• DocumentCode
    1667224
  • Title

    Building a Big Data Platform for Smart Cities: Experience and Lessons from Santander

  • Author

    Bin Cheng ; Longo, Salvatore ; Cirillo, Flavio ; Bauer, Martin ; Kovacs, Ernoe

  • Author_Institution
    NEC Labs. Eur., Heidelberg, Germany
  • fYear
    2015
  • Firstpage
    592
  • Lastpage
    599
  • Abstract
    The Internet of Things (IoT) is now shaping our cities to make them more connected, convenient, and intelligent. However, this change will highly rely on extracted values and insights from the big data generated by our cities via sensors, devices, and human activities. Many existing studies and projects have been done to make our cities smart, focusing more on how to deploy various sensors and devices and then collect data from them. However, this is just the first step towards smart cities and next step will be to make good use of the collected data and enable context-awareness and intelligence into all kinds of applications and services via a flexible big data platform. In this paper, we introduce the system architecture and the major design issues of a live City Data and Analytics Platform, namely CiDAP. More importantly, we share our experience and lessons learned from building this practical system for a large scale running smart city test bed, SmartSantander. Our work provides a valuable example to future Smart City platform designers so that they can foresee some practice issues and refer to our solution when building their own smart city data platforms.
  • Keywords
    Big Data; data analysis; smart cities; software architecture; ubiquitous computing; Big Data platform; CiDAP; Internet of Things; IoT; SmartSantander; context-awareness; human activities; live city data and analytics platform; smart city data platforms; system architecture; Big data; Cities and towns; Databases; Real-time systems; Sensors; Servers; Big Data; Internet of Things; Smart Cities; platform;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (BigData Congress), 2015 IEEE International Congress on
  • Conference_Location
    New York, NY
  • Print_ISBN
    978-1-4673-7277-0
  • Type

    conf

  • DOI
    10.1109/BigDataCongress.2015.91
  • Filename
    7207275