Age-of-Information-Constrained Transmission Optimization for ECG-Based Body Sensor Networks

The electrocardiogram sensor network (ECG-SN) is a medical monitoring system based on IoT technology, which can detect heart bioelectric signals in real time. But the ECG signal is vulnerable to human mobility and sensitive to the Age of Information (AoI). In this article, we first analyze the impact of human mobility on channel fading based on a real-world activity trace data set and design a two-state ECG work model based on the tradeoff between the ECG signal quality and energy consumption. Furthermore, an AoI model is proposed to evaluate the data timeliness. Then, an online transmission optimization algorithm is proposed to maximize the system utility by optimizing the sampling rate, transmission power, and data dropping rate. Furthermore, performance analysis presents the bounds for data buffer, battery capacity, and AoI. Numerical results show the dynamics of the system and the impact of the parameters on system performance, which verify that the proposed design has a larger utility and a smaller AoI in comparison with two benchmark schemes.

Automated summary

The ECG sensor network is a medical monitoring system using IoT technology to detect heart bioelectric signals in real time. The system design includes analyzing the impact of human mobility on channel fading, a two-state ECG work model, an AoI model for data timeliness evaluation, and an online transmission optimization algorithm for maximizing system utility. Performance analysis shows the dynamics and impact of parameters, with proposed design offering larger utility and smaller Age of Information compared to benchmark schemes.