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Journal of Medical Systems

, 40:283 | Cite as

Real-time Medical Emergency Response System: Exploiting IoT and Big Data for Public Health

  • M. Mazhar Rathore
  • Awais Ahmad
  • Anand PaulEmail author
  • Jiafu Wan
  • Daqiang Zhang
Transactional Processing Systems
Part of the following topical collections:
  1. Advances in Big-Data based mHealth Theories and Applications

Abstract

Healthy people are important for any nation’s development. Use of the Internet of Things (IoT)-based body area networks (BANs) is increasing for continuous monitoring and medical healthcare in order to perform real-time actions in case of emergencies. However, in the case of monitoring the health of all citizens or people in a country, the millions of sensors attached to human bodies generate massive volume of heterogeneous data, called “Big Data.” Processing Big Data and performing real-time actions in critical situations is a challenging task. Therefore, in order to address such issues, we propose a Real-time Medical Emergency Response System that involves IoT-based medical sensors deployed on the human body. Moreover, the proposed system consists of the data analysis building, called “Intelligent Building,” depicted by the proposed layered architecture and implementation model, and it is responsible for analysis and decision-making. The data collected from millions of body-attached sensors is forwarded to Intelligent Building for processing and for performing necessary actions using various units such as collection, Hadoop Processing (HPU), and analysis and decision. The feasibility and efficiency of the proposed system are evaluated by implementing the system on Hadoop using an UBUNTU 14.04 LTS coreTMi5 machine. Various medical sensory datasets and real-time network traffic are considered for evaluating the efficiency of the system. The results show that the proposed system has the capability of efficiently processing WBAN sensory data from millions of users in order to perform real-time responses in case of emergencies.

Keywords

Big data IoT Healthcare Intelligent building Hadoop ecosystem 

Notes

Acknowledgments

This study was supported by the Brain Korea 21 Plus project (SW Human Resource Development Program for Supporting Smart Life) funded by the Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (21A20131600005). This work was also supported by the IT R&D Program of MSIP/IITP. [10041145, Self-Organized Software Platform (SoSp) for Welfare Devices].

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • M. Mazhar Rathore
    • 1
  • Awais Ahmad
    • 1
  • Anand Paul
    • 1
    Email author
  • Jiafu Wan
    • 2
  • Daqiang Zhang
    • 3
  1. 1.The School of Computer Science and EngineeringKyungpook National UniversityDaeguSouth Korea
  2. 2.The School of Mechanical and Automotive EngineeringSouth China University of TechnologyGuangzhouChina
  3. 3.The School of Software EngineeringTongji UniversityTongjiChina

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