Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets
| العنوان: | Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets |
|---|---|
| المؤلفون: | Darwin Quezada-Gaibor, Lucie Klus, Joaquin Torres-Sospedra, Elena Simona Lohan, Jari Nurmi, Carlos Granell, Joaquin Huerta |
| المساهمون: | Tampere University, Electrical Engineering, Universidade do Minho |
| المصدر: | 2022 23rd IEEE International Conference on Mobile Data Management (MDM). |
| بيانات النشر: | IEEE, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Data cleansing, Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Science & Technology, Data pre-processing, Indoor positioning, 213 Electronic, automation and communications engineering, electronics, indoor positioning, Wi-Fi Fingerprinting, Localisation, data cleansing, Machine Learning (cs.LG), localisation, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, wi-Fi fingerprinting, data pre-processing |
| الوصف: | 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%. Submitted to ALIAS2022/MDM2022 |
| وصف الملف: | fulltext; application/pdf |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b01884147d6cd6a95cdb40f7a3059487Test https://doi.org/10.1109/mdm55031.2022.00079Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....b01884147d6cd6a95cdb40f7a3059487 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |