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Marinheiro, R. N., Brito e Abreu, F., vieira, T. & Martins, M. (2025). Adaptive Crowd Sensing with Privacy-Preserving WiFi Fingerprinting. In IEEE International Conference on Smart Internet of Things (SmartIoT 2025). Sydney: IEEE.

R. M. Marinheiro et al.,  "Adaptive Crowd Sensing with Privacy-Preserving WiFi Fingerprinting", in IEEE Int. Conf. on Smart Internet of Things (SmartIoT 2025), Sydney, IEEE, 2025

@inproceedings{marinheiro2025_1777836340139,
	author = "Marinheiro, R. N. and Brito e Abreu, F. and vieira, T. and Martins, M.",
	title = "Adaptive Crowd Sensing with Privacy-Preserving WiFi Fingerprinting",
	booktitle = "IEEE International Conference on Smart Internet of Things (SmartIoT 2025)",
	year = "2025",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/SmartIoT66867.2025.00053",
	publisher = "IEEE",
	address = "Sydney",
	organization = "",
	url = "https://ieee-smartiot.org"
}

TY  - CPAPER
TI  - Adaptive Crowd Sensing with Privacy-Preserving WiFi Fingerprinting
T2  - IEEE International Conference on Smart Internet of Things (SmartIoT 2025)
AU  - Marinheiro, R. N.
AU  - Brito e Abreu, F.
AU  - vieira, T.
AU  - Martins, M.
PY  - 2025
SN  - 2770-2669
DO  - 10.1109/SmartIoT66867.2025.00053
CY  - Sydney
UR  - https://ieee-smartiot.org
AB  - This paper presents ongoing work in the context of a recently funded research project, MoniCrowd, aimed at advancing crowd monitoring in dynamic urban settings, particularly during temporary public events. The proposed system adopts an adaptive architecture based on passive Wi-Fi probe request
detection, comprising portable sensors with multi-radio access connectivity and a rule-based fingerprinting method for anonymous and reliable device counting. Sensors autonomously select
the best uplink from available connectivity options, enabling operation in suboptimal locations. A novel tool, the Information Elements Automatic Analyser (IEAA), enhances fingerprint robustness through fine-grained feature selection. A new dataset, collected under controlled Faraday cage conditions, supports this development. Preliminary field deployment results show strong correlation with manual counts, validating the approach under real-world conditions. The proposed fingerprinting method also
achieved top accuracy in the international CONFRONT challenge. To optimise sensor performance in diverse environments, a UAV-assisted calibration tool is under development; its design and
preliminary sensitivity mapping results are presented. Altogether, this work lays the foundation for scalable, autonomous crowd sensing solutions that can be rapidly deployed in dynamic urban contexts without requiring specialised local expertise.
ER  -