  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.
