IEEE 802.15.6 provides PHY and MAC layer profiles for wearable and implanted Wireless Body Area Networks (WBANs). The critical requirements of QoS guarantee and ultra-low-power are severe challenges when implementing IEEE 802.15.6. In this paper, the key problem in IEEE 802.15.6 standard that “How to allocate EAP (Exclusive Access Phase)?” is solved for the first time: An analysis of network performance indicates that too much EAP allocation can not promote traffic performance obviously and effectually. However, since EAP allocation plays an important role in guaranteeing quality of service, a customized and quantitative EAP allocation solution is proposed. Simulation results show that the solution can obtain the optimal network performance. Furthermore, the estimated models of delay and energy are developed, which help to design the WBAN according to application requirements and analyze the network performance according to the traffic characteristics. The models are simple, effective, and relatively accurate. Results show that the models have approximated mean and the correlation coefficient is greater than 0.95 compared with the simulations of IEEE 802.15.6 using NS2 platform. The work of this paper can solve crucial practical problems in using IEEE 802.15.6, and will propel WBANs applications widely.

Wireless body area networks (WBANs) have to work with low power and long lifetime to satisfy human biological safety requirements in e-health; therefore extremely low energy consumption is significant for WBANs. IEEE 802.15.6 standard has been published for wearable and implanted applications which provide communication technology requirements in WBANs. In this paper, the cross-layering optimization methodology is used to minimize the network energy consumption. Both the priority strategy and sleep mechanism in IEEE802.15.6 are considered. Macroscopic sleep model based on WBAN traffic priority and microscopic sleep model based on MAC structure are proposed. Then the network energy consumption optimization problem is solved by Lagrange dual method, the master problem are vertically decomposed into two sub problems in MAC and transport layers which are dealt with gradient method. Finally, a solution including self-adaption sleep mechanism and node rate controlling is proposed. The results of this paper indicate that the algorithm converges quickly and reduces the network energy consumption remarkably.