From Earthquake Detection to Traffic Surveillance – About Information and Communication Infrastructures for Smart Cities

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7744)


Smart cities use networks of sensors, actuators, and centralized computing clusters to observe physical reality, derive information, and thereby influence citizens and authorities. Smart city applications therefore require three components to work: wireless sensor networks, geo-information systems, and frameworks for distributed analysis of sensor and geo-data. In this paper, we provide an overview on a set of concrete technologies for such information and communication infrastructures for smart cities. These technologies include a combination of WiFi- and PAN-based sensor networks, City GML data, a model-driven approach to collect and manage data, as well as distributed data analysis based on domain specific languages. We show how we use these technologies to research two typical smart city applications: earthquake early warning and traffic surveillance.


Sensor Node Wireless Sensor Network Multiple Input Multiple Output Smart City Mesh Router 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Vojdani, A.: Smart Integration. Power and Energy Magazine 6(6), 71–79 (2008)CrossRefGoogle Scholar
  2. 2.
    Samadi, P., Mohsenian-Rad, A., Schober, R., Wong, V.W.S., Jatskevich, J.: Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid. In: First IEEE International Conference on Smart Grid Communications, pp.415-420. IEEE Press (2010)
  3. 3.
    Fischer, J., Redlich, J.P., Zschau, J., Milkereit, C., Picozzi, M., Fleming, K., Brumbulli, M., Lichtblau, B., Eveslage, I.: A wireless mesh sensing network for early warning. Journal of Network and Computer Applications 35(2), 538–547 (2012)CrossRefGoogle Scholar
  4. 4.
    Hernández-Muñoz, J.M., Vercher, J.B., Muñoz, L., Galache, J.A., Presser, M., Gómez, L.A.H., Pettersson, J.: Smart Cities at the Forefront of the Future Internet. In: Domingue, J., Galis, A., Gavras, A., Zahariadis, T., Lambert, D., Cleary, F., Daras, P., Krco, S., Müller, H., Li, M.-S., Schaffers, H., Lotz, V., Alvarez, F., Stiller, B., Karnouskos, S., Avessta, S., Nilsson, M. (eds.) Future Internet Assembly. LNCS, vol. 6656, pp. 447–462. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Murty, R., Mainland, G., Rose, I., Chowdhury, A.R., Gosain, A., Bers, J., Welsh, M.: Citysense–An urban-scale wireless sensor network and testbed. In: 2008 IEEE Conference on Technologies for Homeland Security. IEEE Press (2008)
  6. 6.
    Chatzigiannakis, I., Fischer, S., Koninis, C., Mylonas, G., Pfisterer, D.: WISEBED: An Open Large-Scale Wireless Sensor Network Testbed. In: Komninos, N. (ed.) SENSAPPEAL 2009. LNICST, vol. 29, pp. 68–87. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Estrin, D., Girod, L., Pottie, G., Srivastava, M.: Instrumenting the world with wireless sensor networks. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2001), vol. 4, pp. 2033–2036. IEEE Press (2001)Google Scholar
  8. 8.
    Lynch, J.P.: A summary review of wireless sensors and sensor networks for structural health monitoring. The Shock and Vibration Digest 38(2), 91–128 (2006)CrossRefGoogle Scholar
  9. 9.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks – a survey. Computer Networks 38(4), 393–422 (2002)CrossRefGoogle Scholar
  10. 10.
    Akyildiz, I.F., Melodia, T., Chowdhury, K.R.: A survey on wireless multimedia sensor networks. Computer Networks 51(4), 921–960 (2007)CrossRefGoogle Scholar
  11. 11.
    Günes, M., Juraschek, F., Blywis, B., Mushtaq, Q., Schiller, J.: A testbed for next generation wireless network research. Praxis der Informationsverarbeitung und Kommunikation - Special Issue on Mobile Ad-hoc Networks 34(5) (2009)Google Scholar
  12. 12.
    Scheidgen, M., Zubow, A., Sombrutzki, R.: HWL – A High Performance Wireless Research Network. In: Ninth International Conference on Networked Sensing Systems (INSS). IEEE Press (2012)Google Scholar
  13. 13.
    Kohler, E., Morris, R., Chen, B., Jannotti, J., Kaashoek, M.F.: The click modular router. ACM Transactions on Computer Systems 18(3), 263–297 (2000)CrossRefGoogle Scholar
  14. 14.
    Zubow, A., Sombrutzki, R., Scheidgen, M.: A low-cost mimo mesh testbed based on 802.11n. In: IEEE Wireless Communications and Networking Conference. IEEE Press (2012)Google Scholar
  15. 15.
    Scheidgen, M., Zubow, A., Sombrutzki, R.: ClickWatch – An Experimentation Framework for Communication Network Test-beds. In: Proceedings of the IEEE Wireless Communications and Networking Conference, Paris, France, April 1-4, pp. 3296–3301. IEEE (2012)Google Scholar
  16. 16.
    Baar, M., Will, H., Blywis, B., Liers, A., Wittenburg, G., Schiller, J.: The ScatterWeb MSB-A2 Platform for Wireless Sensor Networks. Technical report, Freie Universität Berlin (2008)Google Scholar
  17. 17.
    Günes, M., Blywis, B., Juraschek, F.: Concept and design of the hybrid distributed embedded systems testbed. Technical Report TR-B-08-10, Freie Universität Berlin (2008),
  18. 18.
    Günes, M., Blywis, B., Juraschek, F., Schmidt, P.: Practical issues of implementing a hybrid multi-nic wireless mesh-network. Technical Report TR-B-08-11, Freie Universität Berlin (2008),
  19. 19.
    Blywis, B., Günes, M., Juraschek, F., Schmidt, P., Kumar, P.: DES-SERT – A framework for structured routing protocol implementation. In: Proceedings of the 2nd IFIP Conference on Wireless Days (WD 2009). IEEE Press (2009)Google Scholar
  20. 20.
    Portele, C.: OGC Geography Markup Language (GML) 3.3. Technical report, Open Geospatial Consortium (OGC) (2012)Google Scholar
  21. 21.
    Gröger, G., Kolbe, T.H., Czerwinski, A., Nagel, C.: OpenGIS City Geography Markup Language (CityGML) 2.0. Open Geospatial Consortium,
  22. 22.
    Stadler, A.: Making interoperability persistent: A 3D geo database based on CityGML. In: Proceedings of the 3rd International Workshop on 3D Geo-Information, pp. 175–192. Springer (2008)Google Scholar
  23. 23.
    Kolovos, D.S., Rose, L.M., Williams, J., Matragkas, N., Paige, R.F.: A Lightweight Approach for Managing XML Documents with MDE Languages. In: Vallecillo, A., Tolvanen, J.-P., Kindler, E., Störrle, H., Kolovos, D. (eds.) ECMFA 2012. LNCS, vol. 7349, pp. 118–132. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  24. 24.
    Scheidgen, M., Zubow, A., Fischer, J., Kolbe, T.H.: Automated and Transparent Model Fragmentation for Persisting Large Models. In: France, R.B., Kazmeier, J., Breu, R., Atkinson, C. (eds.) MODELS 2012. LNCS, vol. 7590, pp. 102–118. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  25. 25.
    Scheidgen, M., Zubow, A., Sombrutzki, R.: Clickwatch – an experimentation framework for communication network test-beds. In: IEEE Wireless Communications and Networking Conference. IEEE Press (2012)Google Scholar
  26. 26.
    Steinberg, D., Budinsky, F., Paternostro, M., Merks, E.: EMF – Eclipse Modeling Framework 2.0, 2nd edn. Addison-Wesley Professional (2009)Google Scholar
  27. 27.
    Scheidgen, M.: EMFFrag – Meta-Model-based Model Fragmentation and Persistence Framework,
  28. 28.
    Stepper, E.: Connected Data Objects (CDO),
  29. 29.
    Fleming, K., Picozzi, M., Milkereit, C., Kühnlenz, F., Lichtblau, B., Fischer, J., Zulfikar, C., Ozel, O., et al.: The Self-organizing Seismic Early Warning Information Network (SOSEWIN). Seismological Research Letters 80(5), 755–771 (2009)CrossRefGoogle Scholar
  30. 30.
    Wald, D.J., Worden, B.C., Quitoriano, V., Pankow, K.L.: ShakeMap Manual - Technical Manual, Users Guide and Software Guide. U.S. Geological Survey (2006)Google Scholar
  31. 31.
    Wu, Y.M., Teng, T.I.: A Virtual Subnetwork Approach to Earthquake Early Warning. Bulletin of the Seismological Society of America 92(5), 2008–2018 (2002)CrossRefGoogle Scholar
  32. 32.
    Horiuchi, S., Negishi, H., Abe, K., Kamimura, A., Fujinawa, Y.: An Automatic Processing System for Broadcasting Earthquake Alarms. Bulletin of the Seismological Society of America 95(2), 708–718 (2005)CrossRefGoogle Scholar
  33. 33.
    Erdik, M., Fahjan, Y., Ozel, O., Alcik, H., Mert, A., Gul, M.: Istanbul Earthquake Rapid Response and the Early Warning System. Bulletin of Earthquake Engineering 1, 157–163 (2003)CrossRefGoogle Scholar
  34. 34.
    Ionescu, C., Böse, M., Wenzel, F., Marmureanu, A., Grigore, A., Marmureanu, G.: An Early Warning System for Deep Vrancea (Romania) Earthquakes. In: Earthquake Early Warning Systems, pp. 343–349. Springer (2007)Google Scholar
  35. 35.
    Schröder, S., Zilske, M., Liedtke, G., Nagel, K.: A computational framework for a multi-agent simulation of freight transport activities. In: Annual Meeting Preprint 12-4152, Transportation Research Board (2012),

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Humboldt Universität zu BerlinGermany
  2. 2.Freie Universität BerlinGermany
  3. 3.Technische Universität BerlinGermany
  4. 4.Deutsches Zentrum für Luft und RaumfahrtGermany

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