Application-Focused Energy-Fidelity Scalability for Wireless Motion-Based Health Assessment

Abstract

Energy-fidelity trade-offs are central to the performance of many technologies, but they are essential in wireless body area sensor networks (BASNs) due to severe energy and processing constraints and the critical nature of certain healthcare applications. On-node signal processing and compression techniques can save energy by greatly reducing the amount of data transmitted over the wireless channel, but lossy techniques, capable of high compression ratios, can incur a reduction in application fidelity. In order to maximize system performance, these trade-offs must be considered at runtime due to the dynamic nature of BASN applications, including sensed data, operating environments, user actuation, etc. BASNs therefore require energy-fidelity scalability, so automated and user-initiated trade-offs can be made dynamically. This article presents a data rate scalability framework within a motion-based health application context which demonstrates the design of efficient and efficacious wireless health systems.