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

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Mathematics of Planet Earth

Part of the book series: Mathematics of Planet Earth ((MPE,volume 5))

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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.

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Authors and Affiliations

  1. IBM Research, Dublin, Ireland

    Ernesto Arandia, Bradley J. Eck & Sean A. McKenna

  2. IBM Research, Singapore, Singapore

    Laura Wynter & Sebastien Blandin

Authors
  1. Ernesto Arandia
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  2. Bradley J. Eck
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  3. Sean A. McKenna
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  4. Laura Wynter
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  5. Sebastien Blandin
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Correspondence to Laura Wynter .

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  1. Mathematics and Statistics, Georgetown University, Washington, DC, USA

    Hans G. Kaper

  2. DIMACS Center, Rutgers University, Piscataway, NJ, USA

    Fred S. Roberts

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Arandia, E., Eck, B.J., McKenna, S.A., Wynter, L., Blandin, S. (2019). The Internet of Things and Machine Learning, Solutions for Urban Infrastructure Management. In: Kaper, H., Roberts, F. (eds) Mathematics of Planet Earth. Mathematics of Planet Earth, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-030-22044-0_13

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