Abstract
A wireless body area network offers cost-effective solutions for healthcare infrastructure. An adaptive transmission algorithm is designed to handle channel efficiency, which adjusts packet size according to the difference in feature-point values that indicate biomedical signal characteristics. Furthermore, we propose a priority-adjustment method that enhances quality of service while guaranteeing signal integrity. A large number of simulations were carried out for performance evaluation. We use electrocardiogram and electromyogram signals as reference biomedical signals for performance verification. From the simulation results, we find that the average packet latency of proposed scheme is enhanced by 30% compared to conventional method. The simulation results also demonstrate that the proposed algorithm achieves significant performance improvement in terms of drop rates of high-priority packets around 0.3%−0.9 %.
1 Introduction
Recent advances in wireless communications technology and low power consumption devices make novel healthcare applications come true. The applications aim to monitor the condition of the body, and furthermore, diagnose disease that may occur. There are a few networks that can be applied to such applications, but a wireless body area network (WBAN) is the most ideal solution for wireless communications in portable, wearable, or implantable sensors that monitor biomedical signals [1]. A WBAN provides preferential delivery for multiple devices that require quality of service (QoS) guarantees by ensuring sufficient bandwidth, latency and jitter, and reducing data loss.
5 Conclusions
1) We propose a novel adaptive transmission scheme to improve QoS for medical devices in a WBAN. In a WBAN, there are various devices trying to transmit medical or non-medical data. The medical devices set the packet priority to an apposite value that fits the characteristics of the biomedical signal information to guarantee QoS and maximize the channel efficiency.
2) The proposed scheme extracts the feature points of a biomedical signal based on a curvature value, and adjusts packet size and packet priority with the extracted feature points that represent the human body information. In addition, the proposed scheme compresses non-medical data packets to reduce transmission overhead and adjusts the priority of emergency medical data (which has significant changes in signal) to guarantee QoS.
3) From the simulation results, we find that the average packet transmission latency of the proposed scheme is reduced. The simulation results also show that the average high-priority packet drop rate decreases even if the average offered load increases.
