Age of Information-Based Abnormality Detection With Decay in the Human Circulatory System

Abstract

Detecting abnormalities early by deploying a network of mobile nanosensors within the human body remains a challenging task. Current methods for abnormality detection rely on placing gateways at arbitrary locations. Given the critical importance of timely monitoring and detection in severe infections, relying on arbitrary gateway locations introduces delays in detection. In this work, we conducted an analysis of the impact of gateway placement and infection locations on detection time, detection ratio, and the average Peak Age of Information (PAoI). Furthermore, we also added decay of nanosensors similar to operation in the human body. We investigated its implications on both the detection ratio of abnormalities and the average PAoI. We employed a Monte Carlo simulation involving 1000 nanosensors circulating in the HCS for 500 seconds. The results revealed that the favorable gateway position is at the heart, minimizing detection time and enhancing the detection ratio for various infection locations. Furthermore, we observed that the detection ratio exhibited reduced variance with increased decay rates in nanosensors. Analyzing the PAoI across varying decay rates highlighted the importance of nanosensor quantity in relation to decay rate in ensuring accurate and timely infection localization.