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Electronics
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IoT Technologies for Smart Cities
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IoT Technologies for Smart Cities

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 July 2020) | Viewed by 10016

Special Issue Editors

Dr. Edoardo Patti

E-Mail Website
Guest Editor
Department of Control and Computer Engineering, Politecnico di Torino, 10129 Torino, Italy
Interests: Internet of Things; ubiquitous computing; smart cities and smart grids; distributed software infrastructure; co-simulation architecture; user awareness
Dr. Lorenzo Bottaccioli

E-Mail Website
Guest Editor
Department of Control and Computer Engineering, Politecnico di Torino, 10129 Torino, Italy
Interests: Internet of Things; smart cities; smart grids; co-simulation architecture; energy and urban informatics; renewable energy systems; energy efficiency
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, we are driving a revolution, pushing new technologies into our cities in order to make them more sustainable and smarter. This innovative view is also known as “smart city”, defined as “an effective integration of physical, digital and human systems in the built environment to deliver a sustainable, prosperous and inclusive future for its citizens” by the British Standards Institution. Thus, rising information and communication technologies, with particular emphasis on Internet of things (IoT) technologies and machine learning solutions, play a crucial role in this transformation, opening up new opportunities to improve the quality of life, and the efficiency of urban operation and services. In this view, different domains, including health, energy, transportation, industry, agriculture, and so on, will change radically.

In this Special Section, we are particularly interested in innovative solutions where IoT technologies are adopted in order to foster this radical change. Topics of interest include, but are not limited to, the following:

  • Scalable distributed software solutions for smart city management
  • Machine learning techniques for smart cities and smart grids
  • Machine learning solutions for edge embedded devices in smart cities applications
  • Big data techniques to handle and process huge smart cities datasets
  • Programming models and tools to develop and support services in smart cities
  • Cyber-security in applications smart cities
  • IoT application for energy efficiency and smart energy systems

Dr. Edoardo Patti
Dr. Lorenzo Bottaccioli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of things
  • Cyber–physical systems
  • Distributed software infrastructure
  • Scalable systems
  • Machine learning
  • Big data
  • Edge embedded devices
  • Edge computing
  • Smart energy systems
  • Energy efficiency

Published Papers (3 papers)

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Research

22 pages, 5947 KiB  
Article
Predicting Car Availability in Free Floating Car Sharing Systems: Leveraging Machine Learning in Challenging Contexts
by Elena Daraio, Luca Cagliero, Silvia Chiusano, Paolo Garza and Danilo Giordano
Electronics 2020, 9(8), 1322; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9081322 - 16 Aug 2020
Cited by 9 | Viewed by 3016
Abstract
Free-Floating Car Sharing (FFCS) services are currently available in tens of cities and countries spread all over the worlds. Depending on citizens’ habits, service policies, and road conditions, car usage profiles are rather variable and often hardly predictable. Even within the same city, [...] Read more.
Free-Floating Car Sharing (FFCS) services are currently available in tens of cities and countries spread all over the worlds. Depending on citizens’ habits, service policies, and road conditions, car usage profiles are rather variable and often hardly predictable. Even within the same city, different usage trends emerge in different districts and in various time slots and weekdays. Therefore, modeling car availability in FFCS systems is particularly challenging. For these reasons, the research community has started to investigate the applicability of Machine Learning models to analyze FFCS usage data. This paper addresses the problem of predicting the short-term level of availability of the FFCS service in the short term. Specifically, it investigates the applicability of Machine Learning models to forecast the number of available car within a restricted urban area. It seeks the spatial and temporal contexts in which nonlinear ML models, trained on past usage data, are necessary to accurately predict car availability. Leveraging ML has shown to be particularly effective while considering highly dynamic urban contexts, where FFCS service usage is likely to suddenly and unexpectedly change. To tailor predictive models to the real FFCS data, we study also the influence of ML algorithm, prediction horizon, and characteristics of the neighborhood of the target area. The empirical outcomes allow us to provide system managers with practical guidelines to setup and tune ML models. Full article
(This article belongs to the Special Issue IoT Technologies for Smart Cities)
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22 pages, 4878 KiB  
Article
A Real-Time Decision Platform for the Management of Structures and Infrastructures
by Massimo Merenda, Filippo Giammaria Praticò, Rosario Fedele, Riccardo Carotenuto and Francesco Giuseppe Della Corte
Electronics 2019, 8(10), 1180; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics8101180 - 17 Oct 2019
Cited by 35 | Viewed by 3223
Abstract
Natural disasters and the poor management of civil engineering structures and infrastructures require timely action and new tools such as specially designed structural health monitoring platforms. This paper proposes an innovative platform based on a network of wirelessly connected, low-power, and renewable-energy-fed sensor [...] Read more.
Natural disasters and the poor management of civil engineering structures and infrastructures require timely action and new tools such as specially designed structural health monitoring platforms. This paper proposes an innovative platform based on a network of wirelessly connected, low-power, and renewable-energy-fed sensor units. The platform is a multipurpose tool for diagnostics, maintenance, and supervision, capable of simultaneously carrying out damage detection, localization, identification, and “multiclass” and “multi-material” level quantification of different types of failures. In addition, it works as a decision support tool for emergency management and post-disaster assessment, here tailored for an Italian theme park. The platform uses innovative algorithms based on the concept of the vibro-acoustic signature of the asset monitored. The vibro-acoustic signatures of the monitored assets are gathered by the microphones and accelerometers of the platform’s sensor units. Then, almost simultaneously, they are analyzed using specifically designed wavelet-based and convolutional-neural-network-based algorithms, which are able to extract crucial information about the structural and environmental conditions of both the asset and the areas of the thematic park. In addition, the platform shows escape routes during an emergency, indicating meeting points and helping people to proceed safely along a recognizable escape route to a safe place, as demonstrated by the simulations. Full article
(This article belongs to the Special Issue IoT Technologies for Smart Cities)
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17 pages, 1655 KiB  
Article
A Non-Linear Autoregressive Model for Indoor Air-Temperature Predictions in Smart Buildings
by Alessandro Aliberti, Lorenzo Bottaccioli, Enrico Macii, Santa Di Cataldo, Andrea Acquaviva and Edoardo Patti
Electronics 2019, 8(9), 979; https://doi.org/10.3390/electronics8090979 - 02 Sep 2019
Cited by 27 | Viewed by 3213
Abstract
In recent years, the contrast against energy waste and pollution has become mandatory and widely endorsed. Among the many actors at stake, the building sector energy management is one of the most critical. Indeed, buildings are responsible for 40 % of total energy [...] Read more.
In recent years, the contrast against energy waste and pollution has become mandatory and widely endorsed. Among the many actors at stake, the building sector energy management is one of the most critical. Indeed, buildings are responsible for 40 % of total energy consumption only in Europe, affecting more than a third of the total pollution produced. Therefore, energy control policies of buildings (for example, forecast-based policies such as Demand Response and Demand Side Management) play a decisive role in reducing energy waste. On these premises, this paper presents an innovative methodology based on Internet-of-Things (IoT) technology for smart building indoor air-temperature forecasting. In detail, our methodology exploits a specialized Non-linear Autoregressive neural network for short- and medium-term predictions, envisioning two different exploitation: (i) on realistic artificial data and (ii) on real data collected by IoT devices deployed in the building. For this purpose, we designed and optimized four neural models, focusing respectively on three characterizing rooms and on the whole building. Experimental results on both a simulated and a real sensors dataset demonstrate the prediction accuracy and robustness of our proposed models. Full article
(This article belongs to the Special Issue IoT Technologies for Smart Cities)
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