The Internet of Things based medical emergency management using Hadoop ecosystem

The prevalence of Internet of Things (IoT) in medical health care is bound to generate the massive volume of heterogeneous data due to the millions of medical sensors attached with various patients' body. Therefore, to process such amount of heterogeneous data in real-time to take emergency actions in critical health situation is a challenging task. Therefore, to address such issues, we proposed Hadoop-based medical emergency management system using IoT technology, which involves a network architecture with the enhanced processing features for collecting data received from millions of medical sensors attached to the human body. The amount of collected data is then forwarded to the Intelligent Building to process and perform necessary actions using various units such as, collection unit, Hadoop Processing Unit (HPU), and Analysis and decision unit. The feasibility and efficiency of the proposed system are evaluated by implementing the system on Hadoop using UBUNTU 14.04 LTS coreTMi5 machine. Sample medical, sensory datasets and real-time network traffic are considered to evaluate the efficiency of the system. The results show that the proposed system efficiently process WBAN sensory data.