Demo Data from VLDB 2026 / reviewers in the wild / expert
Yu-Xin Xiao
dblp:323/2600
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Wireless sensing and localization · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Smart cities and intelligent transportation · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wireless sensing and localization › localization algorithms
GPS-denied localization |
0.5 | 1 | 2021 | MVP: magnetic vehicular positioning system for GNSS-denied environments · MobiCom 2021 |
Wireless sensing and localization › indoor localization › fingerprint-based localization
magnetic fingerprinting |
0.5 | 1 | 2021 | MVP: magnetic vehicular positioning system for GNSS-denied environments · MobiCom 2021 |
Wireless sensing and localization
vehicle localization |
0.5 | 1 | 2021 | MVP: magnetic vehicular positioning system for GNSS-denied environments · MobiCom 2021 |
Smart cities and intelligent transportation › navigation
vehicle navigation |
0.1 | 1 | 2021 | MVP: magnetic vehicular positioning system for GNSS-denied environments · MobiCom 2021 |
Methods — techniques the papers use, named apart from their topics
magnetic fingerprint matching · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | MVP: magnetic vehicular positioning system for GNSS-denied environmentsabstractAccurate positioning in global navigation satellite system (GNSS)-denied environments, such as tunnels and underpasses, remains a challenge. Navigation systems for such environments need to strike a balance between price and precision. In this paper, we propose magnetic vehicular positioning (MVP), a navigation system that guides drivers in GNSS-denied environments. The key idea of MVP is to extract magnetic fingerprints from geomagnetic field anomalies. By comparing the measured magnetic field against a magnetic map, positioning can be achieved without GNSS signals. Our proposed matching algorithm allows MVP to provide 5.14 m positioning accuracy. We conducted large-scale real-road experiments for 36 months in two countries and 56 tunnels to demonstrate the effectiveness of the proposed system. Because MVP can be deployed on off-the-shelf smartphones, our approach makes accurate navigation in GNSS-denied environments affordable. Chia-Cheng Wang, Jyh-Cheng Chen, Rui-Heng Tu, Jia-Jiun Lee, Yu-Xin Xiao, Shan-Yu Cai |
MobiCom | 6 |