Channel autocorrelation-based dynamic slot scheduling for body area networks

Abstract

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.

Step Length Measurements Using the Received Signal Strength Indicator

“…In this paper, we focus on the distance measurement between two human body parts in WBANs scenarios using the hardware-based RSSI approach. Inspired by our previous related work [ 2 , 24 , 25 ], we first develop the transceivers and use them to measure their distance when they are attached to the human ankles. We then propose an experimental path loss model between the two ankles of the subject under test, that jointly considers the path loss, shadowing effect, multipath, mismatch loss, and insertion loss.…”

Joint Transmission Power Control and Relay Cooperation for WBAN Systems

“…For each measurement, there are five trace files, and we call the five trace files as a channel dataset. More details about the measurements and the channel datasets can be found in [4]. In this study, 16 individual channel datasets collected from real daily WBAN scenarios are considered.…”

“…To improve the authenticity of performance evaluation, the 16 channel datasets (CD1–CD16) collected from our measurement campaigns were imported into the simulation model to represent the variation of actual on-body channels in the daily scenarios. More details about the measurement campaigns can be found in [4,44]. We compared the performance of the following methods with or without two-hop cooperation: Static scheme: The hub does not control the transmission power level of the sensor nodes.…”

“…On the one hand, due to the presence of the human body and the complexity of the surrounding environment, the wireless channels in WBAN exhibit significant peculiarity in comparison with other Wireless Sensor Network (WSN) channels [2,3]. Many factors contribute to the severe signal attenuation (over 100 dB has been observed [4]) of WBAN channels. These factors include diffraction, reflection, energy absorption, antenna losses, shadowing by the body tissue, and body posture.…”

“…In this paper, we focus on the adaptive transmission protocol in real daily life WBAN scenarios. Inspired by the autocorrelation analyses presented in the literature [4,42,43], we propose an Autocorrelation-based Adaptive Transmission (AAT) scheme that jointly considers TPC, Dynamic Slot Scheduling (DSS) and two-hop cooperation. Firstly, the slot scheduling results are taken into account to optimize the transmission power level on the sensor side.…”