Indoor Localization from Large-Scale Poor-Quality Crowdsourcing Wifi Data for On-Demand Delivery

Given the increasing number of on-demand delivery, people progressively realize that accurate indoor localization of couriers becomes vital for improving the quality of services. However, existing wireless indoor localization systems suffer from deployment difficulty caused by model migration or extra infrastructure needed, and traditional neural networks fail to get good performance with poor quality data and labels. In this work, we overcome the fundamental challenges in pitiful data to achieve a low-cost indoor localization system using large-scale poor-quality crowdsourcing WiFi data - WiLoc. WiLoc constructs WiFi data into a hypergraph and acquires the topological relationships among APs based on a light-weight graph convolutional network. Then, it utilizes a modified transformer structure to learn the latent global-aware features according to the data quality and task demand. Finally, we implement the prototype of WiLoc in the real on-demand delivery scenario with merchant-level accuracy and evaluate its performance based on real datasets from couriers. Extensive experiments demonstrate that WiLoc achieves an average F1 score of 85.03 % across various shopping malls, outperforming the baseline methods.