가중치가 부여된 공간변수에 의거하여 USN 루트노드 최소화 방안 -대학 캠퍼스를 사례로-

Minimizing Redundant Route Nodes in USN by Integrating Spatially Weighted Parameters: Case Study for University Campus

  • Kim, Jin-Taek (Fire Department, Daegu Metropolitan City Office) ;
  • Um, Jung-Sup (Department of Geography, Kyungpook National University)
  • 투고 : 2010.09.24
  • 심사 : 2010.11.15
  • 발행 : 2010.12.31

초록

현재 유비쿼터스 센서 네트웍(USN: Ubiquitous Sensor Networks)의 노드를 배치하는 방식은 위치 적정성의 관점에서 많은 한계를 가지고 있다. 본 연구는 가시권 분석, 셀중심에 대한 인접성, 도로 밀도, 건물밀도, 셀중첩 비율을 GIS 데이터베이스로 구축하고 공간변수별 가중치에 의거하여 USN루트 노드 설치를 최소화하는 방안을 제시하였다. 기존의 전형적인 격자형 방식에 의거한 USN에서 24개의 루트노드가 필요하였지만 공간가중치에 의한 분석방법은 11개의 노드만으로 네트웍의 구성이 가능하였다. 11개의 노드만으로 구성된 USN에서 신호강도(RSSI: Received Signal Strength Indicator)는 다양한 지점에서 급격한 변동을 보이지 않고 노드의 연결성에 대한 성능평가 기준을 충족하였다. 공간가중치를 반영한 노드의 배치는 USN노드 배치에서 격자형방식이나 무작위로 설치하는 관행을 개선될 수 있는 계기가 되어 USN의 운영과정에서 신호강도를 확보할 수 있는 중요한 참고자료가 될 수 있을 것으로 사료된다.

The present USN (Ubiquitous Sensor Networks) node deployment practices have many limitations in terms of positional connectivity. The aim of this research was to minimize a redundancy of USN route nodes, by integrating spatially weighted parameters such as visibility, proximity to cell center, road density, building density and cell overlapping ratio into a comprehensive GIS database. This spatially weighted approach made it possible to reduce the number of route nodes (11) required in the study site as compared to that of the grid network method (24). The field test for RSSI (Received Signal Strength Indicator) indicates that the spatially weighted deployment could comply with the quality assurance standard for node connectivity, and that reduced route nodes do not show a significant degree of signal fluctuation for different site conditions. This study demonstrated that the spatially weighted deployment can be used to minimize a redundancy of USN route nodes in a routine manner, and the quantitative evidence removing a redundancy of USN route nodes could be utilized as major tools to ensure the strong signal in the USN, that is frequently encountered in real applications.

키워드

과제정보

연구 과제 주관 기관 : Korea Institute of Energy Technology Evaluation and Planning(KETEP)

참고문헌

  1. Ando, S., 2003, Sensing technologies in ubiquitous network environment: From stand-alone intelligent sensing to knowledge-shared network sensing, IEEE Transactions on Sensors and Micromachines, 123(8), 263-270. https://doi.org/10.1541/ieejsmas.123.263
  2. Akl, R. and Sawant, U., 2007, Grid-based coordinated routing in wireless sensor networks, Consumer Communications and Networking Conference, 860-864.
  3. Akyildiz, l. F., Wang, X., and Wang, W., 2005, Wireless mesh networks: A survey, Computer Networks, 47, 445-487. https://doi.org/10.1016/j.comnet.2004.12.001
  4. Alshuwaikhat, H. M. and Abubakar, I., 2008, An integrated approach to achieving campus sustainability: Assessment of the current campus environmental management practices, Journal of Cleaner Production, 16(16), 1777-1785. https://doi.org/10.1016/j.jclepro.2007.12.002
  5. Banai, R., 1993, Fuzziness in Geographic Information Systems: Contributions from the Analytic Hierarchy Process, International Journal of Geographic Information Systems, 7, 315-329. https://doi.org/10.1080/02693799308901964
  6. Breunig, M. and Baer, W., 2004, Database support for mobile route planning systems, Computers, Environment and Urban Systems, 28(6), 595-610. https://doi.org/10.1016/j.compenvurbsys.2003.12.005
  7. Camp, L. J. and Tsang, R. P., 2000, Universal service in a ubiquitous digital network, Ethics and Information Technology, 2(4), 211-221.
  8. Casademont, J., Lopez-Aguilera, E., Paradells, J., Rojas, A., Calveras, A., Barcelo, F., and Cotrina, J., 2004, Wireless technology applied to GIS, Computers & Geosciences, 30, 671-682. https://doi.org/10.1016/j.cageo.2004.02.004
  9. Chipcon Inc 2004, CC2420 IEEE 802.15.4/ 2.4GHz RF transceiver datasheet. CC2420 Product Description.
  10. Dodd, M., 2001, The Validity of Using a Geographic Information System's Viewshed Function as a Predictor for the Reception of Line-of-Sight Radio Waves, Master Thesis, Virginia Polytechnic Institute and State University.
  11. ESRI (Environmental System Research Institute), 2006, ArcGIS software help menu (spatial analysis toolbox).
  12. Fritsch, D. D. K. and Volz, S., 2001, NEXUS - positioning and data management concepts for location-aware applications, Computers, Environment and Urban Systems, 25(3), 279-291. https://doi.org/10.1016/S0198-9715(00)00026-0
  13. IEEE, 2003, Wireless Medium Access Control (MAC) and Physical Layer (PHY) specifications for low-rate wireless personal area networks (LR-WPANs), IEEE Std 802, 15.
  14. Ikegami, F., 1993, Theoretical prediction of propagation for future mobile communications-reviewing and looking forward, IEEE Transactions on Communications, E76-B(2), 51-57.
  15. ITU-R Assembly, 1999, Propagation effects relating to terrestrial land mobile service in the VHF and UHF bands(Question ITU-R 203/3)), ITU-R P.1406, Geneva, 1-10.
  16. Jiang, B. and Yao, X., 2006, Location-based services and GIS in perspective, Computers, Environment and Urban Systems, 30(6),712-725. https://doi.org/10.1016/j.compenvurbsys.2006.02.003
  17. Juan, L. L., Ramos, L. and Cardona, N., 1999, Application of some theoretical models for coverage prediction in macrocell urban environments, IEEE Transactions on Vehicular Technology, 48, 1463-1468. https://doi.org/10.1109/25.790521
  18. Kim, K. Y., 2008, Location optimization in heterogeneous sensor network configuration for security monitoring, Journal of the Korean Geographical Society, 43(2), 220-234.
  19. Krzanowski, R. and Raper, J., 1999, Hybrid genetic algorithm for transmitter location in wireless networks, Computers, Environment and Urban Systems, 23, 359-382. https://doi.org/10.1016/S0198-9715(99)00030-7
  20. Laia, V. S., Trueblood R. P., and Wong, B. K., 1999, Software selection: A case study of the application of the analytical hierarchical process to the selection of a multimedia authoring system, Information & Management, 36, 221-232. https://doi.org/10.1016/S0378-7206(99)00021-X
  21. Letchner, J., Fox, D., and Lamarca, A., 2005, Large-scale localization from wireless signal strength. Proceedings of the National Conference on Artificial Intelligence.
  22. Li, C., 2006, User preferences, information transactions and location-based services: A study of urban pedestrian wayfinding, Computers, Environment and Urban Systems, 30(6). 726-740. https://doi.org/10.1016/j.compenvurbsys.2006.02.008
  23. Liu, X. and Karimi, H. A., 2006, Location awareness through trajectory prediction, Computers, Environment and Urban Systems, 30(6), 741-756. https://doi.org/10.1016/j.compenvurbsys.2006.02.007
  24. Megerian, S., Koushanfar, F., Potkonjak, M., and Srivastava, M., 2005, Worst and best-case coverage in sensor networks, IEEE Transactions on Mobile Computing, 4, 84-92. https://doi.org/10.1109/TMC.2005.15
  25. Ogata, S., 2004, Sensors for realizing ubiquitous sensor network, Systems Control and Information, 48(11), 452-457.
  26. Park, H., Lim, S., Yie, I., Kim, H., Chun, K., and Lee, J., 2007, An information aggregation scheme of multi-node in ubiquitous sensor networks, Lecture Notes in Computer Science, 4743, 60-68.
  27. Park, J. T., 2005, Management of Ubiquitous Sensor Network, APNOMS Tutorial, Okinawa, Japan, 73p.
  28. Rose, S., 2001, The Effect of Digital Elevation Model Resolution on Wave Propagation Predictions at 2.4GHz, Master Thesis, Virginia Polytechnic Institute and State University.
  29. Rui, Z., Hang, Z., and Miguel, A. L., 2006, A grid-based sink location service for large-scale wireless sensor networks, Proceedings of the 2006 International Conference on Wireless Communications and Mobile Computing, Vancouver, British Columbia, Canada.
  30. Saaty, T. L., 1980, The Analytic Hierarchy Process, McGraw Hill, NY.
  31. Sandrasegaran, K. and Prag, K., 1999, Planning point-to-multipoint radio access networks using expert systems, Expert Systems with Applications, 17, 145-166 https://doi.org/10.1016/S0957-4174(99)00031-7
  32. Shim, J, P., 1989, Bibliographical research on the analytic hierarchy process(AHP), Socio-Economic Planning Sciences, 23, 161-7. https://doi.org/10.1016/0038-0121(89)90013-X
  33. Steve, H. L., Arie, C., and Vincent, T., 2005, Distributed geospatial infrastructure for sensor web, Computers and Geosciences, 31(2), 221-231. https://doi.org/10.1016/j.cageo.2004.06.014
  34. Sui, D. Z., 1992, A fuzzy GIS modeling approach for urban land evaluation, Computers, Environment and Urban Systems, 16, 101-115. https://doi.org/10.1016/0198-9715(92)90022-J
  35. Weerakoon, K. G. P. K., 2002, Integration of GIS Based suitability analysis and multi-criteria evaluation for urban land use planning; Contribution from the Analytic Hierarchy Process. Proceedings of the 2002 Asian Conference on Remote Sensing, Kathmandu, Nepal.
  36. Yu, Y., Govidan, R., and Estrin, D., 2001, Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks, UCLA Computer Science Department Technical Report.