Demo Data from VLDB 2026 / reviewers in the wild / expert
Tony Offer
dblp:69/6247
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1
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% | |
| Human-computer interaction and pervasive computing
1 paper |
Ubiquitous computing and smart environments · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wireless sensing and localization › indoor localization
room-level localization |
0.1 | 1 | 2005 | WALRUS: wireless acoustic location with room-level resolution using ultrasound · MobiSys 2005 |
Ubiquitous computing and smart environments › location-based services
indoor location-based services |
0.0 | 1 | 2005 | WALRUS: wireless acoustic location with room-level resolution using ultrasound · MobiSys 2005 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | WALRUS: wireless acoustic location with room-level resolution using ultrasoundabstractIn this paper, we propose a system that uses the wireless networking and microphone interfaces of mobile devices to determine location to room-level accuracy. The wireless network provides a synchronizing pulse along with information about the room. This is accompanied by an ultrasound beacon that allows us to resolve locations to the confines of a physical room (since audio is mostly bounded by walls). We generate the wireless data and ultrasound pulses from the existing PCs in each room; a PDA carried by a user listens for both signals. Thus, our approach does not require special hardware. We do not use ultrasound to send data. As a result we dramatically reduce the computational burden on the mobile device while also decreasing the latency of location resolution. Our results indicate that (i) ultrasound detection is robust even in noisy environments with many reflective surfaces; and (ii) that we can determine the correct room within a couple of seconds with high probability even when the ultrasound emitting PCs are not synchronized. Gaetano Borriello, Alan L. Liu, Tony Offer, Christopher Palistrant, Richard Sharp |
MobiSys | 3 |