Demo Data from EDBT 2026 / reviewers in the wild / expert
Stephane Laroche
dblp:162/9062
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1Applied, interdisciplinary, general and emerging computing · 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% |
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 |
0.2 | 1 | 2015 | Bringing CUPID Indoor Positioning System to Practice · WWW 2015 |
Wireless sensing and localization › indoor localization
time-of-flight localization |
0.2 | 1 | 2015 | Bringing CUPID Indoor Positioning System to Practice · WWW 2015 |
Methods — techniques the papers use, named apart from their topics
signal strength fusion · 0.2crowdsourcing · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Bringing CUPID Indoor Positioning System to PracticeabstractWiFi based indoor positioning has recently gained more attention due to the advent of the IEEE 802.11v standard, requirements by the FCC for E911 calls, and increased interest in location-based services. While there exist several indoor localization techniques, we find that these techniques tradeoff either accuracy, scalability, pervasiveness or cost -- all of which are important requirements for a truly deployable positioning solution. Wireless signal-strength based approaches suffer from location errors, whereas time-of-flight (ToF) based solutions provide good accuracy but are not scalable. Recent solutions address these issues by augmenting WiFi with either smartphone sensing or mobile crowdsourcing. However, they require tight coupling between WiFi infrastructure and a client device, or they can determine the client's location only if it is mobile. In this paper, we present CUPID2.0 which improved our previously proposed CUPID indoor positioning system to overcome these limitations. We achieve this by addressing the fundamental limitations in Time-of-Flight based localization and combining ToF with signal strength to address scalability. Experiments from $6$ cities using $40$ different mobile devices, comprising of more than $2.5$ million location fixes demonstrate feasibility. CUPID2.0 is currently under production, and we expect CUPID2.0 to ignite the wide adoption of WLAN-based positioning systems and their services. Souvik Sen, Stephane Laroche, Kyu-Han Kim, Jeongkeun Lee |
WWW | 3 |