Adaptive Cognitive Enhanced Platform for WBAN

Abstract

Rapid development in wireless sensor device manufacture technologies has made it possible to conglutinate and implant the medical sensors on human body. The improvement of these technologies can be applied to wireless body area network (WBAN) on medical applications. A uniform standard in the field of WBAN, IEEE 802.15.6 has been released recently by IEEE 802.15 TG 6 group, indicating that WBAN will play an important role in future E-health medical system field. This paper presents a WBAN validation platform, Adaptive Cognitive Enhanced Platform (ACEP), which is based on IEEE 802.15.6 and works in the spectrum of Industrial Scientific and Medical (ISM) 2.4GHz band. However, other wireless services occupying ISM band, such as WiFi, Bluetooth and Zigbee will cause great interference to low power, low data rate medical WBANs deployed nearby. To build the realistic co-channel interference scenario, a WiFi network working on the same spectrum is utilized. Furthermore, to strengthen WBAN's robustness against interference caused by WiFi, we have proposed and implemented a fast dynamic cognitive radio (FDCR) algorithm to stagger the spectrum accessing time, which offers an efficient method for coexistence of WBAN and other wireless systems. Through calculating the indicated WiFi temporal white-spaces for WBAN communication, FDCR algorithm minimizes the impact of co-channel interference and promotes resource allocation efficiency. Our simulation and platform validation experiments show that the proposed FDCR algorithm can greatly decrease packets drop rate and improve channel utilization.