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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Brito e Abreu, F., Marinheiro, R. N., oliveira(jpnoa1), J. & Mestre Santos, T. (2025). Quality-Driven Edge-to-Cloud Architecture for Crowd Monitoring with Wi-Fi Sensing. In Houbing Herbert Song, Hirozumi Yamaguchi, Hung-Yu Wei and Pietro Manzoni (Ed.), IEEE Annual Congress on Artificial Intelligence of Things (AIoT 2025). (pp. 881-887). Osaka, Japão: IEEE Computer Society.
Exportar Referência (IEEE) 
F. M. Abreu et al.,  "Quality-Driven Edge-to-Cloud Architecture for Crowd Monitoring with Wi-Fi Sensing", in IEEE Annu. Congr. on Artificial Intelligence of Things (AIoT 2025), Houbing Herbert Song, Hirozumi Yamaguchi, Hung-Yu Wei and Pietro Manzoni, Ed., Osaka, Japão, IEEE Computer Society, 2025, vol. 1, pp. 881-887
Exportar BibTeX 
@inproceedings{abreu2025_1770123939655,
	author = "Brito e Abreu, F. and Marinheiro, R. N. and oliveira(jpnoa1), J. and Mestre Santos, T.",
	title = "Quality-Driven Edge-to-Cloud Architecture for Crowd Monitoring with Wi-Fi Sensing",
	booktitle = "IEEE Annual Congress on Artificial Intelligence of Things (AIoT 2025)",
	year = "2025",
	editor = "Houbing Herbert Song, Hirozumi Yamaguchi, Hung-Yu Wei and Pietro Manzoni",
	volume = "1",
	number = "",
	series = "",
	doi = "10.1109/AIoT66900.2025.00143",
	pages = "881-887",
	publisher = "IEEE Computer Society",
	address = "Osaka, Japão",
	organization = "",
	url = "https://www.ieee-aiot.org/2025"
}
Exportar RIS 
TY  - CPAPER
TI  - Quality-Driven Edge-to-Cloud Architecture for Crowd Monitoring with Wi-Fi Sensing
T2  - IEEE Annual Congress on Artificial Intelligence of Things (AIoT 2025)
VL  - 1
AU  - Brito e Abreu, F.
AU  - Marinheiro, R. N.
AU  - oliveira(jpnoa1), J.
AU  - Mestre Santos, T.
PY  - 2025
SP  - 881-887
DO  - 10.1109/AIoT66900.2025.00143
CY  - Osaka, Japão
UR  - https://www.ieee-aiot.org/2025
AB  - This paper presents an adaptive software architecture designed for edge computing in crowd-monitoring applications utilizing Wi-Fi fingerprinting. The architecture enables the real-time detection and analysis of mobile devices in proximity, facilitating crowd density estimation and management in various scenarios, such as tourist destinations, event venues, and public spaces. The adaptive features of the architecture include auto-healing, vertical handover, and over-the-air updates. By leveraging self-configuration capabilities, the system dynamically adjusts communication protocols between Wi-Fi and LoRaWAN to ensure resilience and continuity of data transmission under changing network conditions. Furthermore, over-the-air update mechanisms facilitate seamless deployment of software updates to edge nodes, enhancing security and performance over time.
By employing a Wi-Fi fingerprinting algorithm, the system accurately differentiates mobile devices while preserving user privacy in compliance with GDPR. Integration with cloud-based servers and APIs enables seamless data ingestion and visualization, empowering destination managers with actionable insights for effective crowd management. Overall, this adaptive edge computing architecture represents a significant advancement in crowd-monitoring technology, offering scalability, flexibility, and privacy for diverse application scenarios.
ER  -