CrowdEstimator

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-Fionly 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. CCS CONCEPTS• Computer systems organization → Embedded and cyberphysical systems;©ACM 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM MobiSys'18, http://doi.

Embracing the Future Internet of Things

“…The Wi-Fi sensor (constituted of a Wi-Fi module configured in monitor mode, sniffing Wi-Fi probe requests) collects raw data from smartphones of students at the campus (such as MAC address of the requester, relative received signal strength-RSSI, and timestamp) and the device at the vehicle anonymizes the data using hashing and salting mechanisms and pushes to the cloud side where “Central IoT” resides. Crowd estimation service (CEMA) [28] creates the analytics results and makes them available through a lightweight broker for the campus. Several pilot sites can be connected through the Federated IoT Platform.…”

“…Memos et al [59] integrates a Wireless Sensors Network (WSN) for surveillance application into a smart city framework taking into consideration security and privacy concerns. Finally, our previous work [28,60] and the work from Chilipirea et al [61] treat the topic of crowd analytics using the Wi-Fi signals (see Section 4.2). In the study of Andión et al [62], a wide review of human activity detection based on Wi-Fi signals is also included.…”

Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches

“…However, using Wi-Fi data only to monitor and forecast the crowd size and dynamics may yield inaccurate results. For this reason, some works in the literature propose to integrate Wi-Fi data with other data sources, such as data coming from stereoscopic cameras [48] in order to estimate the crowd size, or from an automated people counting system [49] in order to better approximate crowd sizes.…”

“…Safety issues for crowd management can be found respectively in [85,86]. A more recent work combines Wi-Fi data captured from mobile devices and stereoscopic cameras to build a model for people counting [48].…”