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The Internet of Things and Machine Learning, Solutions for Urban Infrastructure Management

  • Ernesto Arandia
  • Bradley J. Eck
  • Sean A. McKenna
  • Laura WynterEmail author
  • Sebastien Blandin
Chapter
Part of the Mathematics of Planet Earth book series (MPE, volume 5)

Abstract

Urban infrastructure management requires the ability to reason about a large-scale complex system: What is the state of the system? How can it be compactly represented and quantified? How is the system likely to evolve? Reasoning calls for predictive modeling, feedback, optimization, and control. With an understanding of the system state and its likely evolution, how should resources be allocated or policies changed to produce a better outcome? By leveraging data from the Internet of Things, it becomes feasible to perform online estimation, optimization, and control of such systems to help our cities function better. This involves taking traditional applications of mathematical sciences into a large-scale, online, and adaptive setting. We focus in this chapter on two particular applications that are important to effectively manage a city: transportation and municipal water services.

Keywords

Algorithms Control Data Estimation Internet of Things Machine learning Prediction Smart city Urban computing 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ernesto Arandia
    • 1
  • Bradley J. Eck
    • 1
  • Sean A. McKenna
    • 1
  • Laura Wynter
    • 2
    Email author
  • Sebastien Blandin
    • 2
  1. 1.IBM ResearchDublinIreland
  2. 2.IBM ResearchSingaporeSingapore

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