Skip to main content
Log in

IoT-Based Healthcare Framework for Biomedical Applications

  • Original Article
  • Published:
Journal of Medical and Biological Engineering Aims and scope Submit manuscript

Abstract

Internet of Things (IoT) based biomedical applications are deployed in biomedical systems such as healthcare/telecare, diagnosis, prevention, treatment, monitoring. Wireless body area networks (WBANs) and radio frequency identification (RFID) systems, also called as enabling technologies, are key components of the IoT concept. In this study, a new IoT-based healthcare framework associated with WBANs and RFID technologies is built for hospital information systems. The designed framework has been modeled and simulated using Riverbed Modeler software. The results show that QoS criteria for data rate and latency specified by ISO/IEEE 11073 standard are satisfied by using the proposed energy-aware system. It is also demonstrated that some case studies considered in hospital information systems can be easily realized by the proposed framework by forming a time-saving simulation environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010.

    Article  MATH  Google Scholar 

  2. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys and Tutorials, 17(4), 2347–2376. https://doi.org/10.1109/COMST.2015.2444095.

    Article  Google Scholar 

  3. Pang, Z., & Tian, J. (2013). Ecosystem analysis in the design of open platform based in-home healthcare terminals towards the internet-of-things. In 15th International conference on advanced communication technology (pp. 529–534). https://doi.org/10.1109/ICACT.2014.6779195

  4. Yang, G., Xie, L., Mäntysalo, M., Zhou, X., Pang, Z., Xu, L. Da, et al. (2014). A Health-IoT platform based on the integration of intelligent packaging, unobtrusive bio-sensor, and intelligent medicine box. IEEE Transactions on Industrial Informatics, 10(4), 2180–2191. https://doi.org/10.1109/TII.2014.2307795.

    Article  Google Scholar 

  5. Luo, J., Chen, Y., Tang, K., & Luo, J. (2009). Remote monitoring information system and its applications based on the internet of things. In International conference on future BioMEdical information engineering (pp. 482–485). https://doi.org/10.1109/FBIE.2009.5405813

  6. Cubo, J., Nieto, A., & Pimentel, E. (2014). A cloud-based Internet of Things platform for ambient assisted living. Sensors, 14, 14070–14105. https://doi.org/10.3390/s140814070.

    Article  Google Scholar 

  7. Rohokale, V. M., Prasad, N. R., & Prasad, R. (2011). A cooperative Internet of Things (IoT) for rural healthcare monitoring and control. In 2011 2nd international conference on wireless communication, vehicular technology, information theory and aerospace and electronic systems technology, wireless VITAE 2011. https://doi.org/10.1109/WIRELESSVITAE.2011.5940920

  8. Movassaghi, S., Abolhasan, M., Lipman, J., Smith, D., & Jamalipour, A. (2014). Wireless body area networks: A survey. IEEE Communications Surveys and Tutorials, 16(3), 1658–1686. https://doi.org/10.1109/surv.2013.121313.00064.

    Article  Google Scholar 

  9. Yuce, M. R. (2010). Implementation of wireless body area networks for healthcare systems. Sensors and Actuators A: Physical, 162(1), 116–129. https://doi.org/10.1016/j.sna.2010.06.004.

    Article  Google Scholar 

  10. Lee, D. S., Bhardwaj, S., Alasaarela, E., & Chung, W. Y. (2007). An ECG analysis on sensor node for reducing traffic overload in u-healthcare with wireless sensor network. In Proceedings of IEEE Sensors (pp. 256–259). https://doi.org/10.1109/ICSENS.2007.4388385

  11. Hu, F., Xiao, Y., & Hao, Q. (2009). Congestion-aware, loss-resilient bio-monitoring sensor networking for mobile health applications. IEEE Journal on Selected Areas in Communications, 27(4), 450–465. https://doi.org/10.1109/JSAC.2009.090509.

    Article  Google Scholar 

  12. Rahim, M. R. A., Rashid, R. A., Ariffin, S. H. S., Fisal, N., Sarijari, M. A., & Abdul Hamid, A. H. F. (2011). Testbed design for Wireless Biomedical Sensor Network (WBSN) application. In IEEE conference on computer applications and industrial electronics, ICCAIE 2011, December 4, 2011December 7, 2011, (ICCAIE) pp. 284–289). https://doi.org/10.1109/ICCAIE.2011.6162146

  13. Gundogdu, K., & Calhan, A. (2016). An implementation of wireless body area networks for improving priority data transmission delay. Journal of Medical Systems, 40(3), 75. https://doi.org/10.1007/s10916-016-0443-3.

    Article  Google Scholar 

  14. Zhang, Z., & Hu, X. (2013). ZigBee based wireless sensor networks and their use in medical and health care domain. In … Seventh International Conference on Sensing … (pp. 756–761). https://doi.org/10.1109/ICSensT.2013.6727754

  15. Sevin, A., Bayilmis, C., & Kirbas, I. (2016). Design and implementation of a new quality of service-aware cross-layer medium access protocol for wireless body area networks. Computers & Electrical Engineering, 56, 145–156. https://doi.org/10.1016/j.compeleceng.2016.02.003.

    Article  Google Scholar 

  16. Tachtatzis, C., Di Franco, F., Tracey, D. C., Timmons, N. F., & Morrison, J. (2012). An energy analysis of IEEE 802.15.6 scheduled access modes for medical applications. Ad Hoc Networks, 89, 209–222. https://doi.org/10.1007/978-3-642-29096-1_15.

    Article  Google Scholar 

  17. Chen, G., Chen, W., & Shen, S. (2014). 2L-MAC: A MAC protocol with two-layer interference mitigation in wireless body area networks for medical applications. In 2014 IEEE International Conference on Communications (ICC) (pp. 3523–3528). https://doi.org/10.1109/ICC.2014.6883867

  18. He, D., & Zeadally, S. (2015). An analysis of RFID authentication schemes for internet of things in healthcare environment using elliptic curve cryptography. IEEE Internet of Things Journal, 2(1), 72–83. https://doi.org/10.1109/JIOT.2014.2360121.

    Article  Google Scholar 

  19. OPNET Technologies – Network Simulator | Riverbed. Retrieved March 28, 2017 from http://www.riverbed.com/gb/products/steelcentral/opnet.html?redirect=opnet.

  20. Jurcik, P., Koubâa, A., Alves, M., & Tovar, E. (2007). A simulation model for the IEEE 802.15 .4 protocol: Delay/throughput evaluation of the GTS mechanism. In Proceedings in 15th IEEE international symposium on modeling, analysis, and simulation of computer and telecommunication systems (MASCOTS 2007) (pp. 109–116). https://doi.org/10.1109/MASCOTS.2007.4

  21. Farahani, S. (2008). ZigBee wireless networks and transceivers. USA: Elsevier.

    Google Scholar 

  22. Irmak, E., Kose, A., & Gocmen, G. (2016). Simulation and Zigbee based wireless monitoring of the amount of consumed energy at smart homes. In 5th International conference on renewable energy research and applications (ICRERA 2016) (pp. 1019–1023).

  23. Asaduzzaman, A., Chidella, K. K., & Mridha, M. F. (2015). A time and energy efficient parking system using Zigbee communication protocol. Proceedings of the IEEE SoutheastCon, 2015, 1–5. https://doi.org/10.1109/SECON.2015.7132927.

    Article  Google Scholar 

  24. MicaZ. Retrieved January 4, 2017 from http://www.openautomation.net/uploadsproductos/micaz_datasheet.pdf.

  25. OWASP Top 10 Proactive Controls Project. Retrieved June 23, 2017 from https://www.owasp.org/images/9/9b/OWASP_Top_10_Proactive_Controls_V2.pdf.

  26. RFID Communication Protocol. Retrieved January 4, 2017 from http://shodhganga.inflibnet.ac.in/bitstream/10603/19096/11/11_chapter%204.pdf.

  27. Zheng, F., & Kaiser, T. (2016). Adaptive Aloha anti-collision algorithms for RFID systems. EURASIP Journal on Embedded Systems, 2016(1), 7.

    Article  Google Scholar 

  28. RFID Protocols. Retrieved March 28, 2017 from http://www.enigmatic-consulting.com/Communications_articles/RFID/RFID_protocols.html.

  29. Marino, F., Massei, G., & Paura, L. (2013). Modeling and performance simulation of EPC Gen2 RFID on OPNET. In IEEE international workshop on measurements and networking proceedings (M&N) (pp. 83–88).

  30. Hassanalieragh, M., Page, A., Soyata, T., Sharma, G., Aktas, M., Mateos, G., Kantarci, B., & Andreescu, S. (2015). Health monitoring and management using internet-of-things (IoT) Sensing with cloud-based processing: Opportunities and challenges. In Proceedings - 2015 IEEE international conference on services computing, SCC 2015 (pp. 285–292). https://doi.org/10.1109/SCC.2015.47

Download references

Acknowledgement

The authors would like to acknowledge that this work is supported by the Internet of Things Laboratory (IoTRLab-http://www.iotlab.sakarya.edu.tr/) at Sakarya University.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Faruk Aktas.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aktas, F., Ceken, C. & Erdemli, Y.E. IoT-Based Healthcare Framework for Biomedical Applications. J. Med. Biol. Eng. 38, 966–979 (2018). https://doi.org/10.1007/s40846-017-0349-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40846-017-0349-7

Keywords

Navigation