Demo Data from EDBT 2026 / reviewers in the wild / expert
Tzu-Yin Tai
dblp:200/7325
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—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 · 77% Internet of things and sensor networks · 23% |
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 |
0.3 | 1 | 2017 | $Tack: $ Learning Towards Contextual and Ephemeral Indoor Localization With Crowdsourcing · IEEE J. Sel. Areas Commun. 2017 |
Internet of things and sensor networks › low-power wireless
bluetooth low energy beacon |
0.1 | 1 | 2017 | $Tack: $ Learning Towards Contextual and Ephemeral Indoor Localization With Crowdsourcing · IEEE J. Sel. Areas Commun. 2017 |
Methods — techniques the papers use, named apart from their topics
dead reckoning · 0.3crowdsourcing-based inference · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | $Tack: $ Learning Towards Contextual and Ephemeral Indoor Localization With CrowdsourcingabstractAt events, such as conferences, indoor localization is both contextual and ephemeral, in that localization is only needed within the context of and for the duration of the event. As such, the costs and requirements of providing such services need to be minimal. In this paper, we design, implement, and evaluate Tack, a new mobile application framework that is specifically engineered to support such contextual and ephemeral indoor localization during an event. To provide location-based services with Tack, an event organizer only needs to bring and place a small number of (reusable) beacons around the venue before the event begins. As a system framework, Tack uses a combination of known beacon locations, contacts over bluetooth low energy, crowdsourcing, and dead-reckoning to estimate and refine user locations. To make our location estimates more accurate, we embrace the inherent nature of beacons, design crowdsourcing-based inference algorithms, and present an extensive evaluation by running real-world experiments with iOS devices and beacons. Tack has been implemented as an open-source framework on the iOS platform and can be used by mobile applications designed for events with location-based services. Liyao Xiang, Tzu-Yin Tai, Baochun Li, Bo Li 0001 |
IEEE J. Sel. Areas Commun. | 2 |