Dynamic EAP Based MAC Protocol for Wireless Body Area Networks

Abstract

Wireless body area networks are a network of sensors on, in the vicinity of, or implanted within the body for a variety of applications including medical monitoring. Due to the nature of these sensors, data transmission reliability and quality are essential. Furthermore, we must recognize that each type of body sensor holds a unique importance to each user and his or her specific health needs, an importance that can also change at any time. A user may have a multitude of different sensor combinations being measured with varying significance, and in order to ensure meaningful analysis, it is imperative that we have the highest possible data reliability. However, the current IEEE 802.15.6 [1] is static in general and cannot easily adapt to changing scenarios throughout the day. This paper presents a dynamic EAP based MAC protocol for WBANs that seeks to better suit the unique needs of users by dynamically adjusting the allocated slots of the Exclusive Access Phase (EAP) and Random Access Phase (RAP) of the IEEE 802.15.6 beacon period (superframe) structure to fit the transmission needs of the sensors, as well as utilizing packet prioritization to minimize resource competition during the RAP phase. The length of the EAP is determined by calculating the necessary transmission time of all the packets the EAP node has scheduled to send during the beacon period. The length of the RAP is calculated by weighting the remaining time by the lengths of the respective preceding EAP phase. Furthermore, each type of sensor is assigned to one of three ranks, Always Important, Sometimes Important, and Never Important, depending on their importance to the user. The purpose of these ranks is to categorize sensors based on the user’s unique needs and to ensure that sensor data with the highest importance are successfully collected while also ensuring that sensor data of lower ranked sensors have useful and acceptable throughput as well. Simulation results have shown that this protocol improves throughput for the most important data – data from Always Important sensors and Sometimes Important sensors with important data – with little to no decline in the overall throughput.