Smart Nanosensor Networks for Body Injury Detection

Abstract

Nanosensors synthesize the most recent advantages of nanomaterials and biosensing technologies. Injury detection is one of the important areas of nanosensor applications in healthcare. It is especially useful to the injuries that are more difficult to diagnose at the early stage with the traditional medical methods. This paper focuses on the nanosensor networks for human body injury detection. After reviewing recent progress in the biomedical nanosensor development, an architecture is proposed to employ nanosensors to collect the bio-parameters of the injury part in a human body. Key elements of the architecture are nanosensors, data collectors, medical servers, as well as healthcare providers. Major functions on the biomedical data processing are analyzed in a structure with three layers: sensing layer, networking layer, and application layer. Each layer conducts different data processing functionalities to facilitate sensing, collecting, and analyzing of the vitals. Based on the IEEE nano-scale communication framework, a mathematical model is further derived. This model represents the trade-off between the nanosensor network resource and its injury detection performance. The problem constraints describe the characteristics of the patient body and the injury part. Simulations are conducted in several sets of typical cases to evaluate the model performance. Results demonstrate that the nanosensor amount selection is determined by multiple bio-factors of the human body and the injury part.