activity
20182020
most citedCrowdEstimator: Approximating Crowd Sizes with Multi-modal Data for Internet-of-Things Services

31 citations · 31 across the 2 of their papers we have counts for

collaborators

8 papers

eess.SP2020

Group-In: Group Inference from Wireless Traces of Mobile Devices

Gürkan Solmaz, Jonathan Fürst, Samet Aytaç +1

This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the…

eess.SY2019★ 31 cited

CrowdEstimator: Approximating Crowd Sizes with Multi-modal Data for Internet-of-Things Services

Fang-Jing Wu, Gürkan Solmaz

Crowd mobility has been paid attention for the Internet-of-things (IoT) applications. This paper addresses the crowd estimation problem and builds an IoT service to share the crowd…

eess.SP2019

Toward Understanding Crowd Mobility in Smart Cities through the Internet of Things

Gürkan Solmaz, Fang-Jing Wu, Flavio Cirillo +5

Understanding crowd mobility behaviors would be a key enabler for crowd management in smart cities, benefiting various sectors such as public safety, tourism and transportation. Th…

cs.NI2018

WiFiScout: A Crowdsensing WiFi Advisory System with Gamification-based Incentive

Fang-Jing Wu, Tie Luo

As mobile crowdsensing techniques are steering many smart-city applications, an incentive scheme that motivates the crowd to actively participate becomes a key to the success of su…

cs.NI2018

We Hear Your Activities through Wi-Fi Signals

Fang-Jing Wu, Gürkan Solmaz

In this paper we focus on the problem of human activity recognition without identification of the individuals in a scene. We consider using Wi-Fi signals to detect certain human mo…

cs.NI2018

Together or Alone: Detecting Group Mobility with Wireless Fingerprints

Gürkan Solmaz, Fang-Jing Wu

This paper proposes a novel approach for detecting groups of people that walk "together" (group mobility) as well as the people who walk "alone" (individual movements) using wirele…