Name
Abstract | ||
|---|---|---|
We develop a novel trajectory-based localization scheme which (i) identifies a user's current trajectory based on the measurements collected while the user is moving, by finding the best match among the training traces (trajectory matching) and then (ii) localizes the user on the trajectory (localization). The core requirement of both the steps is an accurate and robust algorithm to match two time-series that may contain significant noise and perturbation due to differences in mobility, devices, and environments. To achieve this, we develop an enhanced Dynamic Time Warping (DTW) alignment, and apply it to RSS, channel state information, or magnetic field measurements collected from a trajectory. We use indoor and outdoor experiments to demonstrate its effectiveness. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2591971.2592027 | SIGMETRICS |
Keywords | Field | DocType |
localization,magnetic field,network monitoring,dynamic time warping,wifi | Computer vision,Dynamic time warping,Computer science,Real-time computing,Artificial intelligence,RSS,Trajectory,Perturbation (astronomy),Channel state information | Conference |
Volume | Issue | ISSN |
42 | 1 | 0163-5999 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Swati Rallapalli | 1 | 202 | 13.89 |
| Wei Dong | 2 | 139 | 7.70 |
| Lili Qiu | 3 | 3987 | 284.13 |
| Yin Zhang | 4 | 3492 | 281.04 |