CrowdEstimator: Approximating crowd sizes with multi-modal data for internet-of-things services
Journal
MobiSys 2018 - Proceedings of the 16th ACM International Conference on Mobile Systems, Applications, and Services
ISBN
9781450357203
Date Issued
2018-06-10
Author(s)
Solmaz, Gürkan
Abstract
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 estimation results across different systems. The crowd estimation problem is to approximate the crowd size in a targeted area using the observed information (e.g., Wi-Fi data). This paper exploits Wi-Fi probe request packets (“Wi-Fi probes" for short) broadcasted by mobile devices to solve this problem. However, using only Wi-Fi probes to estimate the crowd size may result in inaccurate results due to various environmental uncertainties which may lead to crowd overestimation or underestimation. Moreover, the ground-truth is unavailable because the coverage of Wi-Fi signals is time-varying and invisible. This paper introduces auxiliary sensors, stereoscopic cameras, to collect the near ground-truth at a specified calibration choke point. Two calibration algorithms are proposed to solve the crowd estimation problem. The key idea is to calibrate the Wi-Fi-only crowd estimation based on the correlations between the two types of data modalities. Then, to share the calibrated results across systems required by different stakeholders, our system is integrated with the FIWARE-based IoT platform. To verify the proposed system, we have launched an indoor pilot study in the Wellington Railway Station and an outdoor pilot study in the Christchurch Re:START Mall in New Zealand. The large-scale pilot studies show that stereoscopic cameras can reach minimum accuracy of 85% and high precision detection for providing the near ground-truth. The proposed calibration algorithms reduce estimation errors by 43.68% on average compared to the Wi-Fi-only approach.
Subjects
Cyber-physical systems | Human mobility | Smart cities; eess.SY; eess.SY; Computer Science - Networking and Internet Architecture; cs.SY
Description
This work was funded by the joint project collaborations between NEC
New Zealand and NEC Laboratories Europe and between NEC Laboratories Europe
GmbH and Technische Universitat Dortmund, and has been partially funded by
the European Union's Horizon 2020 Programme under Grant Agreement No.
CNECT-ICT-643943 FIESTA-IoT: Federated Interoperable Semantic IoT Testbeds
and Applications. Proc. of ACM MobiSys'18, 2018
New Zealand and NEC Laboratories Europe and between NEC Laboratories Europe
GmbH and Technische Universitat Dortmund, and has been partially funded by
the European Union's Horizon 2020 Programme under Grant Agreement No.
CNECT-ICT-643943 FIESTA-IoT: Federated Interoperable Semantic IoT Testbeds
and Applications. Proc. of ACM MobiSys'18, 2018
Type
conference paper