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Performance Comparison of Brain Wave Transmission Network Protocol using Multi-Robot Communication Network of Medical Center

의료센터의 다중로봇통신망을 이용한 뇌파전송망 프로토콜의 성능비교

  • Received : 2012.10.16
  • Accepted : 2012.12.10
  • Published : 2013.01.28

Abstract

To verify the condition of patients moving in the medical center like hospital needs to be consider the various wireless communication network protocols and network components. Wireless communication protocols such as the 802.11a, 802.11g, and direct sequence has their specific characteristics, and the various components such as the number of mobile nodes or the distance of transmission range could affects the performance of the network. Especially, the network topologies are considered the characteristic of the brain wave(EEG) since the condition of patient is detected from it. Therefore, in this paper, various wireless communication networks are designed and simulated with Opnet simulator, then evaluated the performance to verify the wireless network that transmits the patient's EEG data efficiently. Overall, the 802.11g had the best performance for the wireless network environment that transmits the EEG data. However, there were minor difference on the performance result depends on the components of the topologies.

병원과 같은 의료센터에서 이동하는 환자들의 상태를 효과적이며 실시간으로 감지하기 위해서는 다양한 무선통신망 프로토콜과 네트워크 상황을 고려해야 한다. 802.11a, 802.11g, direct sequence와 같은 무선통신 프로토콜들은 각각의 장단점이 있으며 모바일 노드의 개수나 전파도달 거리등 다양한 요소들이 망의 성능에 영향을 줄 수 있다. 특히, 환자들의 상태를 뇌파전송(EEG)을 통해 감지하기 때문에 이러한 데이터 특성도 고려하여 네트워크 토폴로지를 구성하였다. 따라서, 본 논문에서는 환자의 EEG 데이터를 효율적으로 전송할 수 있는 무선통신망을 설계하고 이를 Opnet 시뮬레이터를 이용하여 시뮬레이션한 뒤 그 결과를 이용하여 성능을 분석하였다. EEG를 전송하는 무선네트워크 환경에서는 전반적으로 802.11g의 성능이 우수한 것으로 나타났으며 토폴로지의 구성요소에 따라 결과의 특성에 다소 차이가 있었다.

Keywords

References

  1. Prajakta Kulkarni and Yusuf Ozturk, "mPHASiS: Mobile patient healthcare and sensor information system," Journal of Network and Computer Applications, Elsevier, pp.402-417, 2011.
  2. N. Oliver and F. Flores-Mangas, "HealthGear: a real-time wearable system for monitoring and analyzing physiological signals," Microsoft Research Technical Report MSR-TR-2005-182, 2005.
  3. F. Delmastro, "Pervasive communications in healthcare," Computer Communications, Elsevier, pp.1284-1295, 2012.
  4. J. Schepps and A. Rosen, "Microwave industry outlook-wireless communications in healthcare," Microwave Theory and Techniques, Vol.50, Issue3, pp.1044-1045, 2002. https://doi.org/10.1109/22.989992
  5. U. Varshney and R. Vetter, "Emerging wireless and mobile networks," Communications of the ACM, Vol.43, No.6, pp.73-81, 2000.
  6. E. Lubrin, E. Lawrence, and K. E. Navarro, "Wireless remote healthcare monitoring with Motes," ICMB, pp.235-241, 2005.
  7. Abdulhamit Subasia and Ergun Ercelebi, "Classification of EEG signals using neural network and logistic regression," Computer Methods and Programs in Biomedicine, Elsevier, pp.87-99, 2005.
  8. http://en.wikipedia.org/wiki/Brainwave
  9. W. Zhao and M. H. Ammar, "Message ferrying: proactive routing in highly partitioned wireless ad hoc networks," Proceedings of the 9th IEEE Workshop on Future Trends of Distributed Computing Systems, Washington, DC, USA, pp.308-314, 2003.
  10. W. Zhao, M. H. Ammar, and E. Zegura, "A message ferrying approach for data delivery in sparse Mobile Ad Hoc Networks," Proceedings of the 5th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp.187-198, 2004.
  11. C. H. Ou, K. F. Ssu, and H .C. Jiau, "Connecting network partitions with location assisted forwarding nodes in mobile ad hoc environments," Proceedings of the 10th IEEE Pacific Rim International Symposium on Dependable Computing, pp.239-247, 2004.