Smart Optical Sensors for Internet of Things: Integration of Temperature Monitoring and Customized Security Physical Unclonable Functions


Nowadays, the Internet of Things (IoT) has an astonishingly societal impact in which healthcare services stand out. Amplified by the COVID-19 pandemic scenario, challenges include the development of authenticatable smart IoT devices with the ability to simultaneously track people and sense in real-time human body temperature aiming to infer a health condition in a contactless and remote way through user-friendly equipment such as a smartphone. Univocal smart labels based on quick response (QR) codes were designed and printed on medical substrates (protective masks and adhesive) using flexible organic-inorganic luminescent inks. Luminescence thermometry and physical unclonable functions (PUFs) are simultaneously combined allowing non-contact temperature detection, identification, and connection with the IoT environment through a smartphone. This is an intriguing example where luminescent inks based on organic-inorganic hybrids modified by lanthanide ions are used to fabricate a smart label that can sense temperature with remarkable figures of merit, including maximum thermal sensitivity of S-r = 1.46 %K-1 and temperature uncertainty of delta T = 0.2 K, and an authentication methodology accuracy, precision, and recall of 96.2%, 98.9%, and 85.7%, respectively. The methodology proposed is feasibly applied for the univocal identification and mobile optical temperature monitoring of individuals, allowing the control of the access to restricted areas and the information transfer to medical entities for post medical evaluation towards a new generation of mobile-assisted eHealth (mHealth).




Computer Science; Engineering; Telecommunications


Dias, LMS; Ramalho, JFCB; Silverio, T; Fu, LS; Ferreira, RAS; Andre, PS

nossos autores


This work was supported in part by the CICECO-Aveiro Institute of Materials under Project UIDB/50011/2020, Project UIDP/50011/2020, and Project LA/P/0006/2020; in part by the Instituto de Telecomunicacoes under Grant UIDB/50008/2020 and Grant UIDP/50008/2020; and in part by the Graphsense funded by National Funds through the FCT/MEC (PIDDAC) when appropriate Co-Financed by FEDER under the PT2020 Partnership through European Regional Development Fund (ERDF) in the Frame of Operational Competitiveness and Internationalization Program (POCI) under Grant POCI-01-0145-FEDER-032072. The work of Lilia M. S. Dias was supported by the SOLPOWINS under Grant PTDC/CTM-REF/4304/2020.

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