A Link Quality Estimator for Power-Efficient Communication Over On-Body Channels

Abstract

Abstract-The human body has an important effect on the performance of on-body wireless communication systems. Given the dynamic and complex nature of the on-body channels, link quality estimation models are crucial in the design of mobility management protocols and power control protocols. In order to achieve a good estimation of link quality in WBSNs, we combine multiple body-related factors into a model that includes: the transmission power, the body position, the body shape and composition characteristics and the received signal strength indicator (RSSI) as an indicator of link quality. In this paper, we propose the Anfis Link Quality Estimator (A-LQE) that has been trained with RSSI values measured at different transmission power levels in a sample of 37 human subjects. Once the accuracy and reliability of our proposed model have been analysed, we apply the model to adapt the transmission power to the link characteristics for energy optimization. The obtained average energy savings reach the 26% in comparison with the maximum transmission power mode.

Predicting types of failures in wireless sensor networks using an adaptive neuro-fuzzy inference system

“…In [31], ANFIS is used to estimate the RSSI values on body sensors. The complex nature of on-body channels are measured based on parameters from different layers such as transmission power and body positions of application layer, and RSSI of physical layer.…”

“…Common generalisation of a link performance as good, intermediate or bad does not provide sufficient information for WSN optimisation protocol to execute optimally [12,25]. For instance, increasing transmit power may improve the delivery reliability of long distance communicating nodes [31]. However, this may not be true for nodes suffering from channel access failure under persistent Wi-Fi interference [13].…”

Proactive and Reactive Transmission Power Control for Energy-Efficient On-Body Communications

“…Below, we shortly describe the A-LQE model; a more profuse description and motivation of the model can be found in [ 7 ]. In this paper, we will further exploit the proposed model to develop two reactive and proactive policies for transmission power control, which have been validated in a realistic simulation scenario.…”

“…In this section, we shortly describe our approaches for the transmission power control (TPC approaches) for WBSNs, which were initially introduced in [ 6 , 7 ]. We present again the main ideas of these algorithms, as the work presented here is an experimental validation of both approaches in a realistic simulation scenario, as well as the upgrade of the Castalia simulator to support both TPC policies.…”

“…Therefore, these techniques allow meeting the reliability constraints, while at the same time saving energy. In this paper, we extend our previously proposed transmission power control algorithms ([ 6 , 7 ]) to a complex and realistic scenario. For that purpose, a state-of-the-art WBSN simulator has been upgraded with our model of the biologic channel and an efficient implementation of our reactive and predictive control techniques.…”