Abstract
Thrombosis is one of the leading causes of death worldwide. Out of four, one person is dying of thrombosis; yet, the seriousness of this disease is underappreciated. Its early prediction and prevention continue to be a dilemma that confuses researchers. Nevertheless, a light can be seen at the end of the tunnel; thanks to nanoscience which has led to the development of new generations of nanostructure with different applications in bio-medicine and bio-engineering. The key paradigm for the Internet of Nano Things (IoNT) has allowed for new medical data to be collected which potentially helps achieve more accurate disease prediction. It has enabled real-time health services and turned the physical space of a patient into a smart space. While an enabler for several applications, the artificial nature of Internet of Nano Things devices can be harmful where the implementation of Nano Things may lead to unintended health effects. To overcome this issue, researchers have suggested the novel paradigm of the IoBNT that combines nanotechnology with tools from synthetic biology to provide reengineering of biological embedded computing devices. IoBNT promises many medical applications, such as intra-body sensing and actuation networks, based on biological cells and their characteristics in the biochemical field. In this paper, a novel IoBNT-based model with an optimized Bio-Cyber communication interface that helps predict and analyze blood vessel clots is introduced. The model utilizes a bio-interface to collect information on the blood vessels and convert it into an electrical equivalent format. Furthermore, the optical or thermal responsiveness excites the release of definite nano-carrier molecules such as liposomes which may be devised across the bloodstream and enter the targeted area passively to stimulate suitable nano-devices to predict the clots. The Bio-Cyber interface is used for linking the traditional electromagnetic wave to the Bio-Signaling Network based on the bioluminescence concept. Lab-scale simulation analysis shows prominent outcomes in the prediction of blood vessel clots with 97.66% accuracy and 12.22% tolerance level in error rate.