Crowd Monitoring in Smart Destinations Based on GDPR-Ready Opportunistic RF Scanning and Classification of WiFi Devices to Identify and Classify Visitors’ Origins
| Autor: | Alberto Berenguer, David Fernández Ros, Andrea Gómez-Oliva, Josep A. Ivars-Baidal, Antonio J. Jara, Jaime Laborda, Jose-Norberto Mazón, Angel Perles |
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| Přispěvatelé: | Universidad de Alicante. Departamento de Análisis Geográfico Regional y Geografía Física, Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante. Instituto Universitario de Investigaciones Turísticas, Universidad de Alicante. Instituto Universitario de Investigación Informática, Planificación y Gestión Sostenible del Turismo, Web and Knowledge (WaKe) |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
IoT
WiFi scanning Computer Networks and Communications RF scanning COVID-19 Crowd monitoring Smart destination smart destination GDPR crowd monitoring people counting FIWARE Smart Cities Hardware and Architecture Control and Systems Engineering Signal Processing Análisis Geográfico Regional Lenguajes y Sistemas Informáticos Electrical and Electronic Engineering People counting |
| Zdroj: | RUA. Repositorio Institucional de la Universidad de Alicante Universidad de Alicante (UA) Electronics; Volume 11; Issue 6; Pages: 835 |
| Popis: | Crowd monitoring was an essential measure to deal with over-tourism problems in urban destinations in the pre-COVID era. It will play a crucial role in the pandemic scenario when restarting tourism and making destinations safer. Notably, a Destination Management Organisation (DMO) of a smart destination needs to deploy a technological layer for crowd monitoring that allows data gathering in order to count visitors and distinguish them from residents. The correct identification of visitors versus residents by a DMO, while privacy rights (e.g., Regulation EU 2016/679, also known as GDPR) are ensured, is an ongoing problem that has not been fully solved. In this paper, we describe a novel approach to gathering crowd data by processing (i) massive scanning of WiFi access points of the smart destination to find SSIDs (Service Set Identifier), as well as (ii) the exposed Preferred Network List (PNL) containing the SSIDs of WiFi access points to which WiFi-enabled mobile devices are likely to connect. These data enable us to provide the number of visitors and residents of a crowd at a given point of interest of a tourism destination. A pilot study has been conducted in the city of Alcoi (Spain), comparing data from our approach with data provided by manually filled surveys from the Alcoi Tourist Info office, with an average accuracy of 83%, thus showing the feasibility of our policy to enrich the information system of a smart destination. This research was carried out within the research Project Alcoi Tourist Lab framework, co-funded by the Alcoi City Council & the Valencian Innovation Agency. The research was also partially funded by project UAPOSTCOVID19-10 from the University of Alicante. Finally, this research was partly supported by the EU CEF project GreenMov, CARM HORECOV-21 project (https://horecovid.com/ (accessed on 12 January 2022)). is financed through the Call for Public Aid destined to finance the Strategic projects contemplated in the Research and Innovation Strategy for Smart Specialization - RIS3MUR Strategy by the Autonomous Community of the Region of Murcia, through the Ministry of Economic Development, Tourism and Employment within the framework of the FEDER Region of Murcia Operational Program 2014–2020 within the framework Thematic Objective 1. Strengthen research, technological development and innovation by 80% and with CARM’s own funds in 20%, and finally the EU project H2020 NIoVE (833742). |
| Databáze: | OpenAIRE |
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