Special Issue "Feature Papers for Smart Cities"

A special issue of Smart Cities (ISSN 2624-6511).

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editor

Prof. Dr. Pierluigi Siano
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Guest Editor
Scientific Director of the Smart Grids and Smart Cities Laboratory (SMARTLab), Department of Management & Innovation Systems, University of Salerno, 84084 Fisciano SA, Italy
Interests: smart grids; energy management; power systems; demand response
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will collect and highlight recent work in smart cities. We are inviting reviews, regular research papers (articles), and communications in all areas of research concerning smart cities. Topics include, but are not limited to the following:

  • Electrical engineering for smart cities: smart grids, smart buildings, smart homes, smart lighting, renewable energies, and power electronics for smart cities.
  • Computer engineering and information technology engineering for smart cities: ICT infrastructure and information management in smart cities, IoT architectures, protocols, and algorithms, IoT device technologies, IoT network technologies, cloud computing, autonomic computing, data management, intelligent data processing, and big data management for smart cities, real-time, and semantic web services, context-aware systems for smart cities.
  • Cyber-physical systems for smart cities.
  • Virtual reality for smart cities.
  • Smart hospitals and health informatics for smart cities: smart health, e-health, digital health, telehealth, telemedicine.
  • Transport and mobility: intelligent transportation systems and vehicular networks, smart mobility, smart parking, traffic congestion, city logistics, people mobility.
  • Electronics, telecommunication, and measurements engineering for smart cities: networks and communications, advances in smart grid sensing, sensor interface, and synchronization in smart grids, multisensor data fusion models for smart grids and smart cities, traceability and calibration of distributed sensing grids, distributed and networked sensors for smart cities, wireless sensor networks, embedded sensing and actuating, radio frequency identification (RFID), mobile internet, ubiquitous sensing.
  • Civil engineering for smart cities: smart city architecture and infrastructure, environmental engineering for smart cities, smart water management, sustainable districts and urban development, waste management for smart cities, smart agriculture, greenhouses.
  • Weather analysis, forecasting, reporting, and flood management for smart cities.
  • Mechanical sciences and automobile engineering for smart cities.
  • Applied science and humanities for smart cities.
  • Retail for smart cities: supply chain control, NFC Payment, intelligent shopping applications, smart product management, etc.
  • Security, privacy, and emergencies in smart cities, cryptography, identity management.
  • Smart living: pollution control, public safety, healthcare, welfare and social innovation, culture, public spaces.
  • Smart urban governance and e-government for smart cities
  • Business and social issues for smart cities: smart economy and business model innovation in smart cities, marketing strategies for firms offering new services in smart cities
  • Experimentation and deployments: real solutions, system design, modeling and evaluation for smart cities, pilot deployments, performance evaluation
  • Trends and challenges in smart cities
  • Intelligent transportation systems, multimodal traffic to improve mobility
  • Industry 4.0 challenges and opportunities in smart city/smart region development

Manuscripts for this important Special Issue of Smart Cities will be accepted by the editorial office, Editor-in-Chief, and editorial board members by invitation only. All the papers in this Special Issue will be published as free of charge.

Procedure

  1. All submissions will be rigorously reviewed according to the Smart Cities journal guidelines.
  2. Manuscripts that are not suitable for this Special Issue will be notified as soon as after consultation with the editorial board members. Authors of these manuscripts may still consider submitting to Smart Cities as a regular paper. Other manuscripts will be forwarded for review.
  3. Manuscripts that are not selected as feature papers will be notified after the first round of reviews. The selection will be based on the review. Authors of those manuscripts that are not selected for the Special Issue may decide to revise and submit as a regular paper. Please note that authors of these manuscripts need to shoulder the publication fees.
  4. Other manuscripts will be sent for a second round of reviews. However, this does not necessarily mean that a manuscript under the second round of reviews will be published as a feature paper. We will still seek comments and suggestions from reviewers.

Please contact Traey Wu ([email protected]), the managing editor, if you have any questions.

Prof. Dr. Pierluigi Siano
Guest Editor

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 papers will be 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. Smart Cities is an international peer-reviewed open access quarterly 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 1000 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.

Published Papers (15 papers)

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Research

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Open AccessFeature PaperArticle
Explainable Artificial Intelligence for Developing Smart Cities Solutions
Smart Cities 2020, 3(4), 1353-1382; https://doi.org/10.3390/smartcities3040065 - 13 Nov 2020
Abstract
Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach [...] Read more.
Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results. Full article
(This article belongs to the Special Issue Feature Papers for Smart Cities)
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Open AccessArticle
Use of Social Media to Seek and Provide Help in Hurricanes Florence and Michael
Smart Cities 2020, 3(4), 1187-1218; https://doi.org/10.3390/smartcities3040059 - 14 Oct 2020
Abstract
During hazardous events, communities can use existing social media networks to share information in real time and initiate a local disaster response. This research conducted a web-based survey to explore two behaviors around the use of social media during hurricanes: seeking help and [...] Read more.
During hazardous events, communities can use existing social media networks to share information in real time and initiate a local disaster response. This research conducted a web-based survey to explore two behaviors around the use of social media during hurricanes: seeking help and responding to help requests. Through the survey, we sampled 434 individuals across several counties affected by 2018 hurricanes Florence and Michael, which were both designated by the National Oceanic and Atmospheric Administration as billion-dollar weather disasters. The survey questions collected data about demographics, social media use habits, perceptions towards social media, hurricane damages, and actions taken during a hurricane to seek and provide help. The Theory of Planned Behavior (TPB) was used to conceptualize and frame parameters that affect intentions and behaviors regarding the use of social media during hurricanes to seek and provide help. Survey responses are analyzed using statistical regression to evaluate hypotheses about the influence of factors on seeking help and responding to help requests. Regression analyses indicate that attitude and perceived behavioral control predict intention to access social media during a hurricane, partially supporting the TPB. Intention and experiencing urgent damages predict help-seeking behaviors using social media. Posting frequency to social media under normal conditions and the number of help requests seen during the event predict help-responding behaviors. Linear regression equations governing intention and behavior were parameterized using survey results. The factors underlying social media behavior during hurricanes as identified in this research provide insight for understanding how smart information technologies, such as personal devices and social media networks, support community self-sufficiency and hazard resilience. Full article
(This article belongs to the Special Issue Feature Papers for Smart Cities)
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Open AccessArticle
Perceptions on Smart Gas Meters in Smart Cities for Reducing the Carbon Footprint
Smart Cities 2020, 3(4), 1173-1186; https://doi.org/10.3390/smartcities3040058 - 13 Oct 2020
Abstract
Carbon emission is a prominent issue, and smart urban solutions have the technological capabilities to implement change. The technologies for creating smart energy systems already exist, some of which are currently under wide deployment globally. By investing in energy efficiency solutions (such as [...] Read more.
Carbon emission is a prominent issue, and smart urban solutions have the technological capabilities to implement change. The technologies for creating smart energy systems already exist, some of which are currently under wide deployment globally. By investing in energy efficiency solutions (such as the smart meter), research shows that the end-user is able to not only save money, but also reduce their household’s carbon footprint. Therefore, in this paper, the focus is on the end-user, and adopting a quantitative analysis of the perception of 1365 homes concerning the smart gas meter installation. The focus is on linking end-user attributes (age, education, social class and employment status) with their opinion on reducing energy, saving money, changing home behaviour and lowering carbon emissions. The results show that there is a statistical significance between certain attributes of end-users and their consideration of smart meters for making beneficial changes. In particular, the investigation demonstrates that the employment status, age and social class of the homeowner have statistical significance on the end-users’ variance; particularly when interested in reducing their bill and changing their behaviour around the home. Full article
(This article belongs to the Special Issue Feature Papers for Smart Cities)
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Open AccessFeature PaperArticle
Artificial Intelligence and Robotics in Smart City Strategies and Planned Smart Development
Smart Cities 2020, 3(4), 1133-1144; https://doi.org/10.3390/smartcities3040056 - 03 Oct 2020
Abstract
Smart city strategies developed by cities around the world provide a useful resource for insights into the future of smart development. This study examines such strategies to identify plans for the explicit deployment of artificial intelligence (AI) and robotics. A total of 12 [...] Read more.
Smart city strategies developed by cities around the world provide a useful resource for insights into the future of smart development. This study examines such strategies to identify plans for the explicit deployment of artificial intelligence (AI) and robotics. A total of 12 case studies emerged from an online keyword search representing cities of various sizes globally. The search was based on the keywords of “artificial intelligence” (or “AI”), and “robot,” representing robotics and associated terminology. Based on the findings, it is evident that the more concentrated deployment of AI and robotics in smart city development is currently in the Global North, although countries in the Global South are also increasingly represented. Multiple cities in Australia and Canada actively seek to develop AI and robotics, and Moscow has one of the most in-depth elaborations for this deployment. The ramifications of these plans are discussed as part of cyber–physical systems alongside consideration given to the social and ethical implications. Full article
(This article belongs to the Special Issue Feature Papers for Smart Cities)
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Open AccessArticle
IoT-Enabled Smart Sustainable Cities: Challenges and Approaches
Smart Cities 2020, 3(3), 1039-1071; https://doi.org/10.3390/smartcities3030052 - 18 Sep 2020
Abstract
The ongoing diffusion of Internet of Things (IoT) technologies is opening new possibilities, and one of the most remarkable applications is associated with the smart city paradigm, which is continuously evolving. In general, it can be defined as the integration of IoT and [...] Read more.
The ongoing diffusion of Internet of Things (IoT) technologies is opening new possibilities, and one of the most remarkable applications is associated with the smart city paradigm, which is continuously evolving. In general, it can be defined as the integration of IoT and Information Communication Technologies (ICT) into city management, with the aim of addressing the exponential growth of urbanization and population, thus significantly increasing people’s quality of life. The smart city paradigm is also strictly connected to sustainability aspects, taking into account, for example, the reduction of environmental impact of urban activities, the optimized management of energy resources, and the design of innovative services and solution for citizens. Abiding by this new paradigm, several cities started a process of strong innovation in different fields (such as mobility and transportation, industry, health, tourism, and education), thanks to significant investments provided by stakeholders and the European Commission (EC). In this paper, we analyze key aspects of an IoT infrastructure for smart cities, outlining the innovations implemented in the city of Parma (Emilia Romagna region, Italy) as a successful example. Special attention is dedicated to the theme of smart urban mobility. Full article
(This article belongs to the Special Issue Feature Papers for Smart Cities)
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Open AccessFeature PaperArticle
A New SDN-Based Routing Protocol for Improving Delay in Smart City Environments
Smart Cities 2020, 3(3), 1004-1021; https://doi.org/10.3390/smartcities3030050 - 09 Sep 2020
Abstract
The smart city is an ecosystem that interconnects various devices like sensors, actuators, mobiles, and vehicles. The intelligent and connected transportation system (ICTS) is an essential part of this ecosystem that provides new real-time applications. The emerging applications are based on Internet-of-Things (IoT) [...] Read more.
The smart city is an ecosystem that interconnects various devices like sensors, actuators, mobiles, and vehicles. The intelligent and connected transportation system (ICTS) is an essential part of this ecosystem that provides new real-time applications. The emerging applications are based on Internet-of-Things (IoT) technologies, which bring out new challenges, such as heterogeneity and scalability, and they require innovative communication solutions. The existing routing protocols cannot achieve these requirements due to the surrounding knowledge supported by individual nodes and their neighbors, displaying partial visibility of the network. However, the issue grew ever more arduous to conceive routing protocols to satisfy the ever-changing network requirements due to its dynamic topology and its heterogeneity. Software-Defined Networking (SDN) offers the latest view of the entire network and the control of the network based on the application’s specifications. Nonetheless, one of the main problems that arise when using SDN is minimizing the transmission delay between ubiquitous nodes. In order to meet this constraint, a well-attended and realistic alternative is to adopt the Machine Learning (ML) algorithms as prediction solutions. In this paper, we propose a new routing protocol based on SDN and Naive Bayes solution to improve the delay. Simulation results show that our routing scheme outperforms the comparative ones in terms of end-to-end delay and packet delivery ratio. Full article
(This article belongs to the Special Issue Feature Papers for Smart Cities)
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Open AccessArticle
Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data
Smart Cities 2020, 3(3), 818-841; https://doi.org/10.3390/smartcities3030042 - 06 Aug 2020
Abstract
Efficient and reliable mobility pattern identification is essential for transport planning research. In order to infer mobility patterns, however, a large amount of spatiotemporal data is needed, which is not always available. Hence, location-based social networks (LBSNs) have received considerable attention as a [...] Read more.
Efficient and reliable mobility pattern identification is essential for transport planning research. In order to infer mobility patterns, however, a large amount of spatiotemporal data is needed, which is not always available. Hence, location-based social networks (LBSNs) have received considerable attention as a potential data provider. The aim of this study is to investigate the possibility of using several different auxiliary information sources for venue popularity modeling and provide an alternative venue popularity measuring approach. Initially, data from widely used services, such as Google Maps, Yelp and OpenStreetMap (OSM), are used to model venue popularity. To estimate hourly venue occupancy, two different classes of model are used, including linear regression with lasso regularization and gradient boosted regression (GBR). The predictions are made based on venue-related parameters (e.g., rating, comments) and locational properties (e.g., stores, hotels, attractions). Results show that the prediction can be improved using GBR with a logarithmic transformation of the dependent variables. To investigate the quality of social media-based models by obtaining WiFi-based ground truth data, a microcontroller setup is developed to measure the actual number of people attending venues using WiFi presence detection, demonstrating that the similarity between the results of WiFi data collection and Google “Popular Times” is relatively promising. Full article
(This article belongs to the Special Issue Feature Papers for Smart Cities)
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Open AccessArticle
From a Comprehensive Pool to a Project-Specific List of Key Performance Indicators for Monitoring the Positive Energy Transition of Smart Cities—An Experience-Based Approach
Smart Cities 2020, 3(3), 705-735; https://doi.org/10.3390/smartcities3030036 - 14 Jul 2020
Abstract
As cities grow rapidly and energy needs increase, shaping an effective energy transition is a top priority towards urban sustainability and smart development. This study attempts to answer three key research questions that can help city authorities, planners and interested agents simplify and [...] Read more.
As cities grow rapidly and energy needs increase, shaping an effective energy transition is a top priority towards urban sustainability and smart development. This study attempts to answer three key research questions that can help city authorities, planners and interested agents simplify and increase the transparency of Key Performance Indicators (KPIs) selection for smart city and communities (SCC) projects focusing on energy transition and creation of Positive Energy Districts (PEDs): Question 1: “What resources are available for extracting such KPIs?”; Question 2: “Which of those KPIs are the most suitable for assessing the energy transition of smart city projects and PED-related developments?” and Question 3: “How can a project-specific shortlist of KPIs be developed?”. Answering these questions can also serve as a major first step towards a “universal” KPI selection procedure. In line with this purpose, an experiential approach is presented, capitalizing on knowledge and lessons learned from an ongoing smart city project in Europe (POCITYF) that focuses on PED deployment. Under this framework, a) a review of smart city KPI frameworks has been conducted, resulting in a pool of 258 indicators that can potentially be adopted by smart city projects; b) eight key dimensions of evaluations were extracted, setting a holistic performance framework relevant to SCCs; c) a detailed evaluation process including pre-determined criteria and city-needs feedback was applied to shortlist the KPI pool, leading to a ready-to-be-used, project-specific list of 63 KPIs and d) KPIs were sorted and analyzed in different granularity levels to further facilitate the monitoring procedure. The experiential procedure presented in this study can be easily adapted to the needs of every smart city project, serving as a recommendation guide. Full article
(This article belongs to the Special Issue Feature Papers for Smart Cities)
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Open AccessArticle
The Nexus between Market Needs and Value Attributes of Smart City Solutions towards Energy Transition. An Empirical Evidence of Two European Union (EU) Smart Cities, Evora and Alkmaar
Smart Cities 2020, 3(3), 604-641; https://doi.org/10.3390/smartcities3030032 - 06 Jul 2020
Abstract
This study presents an experiential process and a market-oriented approach for realizing cities’ energy transition through smart solutions. The aim of this study is twofold: (a) present a process for defining a repository of innovative solutions that can be applied at building, district, [...] Read more.
This study presents an experiential process and a market-oriented approach for realizing cities’ energy transition through smart solutions. The aim of this study is twofold: (a) present a process for defining a repository of innovative solutions that can be applied at building, district, or city level, for two European Union cities, Evora and Alkmaar, and support the deployment of positive energy districts enabling a sustainable energy transition, and (b) understand in a systematic way the attributes of value offered by energy-related smart city solutions, in order to facilitate the development of sustainable value propositions that can successfully address city needs. The repository is assessed against four elements of value, which include social impact, life-changing, emotional, and functional attributes, according to the value pyramid of Maslow. Results show that the value attributes of quality, motivation, integration, cost reduction, information, and organization are highly relevant to the proposed smart solutions. The results presented in this study are useful for city planners, decision-makers, public bodies, citizens, and businesses interested in designing their energy transition strategy and defining novel technologies that promote urban energy sustainability. Full article
(This article belongs to the Special Issue Feature Papers for Smart Cities)
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Open AccessArticle
An Investigation on the Feasibility of Near-Zero and Positive Energy Communities in the Greek Context
Smart Cities 2020, 3(2), 362-384; https://doi.org/10.3390/smartcities3020019 - 09 May 2020
Cited by 2
Abstract
Near Zero Energy and Positive Energy communities are expected to play a significant part in EU’s strategy to cut greenhouse gas emissions by 2050. Within this context, the work presented in this paper aims to investigate the feasibility of: (a) a new-built positive [...] Read more.
Near Zero Energy and Positive Energy communities are expected to play a significant part in EU’s strategy to cut greenhouse gas emissions by 2050. Within this context, the work presented in this paper aims to investigate the feasibility of: (a) a new-built positive energy neighborhood; and (b) the retrofit of an existing neighborhood to near zero energy performance in the city of Alexandroupolis, Greece. Proposed measures involve the rollout at the community scale of renewable energy technologies (PV, geothermal heat pump), energy efficiency (fabric insulation, district heating and cooling networks) and storage systems (batteries). A parametric analysis is conducted to identify the optimum combination of technologies through suitable technical and financial criteria. Results indicate that zero and near zero emissions targets are met with various combinations that impose insulation levels, according to building regulations or slightly higher, and consider renewable energy production with an autonomy of half or, more commonly, one day. In addition, the advantages of performing nearly zero energy retrofit at the district, rather than the building level, are highlighted, in an attempt to stimulate interest in community energy schemes. Full article
(This article belongs to the Special Issue Feature Papers for Smart Cities)
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Open AccessArticle
Leveraging Intelligent Transportation Systems and Smart Vehicles Using Crowdsourcing: An Overview
Smart Cities 2020, 3(2), 341-361; https://doi.org/10.3390/smartcities3020018 - 08 May 2020
Cited by 2
Abstract
The current and expected future proliferation of mobile and embedded technology provides unique opportunities for crowdsourcing platforms to gather more user data for making data-driven decisions at the system level. Intelligent Transportation Systems (ITS) and Vehicular Social Networks (VSN) can be leveraged by [...] Read more.
The current and expected future proliferation of mobile and embedded technology provides unique opportunities for crowdsourcing platforms to gather more user data for making data-driven decisions at the system level. Intelligent Transportation Systems (ITS) and Vehicular Social Networks (VSN) can be leveraged by mobile, spatial, and passive sensing crowdsourcing techniques due to improved connectivity, higher throughput, smart vehicles containing many embedded systems and sensors, and novel distributed processing techniques. These crowdsourcing systems have the capability of profoundly transforming transportation systems for the better by providing more data regarding (but not limited to) infrastructure health, navigation pathways, and congestion management. In this paper, we review and discuss the architecture and types of ITS crowdsourcing. Then, we delve into the techniques and technologies that serve as the foundation for these systems to function while providing some simulation results to show benefits from the implementation of these techniques and technologies on specific crowdsourcing-based ITS systems. Afterward, we provide an overview of cutting edge work associated with ITS crowdsourcing challenges. Finally, we propose various use-cases and applications for ITS crowdsourcing, and suggest some open research directions. Full article
(This article belongs to the Special Issue Feature Papers for Smart Cities)
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Review

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Open AccessReview
Review of Smart City Assessment Tools
Smart Cities 2020, 3(4), 1117-1132; https://doi.org/10.3390/smartcities3040055 - 30 Sep 2020
Abstract
Today’s cities are estimated to generate 80% of global GDP, covering only about 3% of the land, but contributing to about 72% of all global greenhouse gas emissions. Cities face significant challenges, such as population growth, pollution, congestion, lack of physical and social [...] Read more.
Today’s cities are estimated to generate 80% of global GDP, covering only about 3% of the land, but contributing to about 72% of all global greenhouse gas emissions. Cities face significant challenges, such as population growth, pollution, congestion, lack of physical and social infrastructures, while trying to simultaneously meet sustainable energy and environmental requirements. The Smart City concept intends to address these challenges by identifying new and intelligent ways to manage the complexity of urban living and implement solutions for multidisciplinary problems in cities. With the increasing number of Smart City projects being implemented around the world, it is important to evaluate their strengths and weaknesses for their future improvement and evolution track record. It is, therefore, crucial to characterize and improve the proper tools to adequately evaluate these implementations. Following the Smart City implementation growth, several Smart City Assessment tools with different indicator sets have been developed. This work presents a literature review on Smart City Assessment tools, discussing their main gaps in order to improve future methodologies and tools. Smart City Assessment can deliver important performance indicators monitoring for the evaluation of multiple benefits for different actors and stakeholders, such as city authorities, investors and funding agencies, researchers, and citizens. Full article
(This article belongs to the Special Issue Feature Papers for Smart Cities)
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Open AccessReview
Cyber Resilience and Incident Response in Smart Cities: A Systematic Literature Review
Smart Cities 2020, 3(3), 894-927; https://doi.org/10.3390/smartcities3030046 - 13 Aug 2020
Abstract
The world is experiencing a rapid growth of smart cities accelerated by Industry 4.0, including the Internet of Things (IoT), and enhanced by the application of emerging innovative technologies which in turn create highly fragile and complex cyber–physical–natural ecosystems. This paper systematically identifies [...] Read more.
The world is experiencing a rapid growth of smart cities accelerated by Industry 4.0, including the Internet of Things (IoT), and enhanced by the application of emerging innovative technologies which in turn create highly fragile and complex cyber–physical–natural ecosystems. This paper systematically identifies peer-reviewed literature and explicitly investigates empirical primary studies that address cyber resilience and digital forensic incident response (DFIR) aspects of cyber–physical systems (CPSs) in smart cities. Our findings show that CPSs addressing cyber resilience and support for modern DFIR are a recent paradigm. Most of the primary studies are focused on a subset of the incident response process, the “detection and analysis” phase whilst attempts to address other parts of the DFIR process remain limited. Further analysis shows that research focused on smart healthcare and smart citizen were addressed only by a small number of primary studies. Additionally, our findings identify a lack of available real CPS-generated datasets limiting the experiments to mostly testbed type environments or in some cases authors relied on simulation software. Therefore, contributing this systematic literature review (SLR), we used a search protocol providing an evidence-based summary of the key themes and main focus domains investigating cyber resilience and DFIR addressed by CPS frameworks and systems. This SLR also provides scientific evidence of the gaps in the literature for possible future directions for research within the CPS cybersecurity realm. In total, 600 papers were surveyed from which 52 primary studies were included and analysed. Full article
(This article belongs to the Special Issue Feature Papers for Smart Cities)
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Open AccessReview
Worldwide Coverage Mobile Systems for Supra-Smart Cities Communications: Featured Antennas and Design
Smart Cities 2020, 3(3), 556-584; https://doi.org/10.3390/smartcities3030030 - 01 Jul 2020
Abstract
Current terrestrial mobile communications networks can’t provide worldwide coverage. Satellite communications are expensive, and terminals are large and heavy. Worldwide mobile coverage requires the use of satellites providing an appropriate QoS, including polar regions. The analysis of the potential satellite constellations demonstrates that [...] Read more.
Current terrestrial mobile communications networks can’t provide worldwide coverage. Satellite communications are expensive, and terminals are large and heavy. Worldwide mobile coverage requires the use of satellites providing an appropriate QoS, including polar regions. The analysis of the potential satellite constellations demonstrates that LEO one is the best solution. A new generation of low cost, small size, lightweight and global mobile coverage LEO satellites is emerging. The main limitation of the terminals is the antenna size factor, and innovative antennas must be developed to meet this goal. This paper investigates the technologies and techniques for designing and developing antennas aimed at LEO satellite communications in Smart Cities and beyond, which are especially beneficial for mobile communications in areas without 4G/5G coverage. The paper focuses on the terrestrial segment and future mobile devices, remarking the design constraints. In this scenario, the paper reviews the most relevant technologies and techniques used to design suitable antennas. The investigation analyses the state-of-the-art and most recent advances in the design of antennas operating in the Ku-band. The main contribution of the authors is a novel antenna design approach based on SIW technology. The antenna features are compared with other approaches, highlighting the benefits, advantages and drawbacks. As a conclusion, the proposed antenna demonstrates to be a good solution to meet the design constraints for such an application: light, low cost, small size factor. Full article
(This article belongs to the Special Issue Feature Papers for Smart Cities)
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Open AccessLetter
Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities
Smart Cities 2020, 3(3), 842-852; https://doi.org/10.3390/smartcities3030043 - 07 Aug 2020
Abstract
The demand for renewable energy generation, especially photovoltaic (PV) power generation, has been growing over the past few years. However, the amount of generated energy by PV systems is highly dependent on weather conditions. Therefore, accurate forecasting of generated PV power is of [...] Read more.
The demand for renewable energy generation, especially photovoltaic (PV) power generation, has been growing over the past few years. However, the amount of generated energy by PV systems is highly dependent on weather conditions. Therefore, accurate forecasting of generated PV power is of importance for large-scale deployment of PV systems. Recently, machine learning (ML) methods have been widely used for PV power generation forecasting. A variety of these techniques, including artificial neural networks (ANNs), ridge regression, K-nearest neighbour (kNN) regression, decision trees, support vector regressions (SVRs) have been applied for this purpose and achieved good performance. In this paper, we briefly review the most recent ML techniques for PV energy generation forecasting and propose a new regression technique to automatically predict a PV system’s output based on historical input parameters. More specifically, the proposed loss function is a combination of three well-known loss functions: Correntropy, Absolute and Square Loss which encourages robustness and generalization jointly. We then integrate the proposed objective function into a Deep Learning model to predict a PV system’s output. By doing so, both the coefficients of loss functions and weight parameters of the ANN are learned jointly via back propagation. We investigate the effectiveness of the proposed method through comprehensive experiments on real data recorded by a real PV system. The experimental results confirm that our method outperforms the state-of-the-art ML methods for PV energy generation forecasting. Full article
(This article belongs to the Special Issue Feature Papers for Smart Cities)
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