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Enguang Fan

dblp:366/7184 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
0009-0001-8551-9615ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 first-author · 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%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless sensing and localization
indoor localization
0.912025
Poster: Scalable Indoor Localization with Non-Cooperative Wi-Fi Ranging · MobiCom 2025
Wireless sensing and localization › ranging
time-of-flight ranging
0.912025
Poster: Scalable Indoor Localization with Non-Cooperative Wi-Fi Ranging · MobiCom 2025
Wireless sensing and localization › ranging
wifi ranging
0.912025
Poster: Scalable Indoor Localization with Non-Cooperative Wi-Fi Ranging · MobiCom 2025
Wireless sensing and localization › indoor localization
pedestrian dead reckoning
0.312025
Poster: Scalable Indoor Localization with Non-Cooperative Wi-Fi Ranging · MobiCom 2025

Methods — techniques the papers use, named apart from their topics

crowdsourced bootstrapping · 0.9
YearPublicationVenuePosition
2025 Poster: Scalable Indoor Localization with Non-Cooperative Wi-Fi Ranging
abstract
Accurate, ubiquitous indoor localization has long been a central goal in wireless systems, yet most proposed methods remain impractical for large-scale deployment. We present PeepLoc, a scalable Wi-Fi-based system that leverages existing infrastructure and unmodified mobile devices. PeepLoc operates in any indoor space with standards-compliant Wi-Fi APs and regular pedestrian traffic. It combines (a) extracting non-cooperative time-of-flight (ToF) from any AP, and (b) a crowdsourced bootstrapping approach using pedestrian dead reckoning (PDR) to localize APs as anchors. Implemented on commodity hardware, PeepLoc is evaluated across four buildings, achieving 3.41m mean and 3.06m median error, outperforming commercial indoor localization systems and approaching GPS-level accuracy outdoors.
Enguang Fan, Emerson Sie, Federico Cifuentes-Urtubey, Deepak Vasisht
MobiCom1