Journal of Internet Computing and Services (인터넷정보학회논문지)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지

Balanced Clustering based on Mobile Agents for the Ubiquitous Healthcare Systems

유비쿼터스 헬스케어 시스템에서 이동에이전트 기반 균형화 클러스터링

  • 마테오 로미오 (군산대학교 전자정보공학부) ;
  • 이재완 (군산대학교 정보통신공학과) ;
  • 이말례 (전북대학교 영상정보신기술 연구 센터, 컴퓨터공학부)
  • Received : 2009.05.27
  • Accepted : 2009.12.29
  • Published : 2010.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

In the ubiquitous healthcare, automated diagnosis is commonly achieved by an agent system to provide intelligent decision support and fast diagnosis result. Mobile agent technology is used for efficient load distribution by migrating processes to a less loaded node which is considered in our design of a ubiquitous healthcare system. This paper presents a framework for ubiquitous healthcare technologies which mainly focuses on mobile agents that serve the on-demand processes of an automated diagnosis support system. Considering the efficient utilization of resources, a balanced clustering for the load distribution of processes within nodes is proposed. The proposed algorithm selects overloaded nodes to migrate processes to near nodes until the load variance of the system is minimized. Our proposed balanced clustering efficiently distributes processes to all nodes considering message overheads by performing the migration to the near nodes.

유비쿼터스 헬스케어에서 지능형 의사결정지원 및 빠른 진단결과를 제공하기 위한 자동진단은 일반적으로 에이전트 시스템에 의해 수행된다. 본 연구에서는 이동에이전트기술을 사용하여 저 부하 노드에 효율적으로 프로세스를 이주시켜 부하를 분산시키도록 유비쿼터스 헬스케어시스템을 설계하였다. 또한 실시간 자동진단시스템을 지원하는 이동에이전트 중심의 유비쿼터스 헬스케어 기술을 위한 프레임워크를 제시하며, 효율적인 자원활용을 고려하여, 노드들 내에 있는 프로세스의 부하분산을 위한 균형화된 클러스터링을 제안한다. 제안한 알고리즘은 시스템의 부하분산이 최소화될 때까지 과부하된 노드를 선택하여 프로세스를 가까운 노드에 이주시킨다. 제안한 균형화 클러스터링은, 가까운 노드에 이주시킴으로써 메시지오버헤드를 감안할 때, 효율적으로 프로세스를 모든 노드에 분산시킨다.

Keywords

References

  1. J. Jung, K. Ha, j. Lee, Y.S. Kim and D. Kim, "Wireless Body Area Network in a Ubiquitous Healthcare System for Physiological Signal Monitoring and Health Consulting", IJISP, Vol. 1, No. 1, pp. 47-54, 2008.
  2. B. L. Iantovics, "Cooperative Medical Diagnosis Elaboration by Physicians and Artificial Agents", Understanding Complex Systems, pp. 315-339, 2009. https://doi.org/10.1007/978-3-642-02199-2_16
  3. M. Rodríguez and J. Favela, "Autonomous Agents to Support Interoperability and Physical Integration in Pervasive Environments". Proc. of AWIC, pp. 307-317, 2003.
  4. P. Bellavista, A. Corradi and C. Stefanelli, "Mobile Agent Middleware for Mobile Computing", Computer, Vol. 34, No. 3, pp. 73-81, 2001. https://doi.org/10.1109/2.910896
  5. S. W. Han, Y. B. Yoon, H. Y. Youn W. D. Cho, "A New Middleware Architecture for Ubiquitous Computing Environment", Proc. of STFEUS, pp. 117-121, 2004.
  6. O. Shehory, K. Sycara, P. Chalasani, S. Jha, "Agent Cloning: An Approach to Agent Mobility and Resource Allocation", IEEE Communications Magazine, Vol. 36, No. 7, pp. 63-67, 1998.
  7. H.A. Thant, K.M. San, K.M.L. Tun, T.T. Naing, N. Thein, "Mobile Agents Based Load Balancing Method for Parallel Applications". APSITT 2005 Proceedings, pp.77-82, 2005.
  8. Y.Yang, Y.Chen, X.Cao1, J.Ju1, "Load Balancing Using Mobile Agent and a Novel Algorithm for Updating Load Information Partially", Springer Verlag, LNCS 3619, pp. 1243-1252
  9. R. M. Mateo, L. F. Cervantes, H. K. Yang, and J. W. Lee "Mobile Agents Using Data Mining for Diagnosis Support in Ubiquitous Healthcare", Springer Verlag, LNAI 4496, pp. 795-804, 2007.
  10. J. B. MacQueen, "Some Methods for classification and Analysis of Multivariate Observations", Proc. of 5th Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 281-297, 1967.
  11. M. Berthold and D. J. Hand, "Intelligent Data Analysis, An Introduction", Springer, 1999.
  12. J. C. Bezdek, "Pattern Recognition with Fuzzy objective Function Algorithms", New York, Plenum Press, 1981.
  13. O. Othman, C. O'Ryan, and D. C. Schmidt, "The Design and Performance of an Adaptive CORBA Load Balancing Service", IEEE DS Online, Vol. 2, No. 4, 2001