Wireless Body Area Networks with Compressed Sensing Theory

Abstract

Wireless Body Area Networks (WBANs) consist of small intelligent wireless sensors attached on or implanted in the body. These wireless sensors are responsible for collecting, processing, and transmitting vital information such as: blood pressure, heart rate, respiration rate, electrocardiographic (ECG), electroencephalography (EEG) and oxygenation signals to provide continuous health monitoring with real-time feedback to the users and medical centers. In order to fully exploit the benefits of WBANs for important applications such as Electronic Health (EH), Mobile Health (MH), and Ambulatory Health Monitoring (AHM), the power consumption must be minimized. Since Wireless Nodes (WNs) in WBANs are usually driven by a battery, power consumption is the most important factor to determine the life of WBAN .The life expectancy of a WBAN for a given battery capacity can be enhanced by minimizing power consumption during the operation of the network. CS theory solves the aforementioned problem by reducing the sampling rate throughout the network. A combination of CS theory to WBANs is the optimal solution for achieving the networks with low-sampling rate and low-power consumption. Our simulation results show that sampling rate can be reduced to 25% without sacrificing performances by employing the CS theory to WBANs. This paper presents a novel sampling approach using compressive sensing methods to WBANs.