In Wireless Body Area Networks (WBANs), the tradeoff between network throughput and energy efficiency remains a key challenge. Most current transmission schemes try to cope with the challenge from the perspective of general Wireless Sensor Networks (WSNs), which may not take the peculiarities of WBAN channels into account. In this paper, we take advantage of the correlation of on-body channels in walking scenarios to achieve a better tradeoff between throughput and energy consumption. We first analyze the characteristics of on-body channels based on realistic channel gain datasets, which are collected by our customized wireless transceivers in walking scenarios. The analytical results confirm the rationale of our newly proposed transmission scheme A3NC, which explores the combination of the aggregative allocation (AA) mechanism in MAC layer and the Analog Network Coding (ANC) technique in PHY layer. Both theoretical analyses and simulation results show that the A3NC scheme achieves significant improvement in upload throughput and energy efficiency, compared to the conventional approaches.

As a promising technology in the context of m-health and e-medical, wireless body area networks (WBANs) have a stringent requirement in terms of transmission reliability. Meanwhile, the wireless channel in WBANs is prone to deep fading due to multiple reasons, such as shadowing by the body, reflection, diffraction, and interference. To meet the challenge in transmission reliability, the dynamic slot scheduling (DSS) methods have attracted considerable interest in recent years. DSS method does not require extra hardware or software overhead on the sensor side. Instead, the hub optimizes the time-division multiple access slots by selecting the best permutation at the beginning of each superframe to improve the transmission reliability. However, most existing DSS works optimize the time slot scheduling based on a two-state ("good" or "bad") Markov channel model, which is insufficient for human daily life scenarios with a variety of irregular activities. In this paper, we first collect the channel gain data in the real human daily scenarios and analyze the autocorrelation of wireless channels based on this real database. Motivated by the significant temporal autocorrelation, we then propose a new DSS method, which applies a temporal autocorrelation model to predict the channel condition for future time slots. The new method is designed to be compatible with IEEE 802.15.6 standard. Compared to the classical Markov model-based methods, simulation results show that the newly proposed DSS method achieves up to 12.9% reduction in terms of packet loss ratios.