Behavioral Reconfigurable Compression in Body Sensor Networks

Body area sensor networks have been attracting more and more applications which focus on human behavior and monitoring, ranging from simple positioning to medical applications. These BSNs inherit unique specifications since are composed of light-weight embedded systems. In this paper, we focus on energy and lifetime requirements of these systems which is one of the most challenging design constraints. We study this problem from the angle of data compression which is known to be very efficient in energy reduction spe- cially when large amount of data is to be transmitted wirelessly. We introduce the notion of functional compression which utilizes classes of data patterns to efficiently represent information through regenerative functions. Furthermore, we propose a reconfigurable compression methods which dynamically uses different compression methods to optimize compression ratio and energy savings.