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A shortest path planning algorithm for cloud computing environment based on multi-access point topology analysis for complex indoor spaces

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Abstract

Due to the increasing complexity of internal spaces and dynamic change in certain specific partitions in large indoor areas, indoor navigation has become more important as it is useful to help people find their destination or evacuate from dangerous areas. A shortest path planning method is the main technique used in an indoor navigation system. Hence, we proposed a shortest path planning algorithm based on multi-access point topological analysis for a dynamically changing indoor navigation path. To support the dynamically changing characteristics, we pre-construct an indoor route when the route is requested. Further, we dynamically update its internal path information when the route changes. The proposed method is suitable for both simple and complex large-scale indoor spaces, even when the related indoor maps are difficult to be used for navigation. We conduct a performance evaluation to compare the proposed method with the current research approaches. The results show that our method provides improved performance for indoor navigation.

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References

  1. Wang X, Pang X, Luo Y (2010) LBS-p: a LBS platform supportingonline map services. In: Vehicular Technology Conference Fall (VTC 2010-Fall), pp 1–5. doi:10.1109/VETECF.2010.5594114

  2. Yu Z, Liang Y, Xu B, Yang Y, Guo B (2011) Towards a smart campus with mobile social networking. In: Proceedings of 2011 4th international conference on cyber-physical and social computing, pp 162-169. doi:10.1109/iThings/CPSCom.2011.55

  3. Albayram Y, Khan MMH, Bamis A, Kentros S, Nguyen N, Jiang R (2015) Designing challenge questions for location based authentication systems: a real life study. Hum Cent Comput Inf Sci 5:17. doi:10.1186/s13673-015-0032-3

    Article  Google Scholar 

  4. Lu E, Lee W, Tseng V (2012) A framework for personal mobile commerce pattern mining and prediction. Knowledge and data engineering. IEEE Trans Knowl Data Eng 24(5):769–782. doi:10.1109/TKDE.2011.65

    Article  Google Scholar 

  5. Nguyen XT, Nguyen BT, Do KP, Bui QH, Nguyen TNT, Vuong VQ, Le TH (2015) Spatial interpolation of meteorologic variables in Vietnam using the Kriging method. JIPS 11(1):134–147. doi:10.1016/j.agrformet.2007.04.004

    Google Scholar 

  6. Raubal M, Egenhofer MJ (1998) Comparing the complexity of wayfinding tasks in built environments. Environ Plan B Plan Des 25(6):895–913. doi:10.1068/b250895

    Article  Google Scholar 

  7. Guha RK, Chen W (2009) A distributed traffic navigation system using vehicular communication. In: Proceeding of 1st IEEE Vehicular Network Conference, pp 1–8. doi:10.1109/VNC.2009.5416381

  8. Fizza A, Heekuck O (2014) A step towards user privacy while using location-based services. JIPS 10(4):618–627. doi:10.3745/JIPS.01.0003

    Google Scholar 

  9. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1(1):267–271. doi:10.1007/BF01386390

    Article  MathSciNet  MATH  Google Scholar 

  10. Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. Trans Syst Sci Cybern SSC 4(2):100–107. doi:10.1109/TSSC.1968.300136

    Article  Google Scholar 

  11. Chen Y, Ke-qin W, Wang D, Chen G (2006) Elastic algorithm: a new path planning algorithm about auto-navigation in 3D virtual scene. ICAT 2006:1156–1165. doi:10.1007/11941354_120

    Google Scholar 

  12. Yuan W, Schneider M (2010) iNav: an indoor navigation model supporting length-dependent optimal routing. In: 13th AGILE international conference on geographic information science. doi:10.1007/978-3-642-12326-9_16

  13. Goetz M, Zipf A (2011) Formal definition of a user-adaptive and length-optimal routing graph for complex indoor environments. Geo-spat Inf Sci 14(2):119–128. doi:10.1007/s11806-011-0474-3

    Article  Google Scholar 

  14. Afyouni et al (2012) Spatial models for context-aware indoor navigation systems: a survey. J Spat Inf Sci 4:85–123. doi:10.5311/JOSIS.2012.4.73

    Google Scholar 

  15. Shen J, Wu M, Lv G, Wen Y, Wang X, Zhang H (2010) Topological relationships calculation for 3D curves data set based on monotone chains. In: Geoinformatics, 2010 18th international conference on, pp 1–5. IEEE. doi:10.1109/GEOINFORMATICS.2010.5567856

  16. Park I, Jang GU, Park S, Lee J (2009) Time-dependent optimal routing in micro-scale emergency situation. In: 10th international conference on mobile data management: systems, services and middleware, pp 714–719. doi:10.1109/MDM.2009.122

  17. Yuan W, Schneider M (2010) Supporting 3d route planning in indoor space based on the lego representation. In: 2nd ACM SIGSPATIAL international workshop on indoor spatial awareness (ISA), Springer, Heidelberg, pp 16–23. doi:10.1145/1865885.1865890

  18. Stoffel E-P, Lorenz B, Ohlbach H (2007) Towards a semantic spatial model for pedestrian indoor navigation. Adv Concept Model Found Appl 4802:328–337. doi:10.1007/978-3-540-76292-8_39

    Article  Google Scholar 

  19. Xing TY, Zhang S, Tao LF (2014) Cloud-based spatial information service architecture within LBS. Positioning 5(3):59–65. doi:10.4236/pos.2014.53008

    Article  Google Scholar 

  20. Xu T, Zhou Y (2012) Topology analysis of rural power distribution network based on spatial database. In: Power and energy engineering conference (APPEEC), 2012 Asia-Pacific, IEEE. doi:10.1109/APPEEC.2012.6307450

  21. Gonzalez H, Han J, Li X, Myslinska M, Sondag JP (2007) Adaptive fastest path computation on a road network: a traffic mining approach. VLDB 2007:794–805

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Acknowledgments

This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2012M3C4A7032781).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Inha University, Incheon, South Korea

    Yan Li & Byeong-Seok Shin

  2. Department of Computer Science and Engineering, Seoul National University of Science and Technology (SeoulTech), Seoul, South Korea

    Jong-Hyuk Park

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  1. Yan Li
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  2. Jong-Hyuk Park
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Correspondence to Byeong-Seok Shin.

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Li, Y., Park, JH. & Shin, BS. A shortest path planning algorithm for cloud computing environment based on multi-access point topology analysis for complex indoor spaces. J Supercomput 73, 2867–2880 (2017). https://doi.org/10.1007/s11227-016-1650-x

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