Building platform application big sensor data for e-health wireless body area network

Wireless Body Area Network is a wireless network of wearable computing devices for monitoring the condition of a human body. The benefit is very big in healthcare with the ability to remotely monitor the patients, and the cost is not too expensive. Today, almost every medical organization utilize these technology to retrieve patient data. It is necessary to find solutions for processing such a big sensor data with the growth of data is growing every second. The concept of rapid growth of data with large volume data and variance data types are characteristic of Big Data. This study focuses on the manufacture of systems integration of sensor data into the database and visualization of data. By applying technology Hadoop Pseudo-distributed Mode as the system architecture to address the problems of big data growth on WBAN sensor data. Hadoop is famous reliable in large-scale data processing by using Hive as database managers better compared to traditional RDBMS. The purpose of this paper is to create a platform application for loading raw sensor data from e-Health to Hadoop system, then processing raw data so that can be further analyzed. The application visualize the data from Hive database into a web page and provide API service for client to access the sensor data. The results of comparative analysis between Hadoop Pseudo-distributed Mode system with traditional RDBMS in terms of importing data and query shows that Hadoop can perform these tasks with a much faster and the process is not too heavy for the case of large sensor data.