DOI QR코드

DOI QR Code

센서 정보의 안정적인 이용을 위한 경로 예측 기반 센서 레지스트리 시스템

A Path Prediction-Based Sensor Registry System for Stable Use of Sensor Information

  • 정동원 (군산대학교 통계컴퓨터과학과) ;
  • 두미경 (군산대학교 통계컴퓨터과학과)
  • 투고 : 2014.09.15
  • 심사 : 2014.11.11
  • 발행 : 2015.02.15

초록

센서 레지스트리 시스템은 이기종 센서 네트워크 환경에서 센서 데이터의 즉시적 활용 및 끊김 없는 해석을 위해 개발되었다. 그러나 기존 센서 레지스트리 시스템은 불안정한 네트워크 상황에서 센서 데이터 해석을 위한 정보를 제공하지 못하며, 이로 인해 센서 데이터의 손실, 처리 결과의 부정확성, 서비스 품질 저하 등의 문제를 야기한다. 이 논문에서는 소프트웨어 관점에서 이러한 문제점을 해결할 수 있는 방안을 제시한다. 사용자의 이동 경로를 예측하여 사전에 센서 정보를 이동 단말기에 제공함으로써 불완전한 네트워크 접속 시점에 안정적으로 센서 정보를 활용할 수 있는 확장된 센서 레지스트리 시스템을 제안하고 실험 및 평가 결과를 보인다. 이 논문에서 제안한 확장된 센서 레지스트리 시스템은 센서 정보의 안정적 활용성 증가와 더불어 센서 기반 서비스 품질을 향상시킨다.

The sensor registry system has been developed for instant use and seamless interpretation of sensor data in a heterogeneous sensor network environment. However, the existing sensor registry system cannot provide information for interpretation of the sensor data in situations in which the network is unstable. This limitation causes several problems such as sensor data loss, inaccuracy of processed results, and low service quality. A method to resolve such problems in the aspect of software is presented herein. In other words, an extended sensor registry system is proposed to enable the stable use of sensor information, even under conditions of unstable network connection, by providing sensor information with a mobile device in advance through the user path prediction. The results of experiments and evaluation are also presented. The extended sensor registry system proposed in this paper enhances the stable usability of sensor information as well as improves the quality of sensor-based services.

키워드

참고문헌

  1. L. Atzori, A. Iera, and G. Morabito, "The Internet of Things: A survey," Computer Networks, Vol. 54, pp. 2787-2805, 2010. https://doi.org/10.1016/j.comnet.2010.05.010
  2. M. Compton, C. Henson, L. Lefort, H. Neuhaus, and A. Sheth, "A survey of the semantic specification of sensors," Proc. of 2nd International Semantic Sensor Networks Workshop, International Workshop on Semantic Sensor Networks 2009, 2009.
  3. A. Sheth, C. Henson, and S.S. Sahoo, "Semantic Sensor Web," IEEE Internet Computing, Vol. 12, No. 4, pp. 78-83, 2008. https://doi.org/10.1109/MIC.2008.87
  4. Y. Shi, G. Li, X. Zhou, and X. Zhang, "Sensor Ontology Building in Semantic Sensor Web," Proc. of IoT Workshop 2012, Vol. 312, pp. 277-284, 2012.
  5. C. Reed, M. Botts, G. Percivall, and J. Davidson, "OGC Sensor Web Enablement: Overview And High Level Architecture," Open Geospatial Consortium, 2013.
  6. M. Compton et al., "The SSN ontology of the W3C semantic sensor network incubator group," Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 17, pp. 25-32, 2012. https://doi.org/10.1016/j.websem.2012.05.003
  7. D. Jeong and J. Ji, "A Registration and Management System for Consistently Interpreting Semantics of Sensor Information in Heterogeneous Sensor Network Environments," Journal of KIISE : Databases, Vol. 38, No. 5, pp. 289-302, 2011. (in Korean)
  8. ISO/IEC JTC 1/SC 32, ISO/IEC 11179-3: 2003-Information Technology-Metadata Registries (MDR)-Part 3: Registry Metamodel and Basic Attributes, 2003.
  9. S. Han, H. J. Kang, and S. Cho, "Learning User's Moving Patterns for Location-based Services with Intelligent Agent," Proc. of the 31th KIISE Spring Conference, pp. 562-564, 2004. (in Korean)
  10. T. B. Yoon, K. H. Park, and J. H. Lee, "A Spatiotemporal Location Prediction Method of Moving Objects Based on Path Data," Journal of Korean Institute of Intelligent Systems, Vol. 16, No. 5, pp. 568-574, 2006. (in Korean) https://doi.org/10.5391/JKIIS.2006.16.5.568
  11. M. Heo, M. Kang, B. Lim, K. Hwang, Y. Park, and B. Zhang, "Real-time Route Inference and Learning for Smartphone Users using Probabilistic Graphical Models," Journal of KIISE : Software and Applications, Vol. 39, No. 6, pp. 425-435, 2012. (in Korean)
  12. T. Yoon, D. lee, J. Jung, and J. Lee, "Path Selection and Summarization of User's Moving Path for Spatio-Temporal Location Prediction," Proc. of the HCI Korea 2008, pp. 298-303, 2008. (in Korean)
  13. T. Yoon and J. Lee, "Representative Path Selection for Goal & Path Prediction," IEICE Transactions on Communications, Vol. E91-B, No. 11, pp. 3516-3523, 2008. https://doi.org/10.1093/ietcom/e91-b.11.3516
  14. S. Lee, B. Kim, J. Kim, T. Yoon, and J. Lee, "A Path Prediction Method using Previous Moving Path and Context Data," Proc. of the Korean Institute of Intelligent Systems Spring Conference 2009, pp. 3-4, 2009. (in Korean)
  15. Y. Kim and S. Cho, "Personalized Destination Prediction by Integrating Place Movement Pattern and Moving Path Model," Proc. of the 39th KIISE Fall Conference, pp. 136-138, 2012. (in Korean)
  16. J.-M. Kim, S.-K. Yang, H.-J. Baek, M.-J. Jeon, and Y.-T. Park, "GPS Noise Reduction and Trajectories Simplification for Personal Routes Learning in Close Range," Journal of KIISE : Computer Systems and Theory, Vol. 39, No. 4, pp. 260-269, 2012. (in Korean)
  17. D. Jeong, "Fast and Close-Range Prediction Algorithm of User Paths," Journal of KIISE: Computing Practices and Letters, Vol. 20, No. 3, pp. 153-158, 2014. (in Korean)
  18. D. Jeong, S. Lee, and D.-K. Baik, "Selecting Projection Candidates for Improving the Performance of the Path Prediction Algorithm," Proc. of the Korea Computer Congress 2014, pp. 350-352, 2014. (in Korean)

피인용 문헌

  1. Extending Sensor Registry System Using Network Coverage Information vol.4, pp.9, 2015, https://doi.org/10.3745/KTSDE.2015.4.9.425