APs)'s identifier. We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset. We evaluated the proposed method on 14 independent publicly-available datasets. As a result, an average of 14% of fingerprints were removed from the datasets. The 2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with a slight increase in the floor hit rate by 1.2% on average. Consequently, the average speed of position prediction was also increased by 14%.