Exportar Publicação

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) 
Silva, R. D., Marinheiro, R. N. & Abreu, F. B. (2019). Crowding detection combining trace elements from heterogeneous wireless technologies. In 2019 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC). Lisbon, Portugal: IEEE.
Exportar Referência (IEEE) 
R. D. Silva et al.,  "Crowding detection combining trace elements from heterogeneous wireless technologies", in 2019 22nd Int. Symp. on Wireless Personal Multimedia Communications (WPMC), Lisbon, Portugal, IEEE, 2019
Exportar BibTeX 
@inproceedings{silva2019_1731979581234,
	author = "Silva, R. D. and Marinheiro, R. N. and Abreu, F. B.",
	title = "Crowding detection combining trace elements from heterogeneous wireless technologies",
	booktitle = "2019 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC)",
	year = "2019",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/WPMC48795.2019.9096131",
	publisher = "IEEE",
	address = "Lisbon, Portugal",
	organization = "IEEE",
	url = "https://ieeexplore.ieee.org/xpl/conhome/9093082/proceeding"
}
Exportar RIS 
TY  - CPAPER
TI  - Crowding detection combining trace elements from heterogeneous wireless technologies
T2  - 2019 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC)
AU  - Silva, R. D.
AU  - Marinheiro, R. N.
AU  - Abreu, F. B.
PY  - 2019
SN  - 1347-6890
DO  - 10.1109/WPMC48795.2019.9096131
CY  - Lisbon, Portugal
UR  - https://ieeexplore.ieee.org/xpl/conhome/9093082/proceeding
AB  - Non-invasive crowding detection in quasi-real-time is required for a number of use cases, such as for mitigating tourism overcrowding. The present goal is a low-cost crowding detection technique combining personal trace elements obtained from heterogeneous wireless technologies (4G, 3G, GSM, Wi- Fi and Bluetooth) supported by mobile devices carried by most people. This work proposes detection nodes containing Raspberry-Pi boards equipped with several off-the-shelf Software Defined Radio (SDR) dongles. Those nodes perform spectrum analysis on the bands corresponding to the aforementioned wireless technologies, based on several open source software components. The outcome of this edge computing, performed in each node, is integrated in a cloud server using a Long Range Wide Area Network (LoRaWAN), a recent technology developed for IoT applications. Our preliminary results show that is possible to determine the number of mobile devices in the vicinity of each node, by combining information from several wireless technologies, each with its own detection range and precision.
ER  -