Modeling and Analysis of Abnormality Detection in Biomolecular Nano-Networks

TL;DR

This paper proposes a biomolecular nano-network scheme for early abnormality detection, combining sensor nano-machines and data gathering nodes, with performance analysis and optimization for disease diagnosis.

Contribution

It introduces a novel two-tier nano-network model for abnormality detection with performance analysis and an optimization framework for sensor concentration.

Findings

Performance metrics for detection accuracy are derived.

Optimal sensor concentration for high detection probability is identified.

The scheme effectively detects abnormalities with controlled false alarms.

Abstract

A scheme for detection of abnormality in molecular nano-networks is proposed. This is motivated by the fact that early diagnosis, classification and detection of diseases such as cancer play a crucial role in their successful treatment. The proposed nano-abnormality detection scheme (NADS) comprises of a two-tier network of sensor nano-machines (SNMs) in the first tier and a data gathering node (DGN) at the sink. The SNMs detect the presence of competitor cells as abnormality that is captured by variations in parameters of a nano-communications channel. In the second step, the SNMs transmit micro-scale messages over a noisy micro communications channel (MCC) to the DGN, where a decision is made upon fusing the received signals. The detection performance of each SNM is analyzed by setting up a Neyman-Pearson test. Next, taking into account the effect of the MCC, the overall performance of the proposed NADS is quantified in terms of probabilities of misdetection and false alarm. A design problem is formulated, when the optimized concentration of SNMs in a sample is obtained for a high probability of detection and a limited probability of false alarm.