Leveraging machine learning for optimize predictive classification and scheduling E-Health traffic

Wireless Body Area Network (WBAN) is a special kind of autonomous sensor network evolved to provide wide variety of services. Nowadays WABN becomes an integral component of healthcare management system where a patient needs to be monitors both inside and outside home or hospital. These applications are responsible for gathering and managing heterogeneous data in terms of both for real time and non-real time traffic. Heterogeneous traffic classification plays an important role in various application of WBAN. Due to the ineffectiveness of traditional port-based and payload-based methods, recent work were proposed using machine learning methods to classify flows based on statistical characteristics. In this paper, we evaluate the effectiveness of integral concept of machine learning in terms of binary decision tree and genetic algorithm for classification of heterogeneous traffic flow according to rules. We have also designed an Earliest Deadline based flexible dynamic scheduling algorithm, which has been proven to be an optimal prioritized scheduling for problem like starvation.