Soft Computing

, Volume 20, Issue 5, pp 1671–1681 | Cite as

Data stream visualization framework for smart cities

  • F. J. Villanueva
  • C. Aguirre
  • A. Rubio
  • D. Villa
  • M. J. Santofimia
  • J. C. López
Focus

Abstract

Monitoring smart cities is a key challenge due the variety of data streams generated from different process (traffic, human dynamics, pollution, energy supply, water supply, etc.). All these streams show us what is happening and as to where and when in the city. The purpose of this paper was to apply different types of glyphs for showing real-time stream evolution of data gathered in the city. The use of glyphs is intended to make the most out of the human capacity for detecting visual patterns.

Keywords

Smart cities Data visualization Human behaviour understanding 

References

  1. Blog SC (2014) Video ‘must haves’ for active surveillance. http://security.americandynamics.net/blog/bid/69402/Video-must-haves-for-active-surveillance
  2. Cai Y (2007) Instinctive computing. In: Artifical intelligence for human computing, Lecture notes in computer Science, vol 4451. Springer, Berlin, pp 17–46Google Scholar
  3. Cavallari R, Martelli F, Rosini R, Buratti C, Verdone R (2014) A survey on wireless body area networks: technologies and design challenges. Commun Surv Tutor IEEE 16(3):1635–1657CrossRefGoogle Scholar
  4. Childs H, Geveci B, Schroeder W, Meredith J, Moreland K, Sewell C, Kuhlen T, Bethel E (2013) Research challenges for visualization software. Computer 46(5):34–42CrossRefGoogle Scholar
  5. Haklay MM, Weber P (2008) Openstreetmap: user-generated street maps. IEEE Pervasive Comput 7(4):12–18CrossRefGoogle Scholar
  6. Hao M, Marwah M, Mittelstadt S, Janetzko H, Keim D, Dayal U, Bash C, Felix C, Patel C, Hsu M, Chen Y (2012) Exploring cyber physical data streams using radial pixel visualizations. In: Visual analytics science and technology (VAST), 2012 IEEE Conference on, pp 225–226. doi:10.1109/VAST.2012.6400541
  7. Henning M (2004) A new approach to object-oriented middleware. IEEE Internet Comput 8(1):66–75Google Scholar
  8. Hsu C-W, Chang CC, Lin CJ (2010) A practical guide to support vector classification. National Taiwan UniversityGoogle Scholar
  9. Moya F, Villa D, Villanueva FJ, Barba J, Rincön F, Löpez JC (2009) Embedding standard distributed object-oriented middlewares in wireless sensor networks. Wirel Commun Mob Comput 9(3):335–345. doi:10.1002/wcm.545
  10. Park HK, Lee YW, Jang SI, Lee IP (2010) Online visualization of urban noise in ubiquitous-city middleware. In: Advanced communication technology (ICACT), 2010 The 12th international conference on, vol 1, pp 268–271Google Scholar
  11. Park JW, Yun CH, Jung HS, Lee YW (2011) Visualization of urban air pollution with cloud computing. In: Services (SERVICES), 2011 IEEE world congress on, pp 578–583Google Scholar
  12. Rhyne T, Chen M (2013) Cutting-edge research in visualization. Computer 46(5):22–24CrossRefGoogle Scholar
  13. Sapankevych N, Sankar R (2009) Time series prediction using support vector machines: a survey. Comput Intell Mag IEEE 4(2):24–38CrossRefGoogle Scholar
  14. Tory M, Möller T (2004) Human factors in visualization research. IEEE Trans Vis Comput Graph 10:72–84CrossRefGoogle Scholar
  15. Villanueva F, Santofimia M, Villa D, Barba J, Lopez J (2013) Civitas: the smart city middleware, from sensors to big data. In: Innovative mobile and internet services in ubiquitous computing (IMIS), 2013 Seventh international conference on, pp 445–450Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • F. J. Villanueva
    • 1
  • C. Aguirre
    • 1
  • A. Rubio
    • 1
  • D. Villa
    • 1
  • M. J. Santofimia
    • 1
  • J. C. López
    • 1
  1. 1.University of Castilla-La ManchaCiudad RealSpain

Personalised recommendations