Demo Data from EDBT 2026 / reviewers in the wild / expert
Amy Sniffen
dblp:282/6257
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 2 · 2 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
2 papers |
Wireless sensing and localization · 88% Vehicular, aerial and satellite networks · 12% | |
| Artificial intelligence
1 paper |
Robot navigation and mapping · 100% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping
localization |
0.6 | 1 | 2022 | Sunflower: locating underwater robots from the air: video · MobiSys 2022 |
Robotics › Robot navigation and mapping › localization › GPS-denied localization
underwater localization |
0.6 | 1 | 2022 | Sunflower: locating underwater robots from the air: video · MobiSys 2022 |
Wireless sensing and localization
optical sensing |
0.6 | 1 | 2022 | Sunflower: locating underwater robots from the air · MobiSys 2022 |
Wireless sensing and localization › localization algorithms
underwater localization |
0.6 | 1 | 2022 | Sunflower: locating underwater robots from the air · MobiSys 2022 |
Wireless sensing and localization
aerial localization |
0.2 | 1 | 2022 | Sunflower: locating underwater robots from the air: video · MobiSys 2022 |
Methods — techniques the papers use, named apart from their topics
optical fiber sensing · 0.6backscatter communication · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Sunflower: locating underwater robots from the airabstractLocating underwater robots is fundamental for enabling important underwater applications. The current mainstream method requires a physical infrastructure with relays on the water surface, which is largely ad-hoc, introduces a significant logistical overhead, and entails limited scalability. Our work, Sunflower, presents the first demonstration of wireless, 3D localization across the air-water interface - eliminating the need for additional infrastructure on the water surface. Specifically, we propose a laser-based sensing system to enable aerial drones to directly locate underwater robots. The Sunflower system consists of a queen and a worker component on a drone and each tracked underwater robot, respectively. To achieve robust sensing, key system elements include (1) a pinhole-based sensing mechanism to address the sensing skew at air-water boundary and determine the incident angle on the worker, (2) a novel optical-fiber sensing ring to sense weak retroreflected light, (3) a laser-optimized backscatter communication design that exploits laser polarization to maximize retroreflected energy, and (4) the necessary models and algorithms for underwater sensing. Real-world experiments demonstrate that our Sunflower system achieves average localization error of 9.7 cm with ranges up to 3.8 m and is robust against ambient light interference and wave conditions. Charles J. Carver, Qijia Shao, Samuel Lensgraf, Amy Sniffen, Maxine Perroni-Scharf, Hunter Gallant, Alberto Quattrini Li |
MobiSys | 4 |
| 2022 | Sunflower: locating underwater robots from the air: video
Charles J. Carver, Qijia Shao, Samuel Lensgraf, Amy Sniffen, Maxine Perroni-Scharf, Hunter Gallant, Alberto Quattrini Li |
MobiSys | 4 |