Performance analyses of the IEEE 802.15.6 Wireless Body Area Network with heterogeneous traffic

Abstract

Recent advance in wireless communication and electronics industry has allowed the development of a wide range of biosensors to be used for Wireless Body Area Networks (WBANs) and Internet of Medical Things (IoMTs) which are emerging technologies for health monitoring. IEEE 802.15.6 is the first international WBANs’ standard, developed by the IEEE Task Group-6 to fulfill WBANs requirements for wireless communication around the human body, by supporting short range communications, high quality of service and extremely low power consumption. In many WBANs applications, sensor nodes must manage various types of traffic due to the fact that the monitored vital signs have different requirements in terms of reliability and latency. Many analytical models were proposed in the literature to study and assess the standard performances. However, taking into consideration heterogeneous traffic in the proposed models is overlooked in most, if not all, of existing work. For this aim, we propose in this paper, a general analytical model for performance evaluation of the IEEE 802.15.6 based WBANs with heterogeneous traffic in terms of priority. The proposed model is composed of two complementary sub-models. The first is a renewal reward based analytical sub-model that efficiently describes the IEEE 802.15.6 CSMA/CA Backoff process. The second sub-model is an M/G/1 queuing model with non-preemptive priority. To the best of our knowledge, this is the first analytical analysis of the IEEE 802.15.6 standard with heterogeneous traffic in terms of priority. Using Matlab and Maple, we conducted an analysis of the proposed analytical model under ideal channel conditions and saturated network traffic regime. Besides, to validate the proposed model, we performed simulations of the IEEE 802.15.6 standard using Castalia Simulator based on OMNeT++. Results showed the accuracy of the proposed model for managing WBANs with heterogeneous traffic.