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.

I. Introduction

With the increase of the aging population, the demands for health monitoring system have witnessed a huge increase [1]. Internet-of-Things (IoT) technology has been considered as a revolutionary solution to provide this service outside hospitals and save public healthcare resources [2]. IoT-based body sensor network (BSN) [3] is a healthcare platform, which is composed of several low-power and lightweight wireless medical sensors and one personal digital assistant (PDA). In the BSN platform, many applications are proposed to provide emergency response services [4] and monitoring services [5], which can be used for diagnosis, well-being management, and elderly care [6].