An attack resistant physical unclonable function smart optical sensors for internet of things for secure remote sensing

Abstract

COVID-19 pandemic scenario makes extensive use of remote temperature sensors, using IoT devices and communications. Earlier, flexible organic-inorganic luminous inks were used to design and print univocal smart labels based on QRCs (quick response codes) on medical substrates (protective masks and adhesive). Through simultaneous combination of luminescence thermometry and PUFs (physical unclonable functions), temperature measurements in non-contacts, identifications, and connections with IoTs. PUFs are extremely helpful for hardware security, but are open to attacks from MLTs (machine learning techniques) that mimic the behaviours of CRPs (controvert-response pairs). The study suggests ARPUFs architecture that implements two-round controverts to randomise the mapping of CRPs in order to satisfy the unpredictability requirements for resistances, intriguing examples on the usages of organic/inorganic hybrids luminous inks changed by lanthanide ions for creating smart labels with the ability of sensing temperatures including maximal thermal sensitivities up to 1.46%K and uncertainties of 0.2 K, along with authentications like methodology accuracies (98.4%), precisions (99.6%), and recalls (87.7%). Proposed methodology of univocal identifications and monitoring of mobile optical temperatures in individuals, enable control of accesses to restricted areas and medical information transfers for post-medical evaluation results in opening the door to new generations of mobile-assisted eHealths (mHealth-mobile-assisted eHealths).

Keywords

• Remote temperature sensing
• Internet of things
• Covid-19
• Quick response codes
• Physical unclonable functions
• Machine learning and attack resistant PUF