Internet of Things (IoT) has become an enabling technology in a huge number of diverse domains, including smart manufacturing, health, and cities. The latter encompasses cyber-physical infrastructures able to improve the citizens’ quality of life in a broader sense, including so-called smart grids (for water, natural gas, energy delivery). This chapter highlights the challenges and possible solutions related to the IoT-oriented design and deployment of smart water and gas grids, and unveils the potentially disruptive impact data analytics and machine learning could have in their management, and in consumption forecasting. The chapter reviews both capillary networks, and future opportunities provided by cellular IoT, as communication infrastructures. Open issues in network planning for smart metering applications, from an electromagnetic perspective, are also discussed, and supported by experimental evaluations. A thorough review of the state-of-the-art literature in the field of machine learning for leakage detection and consumption forecasting concludes the chapter.

IoT-Enabled Smart Gas and Water Grids: from Communication Protocols to Data Analysis

Susanna Spinsante
Project Administration
;
Stefano Squartini
Writing – Review & Editing
;
Paola Russo
Writing – Original Draft Preparation
;
Adelmo De Santis
Membro del Collaboration Group
;
Marco Severini
Writing – Original Draft Preparation
;
Marco Fagiani
Writing – Original Draft Preparation
;
Valentina Di Mattia
Membro del Collaboration Group
;
2018-01-01

Abstract

Internet of Things (IoT) has become an enabling technology in a huge number of diverse domains, including smart manufacturing, health, and cities. The latter encompasses cyber-physical infrastructures able to improve the citizens’ quality of life in a broader sense, including so-called smart grids (for water, natural gas, energy delivery). This chapter highlights the challenges and possible solutions related to the IoT-oriented design and deployment of smart water and gas grids, and unveils the potentially disruptive impact data analytics and machine learning could have in their management, and in consumption forecasting. The chapter reviews both capillary networks, and future opportunities provided by cellular IoT, as communication infrastructures. Open issues in network planning for smart metering applications, from an electromagnetic perspective, are also discussed, and supported by experimental evaluations. A thorough review of the state-of-the-art literature in the field of machine learning for leakage detection and consumption forecasting concludes the chapter.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/252825
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