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Smart Cities, Volume 4, Issue 4 (December 2021) – 7 articles

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Article
An Anthropocentric and Enhanced Predictive Approach to Smart City Management
Smart Cities 2021, 4(4), 1366-1390; https://doi.org/10.3390/smartcities4040072 - 21 Oct 2021
Viewed by 253
Abstract
Cities are becoming increasingly complex to manage, as they increase in size and must provide higher living standards for their populations. New technology-based solutions must be developed towards attending this growth and ensuring that it is socially sustainable. This paper puts forward the [...] Read more.
Cities are becoming increasingly complex to manage, as they increase in size and must provide higher living standards for their populations. New technology-based solutions must be developed towards attending this growth and ensuring that it is socially sustainable. This paper puts forward the notion that these solutions must share some properties: they should be anthropocentric, holistic, horizontal, multi-dimensional, multi-modal, and predictive. We propose an architecture in which streaming data sources that characterize the city context are used to feed a real-time graph of the city’s assets and states, as well as to train predictive models that hint into near future states of the city. This allows human decision-makers and automated services to take decisions, both for the present and for the future. To achieve this, multiple data sources about a city were gradually connected to a message broker, that enables increasingly rich decision-support. Results show that it is possible to predict future states of a city, in aspects such as traffic, air pollution, and other ambient variables. The key innovative aspect of this work is that, as opposed to the majority of existing approaches which focus on a real-time view of the city, we also provide insights into the near-future state of the city, thus allowing city services to plan ahead and adapt accordingly. The main goal is to optimize decision-making by anticipating future states of the city and make decisions accordingly. Full article
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Article
Scaling Up Smart City Logistics Projects: The Case of the Smooth Project
Smart Cities 2021, 4(4), 1337-1365; https://doi.org/10.3390/smartcities4040071 - 15 Oct 2021
Viewed by 100
Abstract
A large number of smart city logistics projects fail to scale up, remaining a local experimental exercise. This lack of scalability is, in fact, commonly recognized as a major problem. This study aims to determine the key success factors related to the scalability [...] Read more.
A large number of smart city logistics projects fail to scale up, remaining a local experimental exercise. This lack of scalability is, in fact, commonly recognized as a major problem. This study aims to determine the key success factors related to the scalability of smart city logistics projects. The process of scaling up, which is articulated as expansion, roll-out, and replication, is defined as the ability of a system to improve its scale by aiming to meet the increasing volume demand. Specifically, this study investigates the scalability intended to be used as expansion and roll-out. A qualitative case study was conducted to fulfill the research purpose. The chosen case study is SMOOTh, a pilot project currently underway in the city of Gothenburg, Sweden, involving a diverse group of companies including Volvo Group and DHL. Semi-structured interviews were conducted with seven of the project’s stakeholders. Through a thematic analysis, four categories and the respective success factors were identified. These were represented by a business model, as well as technical, stakeholder and regulatory factors. The paper concludes with observations and recommendations aimed at the pilot initiatives, adding new perspectives to the upscaling debate. Full article
(This article belongs to the Special Issue Advances in Connected and Autonomous Vehicles)
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Article
Requirements and Architecture of a Cloud Based Insomnia Therapy and Diagnosis Platform: A Smart Cities Approach
Smart Cities 2021, 4(4), 1316-1336; https://doi.org/10.3390/smartcities4040070 - 12 Oct 2021
Viewed by 365
Abstract
Insomnia is the most common sleep disorder worldwide. Its effects generate economic costs in the millions but could be effectively reduced using digitally provisioned cognitive behavioural therapy. However, traditional acquisition and maintenance of the necessary technical infrastructure requires high financial and personnel expenses. [...] Read more.
Insomnia is the most common sleep disorder worldwide. Its effects generate economic costs in the millions but could be effectively reduced using digitally provisioned cognitive behavioural therapy. However, traditional acquisition and maintenance of the necessary technical infrastructure requires high financial and personnel expenses. Sleep analysis is still mostly done in artificial settings in clinical environments. Nevertheless, innovative IT infrastructure, such as mHealth and cloud service solutions for home monitoring, are available and allow context-aware service provision following the Smart Cities paradigm. This paper aims to conceptualise a digital, cloud-based platform with context-aware data storage that supports diagnosis and therapy of non-organic insomnia. In a first step, requirements needed for a remote diagnosis, therapy, and monitoring system are identified. Then, the software architecture is drafted based on the above mentioned requirements. Lastly, an implementation concept of the software architecture is proposed through selecting and combining eleven cloud computing services. This paper shows how treatment and diagnosis of a common medical issue could be supported effectively and cost-efficiently by utilising state-of-the-art technology. The paper demonstrates the relevance of context-aware data collection and disease understanding as well as the requirements regarding health service provision in a Smart Cities context. In contrast to existing systems, we provide a cloud-based and requirement-driven reference architecture. The applied methodology can be used for the development, design, and evaluation of other remote and context-aware diagnosis and therapy systems. Considerations of additional aspects regarding cost, methods for data analytics as well as general data security and safety are discussed. Full article
(This article belongs to the Special Issue Systems, Applications and Services for Smart Health)
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Article
Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems
Smart Cities 2021, 4(4), 1293-1315; https://doi.org/10.3390/smartcities4040069 - 01 Oct 2021
Viewed by 341
Abstract
This paper presents an investigation of the capacity of machine learning methods (ML) to localize leakage in water distribution systems (WDS). This issue is critical because water leakage causes economic losses, damages to the surrounding infrastructures, and soil contamination. Progress in real-time monitoring [...] Read more.
This paper presents an investigation of the capacity of machine learning methods (ML) to localize leakage in water distribution systems (WDS). This issue is critical because water leakage causes economic losses, damages to the surrounding infrastructures, and soil contamination. Progress in real-time monitoring of WDS and ML has created new opportunities to develop data-based methods for water leak localization. However, the managers of WDS need recommendations for the selection of the appropriate ML methods as well their practical use for leakage localization. This paper contributes to this issue through an investigation of the capacity of ML methods to localize leakage in WDS. The campus of Lille University was used as support for this research. The paper is presented as follows: First, flow and pressure data were determined using EPANET software; then, the generated data were used to investigate the capacity of six ML methods to localize water leakage. Finally, the results of the investigations were used for leakage localization from offline water flow data. The results showed excellent performance for leakage localization by the artificial neural network, logistic regression, and random forest, but there were low performances for the unsupervised methods because of overlapping clusters. Full article
(This article belongs to the Special Issue Machine Learning and Big Data in Geosciences)
Review
Role of Internet of Things (IoT) and Crowdsourcing in Smart City Projects
Smart Cities 2021, 4(4), 1276-1292; https://doi.org/10.3390/smartcities4040068 - 01 Oct 2021
Viewed by 465
Abstract
This paper presents and discusses the role of the Internet of Things (IoT) and crowdsourcing in constructing smart cities. The literature review shows an important and increasing concern of the scientific community for these three issues and their association as support for urban [...] Read more.
This paper presents and discusses the role of the Internet of Things (IoT) and crowdsourcing in constructing smart cities. The literature review shows an important and increasing concern of the scientific community for these three issues and their association as support for urban development. Based on an extensive literature review, the paper first presents the smart city concept, emphasizing smart city architecture and the role of data in smart city solutions. The second part presents the Internet of Things, focusing on IoT technology, the use of IoT in smart city applications, and security. Finally, the paper presents crowdsourcing with particular attention to mobile crowdsourcing and its role in smart cities. The paper shows that IoT and crowdsourcing have a crucial role in two fundamental layers of smart city applications, namely, the data collection and services layers. Since these two layers ensure the connection between the physical and digital worlds, they constitute the central pillars of smart city projects. The literature review also shows that the smart city development still requires stronger cooperation between the smart city technology-centered research, mainly based on the IoT, and the smart city citizens-centered research, mainly based on crowdsourcing. This cooperation could beneficiate in recent developments in the field of crowdsensing that combines IoT and crowdsourcing. Full article
(This article belongs to the Section Internet of Things)
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Review
The Favela as a Place for the Development of Smart Cities in Brazil: Local Needs and New Business Strategies
Smart Cities 2021, 4(4), 1259-1275; https://doi.org/10.3390/smartcities4040067 - 29 Sep 2021
Viewed by 392
Abstract
Smart cities are a natural evolution of the concept of sustainable cities. These cities can be analyzed by social, economic, environmental, and technological biases. For this work, we chose the social and economic vision, with a special focus on the poorest and most [...] Read more.
Smart cities are a natural evolution of the concept of sustainable cities. These cities can be analyzed by social, economic, environmental, and technological biases. For this work, we chose the social and economic vision, with a special focus on the poorest and most vulnerable territories of Brazilian cities. These territories in Brazil are called slums, places of poverty but with opportunities for the development of the creative economy with its own brand. Seen by many in a simplistic way, summed up to be geographic spaces of drug circulation dominated by trafficking, Brazilian favelas have been consolidating themselves as a storehouse of innovative minds, a creative territory with multiple and complex cultures. These places today are capable of producing a positive image with potential for market exploitation. Therefore, the objective was to draw a relationship between the creative economy, branding and favelas, considering the concept of smart cities that include products and services from the slums. The present study shows the results of a survey and a bibliographic analysis based on the methodology Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and with parameters that took into account the favela, branding and the creative economy. Thus, we expect that it will be possible to point out ways to accelerate entrepreneurial actions and foster the development of these locations. Full article
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Article
Identifying and Quantifying Factors Determining Dynamic Vanpooling Use
Smart Cities 2021, 4(4), 1243-1258; https://doi.org/10.3390/smartcities4040066 - 29 Sep 2021
Viewed by 417
Abstract
Nowadays, the growth of traffic congestion and emissions has led to the emergence of an innovative and sustainable transportation service, called dynamic vanpooling. The main aim of this study is to identify factors affecting the travel behavior of passengers due to the introduction [...] Read more.
Nowadays, the growth of traffic congestion and emissions has led to the emergence of an innovative and sustainable transportation service, called dynamic vanpooling. The main aim of this study is to identify factors affecting the travel behavior of passengers due to the introduction of dynamic vanpooling in the transportation system. A web-based mode choice survey was designed and implemented for this scope. The stated-preference experiments offered respondents binary hypothetical scenarios with an ordered choice between dynamic vanpool and the conventional modes of transport, private car and public transportation. In-vehicle travel time, total travel cost and walking and waiting time or searching time for parking varies across the choice scenarios. An ordered probit model, a multinomial logit model and two binary logit models were specified. The model estimation results indicate that respondents who are aged between 26 and 35 years old, commute with PT or are members of bike-sharing services were significantly more likely to choose dynamic vanpool or PT than private car. Moreover, respondents who are worried about climate change and are willing to spend more for environmentally friendly products are significantly more likely to use dynamic vanpool in comparison with private cars. Finally, to indicate the model estimation results for dynamic vanpool, the value of in-vehicle travel time is found to be 12.2€ per hour (13.4€ for Munich subsample). Full article
(This article belongs to the Special Issue Advances in Connected and Autonomous Vehicles)
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