Name
Abstract | ||
|---|---|---|
Recent advances in GPS and WiFi-based positioning technologies for mobile phones have triggered many location-based services. However, GPS positioning quickly drains a phone's battery and cannot be used indoors. On the other hand, WiFi positioning provides energy-efficient indoor and outdoor positioning with reasonable accuracy. However, WiFi positioning sometimes makes large errors caused by various reasons, e.g., the movement of reference WiFi access points. In this paper we attempt to detect and correct such errors automatically by performing outlier detection in time series. So, we solve this problem by comparing a user's current measurement at time T with her coordinate point at time T predicted from her past coordinate history, and judging whether the current measurement is correct or not by computing the distance between the measurement location and the predicted location. However, it is difficult to predict the user's coordinates accurately with a single prediction method (predictor) because the user's context (e.g., migration speed and sparseness of past coordinates) greatly affects predictor performance. We thus design a context-aware error detection method by employing an ensemble of predictors that have different strengths and weaknesses. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2493432.2493460 | UbiComp |
Keywords | Field | DocType |
wifi positioning error,outlier detection,time series,context-aware error detection method,gps positioning,wifi-based positioning technology,outdoor positioning,reference wifi access point,current measurement,measurement location,wifi positioning,reliability | Anomaly detection,Hybrid positioning system,Gps positioning,Simulation,Computer science,Real-time computing,Error detection and correction,Human–computer interaction,Phone,Global Positioning System,Strengths and weaknesses | Conference |
Citations | PageRank | References |
5 | 0.49 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuki Tsuda | 1 | 11 | 1.91 |
| Quan Kong | 2 | 11 | 4.04 |
| Takuya Maekawa | 3 | 326 | 49.93 |