Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Sensing Technology for Smart Cities: Data, Analytics, and Visualizations
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sensing Technology for Smart Cities: Data, Analytics, and Visualizations

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 15 July 2024 | Viewed by 3953

Special Issue Editors

Dr. Pierfrancesco Bellini

E-Mail Website
Guest Editor
Distributed Systems and Internet Tech Lab, DISIT Lab, Department of Information Engineering, University of Florence, DINFO, 50139 Firenze, Italy
Interests: smart cities; IoT/IoE architectures; big data; ontology design; knowledge graphs; RDF stores; linked data technologies; security & privacy
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Fanfani

E-Mail Website
Guest Editor
DISIT Lab, Department of Information Engineering, School of Engineering, University of Florence, Via Santa Marta 3, 50139 Firenze, Italy
Interests: smart cities; smart mobility; digital twins; IoT/IoE; ontologies; computer vision; 3D reconstruction
Special Issues, Collections and Topics in MDPI journals
Dr. Stefano Bilotta

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science “U. Dini”, University of Florence, DIMAI, 50134 Firenze, Italy
Interests: theory of codes; discrete mathematics; information theory; theoretical computer science; traffic flow algorithms; pollution models

Special Issue Information

Dear Colleagues,

Nowadays a huge portion of population lives in urban areas, and projections indicate that most cities are going to be confronted with a growing urban population in the next few years. This undoubtably poses new challenges that must be addressed by city councils and stakeholders to guarantee citizens’ high quality of life. Mobility, pollution, climate change, and waste management are only some of the problems that cities will face in the near future. In the context of smart cities, data produced in real-time by IoT/IoE sensors can be exploited for the development of innovative technologies based on data-driven approaches to monitor and analyze the status of the urban area, perform predictions and evaluations of specific scenarios, and give instruments to stakeholders to visualize and measure the impact of new policies aimed at promoting a green, sustainable, inclusive, and smart urban development.

This Special Issue therefore aims to put together original research and review articles on recent advances, technologies, solutions, and applications addressing urban development in the context of smart cities.

Potential topics include but are not limited to:

  • IoT/IoE sensors data acquisition and management;
  • Distributed sensing and computation;
  • IoT platforms;
  • Knowledge bases of urban data;
  • Data analytics for predictions and simulations;
  • Development of tools for dashboard construction;
  • Smart city digital twins;
  • Evaluation studies and datasets for smart cities;
  • Data security and privacy;
  • Smart mobility and transportation;
  • Environmental monitoring technologies;
  • Smart waste management.

Dr. Pierfrancesco Bellini
Dr. Marco Fanfani
Dr. Stefano Bilotta
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. Sensors 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 2600 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

  • IoT/IoE
  • big data
  • knowledge engineering
  • data analytics
  • dashboard
  • digital twin

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 4067 KiB  
Article
An Urban Intelligence Architecture for Heterogeneous Data and Application Integration, Deployment and Orchestration
by Stefano Silvestri, Giuseppe Tricomi, Salvatore Rosario Bassolillo, Riccardo De Benedictis and Mario Ciampi
Sensors 2024, 24(7), 2376; https://doi.org/10.3390/s24072376 - 08 Apr 2024
Viewed by 653
Abstract
This paper describes a novel architecture that aims to create a template for the implementation of an IT platform, supporting the deployment and integration of the different digital twin subsystems that compose a complex urban intelligence system. In more detail, the proposed Smart [...] Read more.
This paper describes a novel architecture that aims to create a template for the implementation of an IT platform, supporting the deployment and integration of the different digital twin subsystems that compose a complex urban intelligence system. In more detail, the proposed Smart City IT architecture has the following main purposes: (i) facilitating the deployment of the subsystems in a cloud environment; (ii) effectively storing, integrating, managing, and sharing the huge amount of heterogeneous data acquired and produced by each subsystem, using a data lake; (iii) supporting data exchange and sharing; (iv) managing and executing workflows, to automatically coordinate and run processes; and (v) to provide and visualize the required information. A prototype of the proposed IT solution was implemented leveraging open-source frameworks and technologies, to test its functionalities and performance. The results of the tests performed in real-world settings confirmed that the proposed architecture could efficiently and easily support the deployment and integration of heterogeneous subsystems, allowing them to share and integrate their data and to select, extract, and visualize the information required by a user, as well as promoting the integration with other external systems, and defining and executing workflows to orchestrate the various subsystems involved in complex analyses and processes. Full article
(This article belongs to the Special Issue Sensing Technology for Smart Cities: Data, Analytics, and Visualizations)
►▼ Show Figures

Figure 1

25 pages, 1795 KiB  
Article
Next Generation Computing and Communication Hub for First Responders in Smart Cities
by Olha Shaposhnyk, Kenneth Lai, Gregor Wolbring, Vlad Shmerko and Svetlana Yanushkevich
Sensors 2024, 24(7), 2366; https://doi.org/10.3390/s24072366 - 08 Apr 2024
Viewed by 506
Abstract
This paper contributes to the development of a Next Generation First Responder (NGFR) communication platform with the key goal of embedding it into a smart city technology infrastructure. The framework of this approach is a concept known as SmartHub, developed by the US [...] Read more.
This paper contributes to the development of a Next Generation First Responder (NGFR) communication platform with the key goal of embedding it into a smart city technology infrastructure. The framework of this approach is a concept known as SmartHub, developed by the US Department of Homeland Security. The proposed embedding methodology complies with the standard categories and indicators of smart city performance. This paper offers two practice-centered extensions of the NGFR hub, which are also the main results: first, a cognitive workload monitoring of first responders as a basis for their performance assessment, monitoring, and improvement; and second, a highly sensitive problem of human society, the emergency assistance tools for individuals with disabilities. Both extensions explore various technological-societal dimensions of smart cities, including interoperability, standardization, and accessibility to assistive technologies for people with disabilities. Regarding cognitive workload monitoring, the core result is a novel AI formalism, an ensemble of machine learning processes aggregated using machine reasoning. This ensemble enables predictive situation assessment and self-aware computing, which is the basis of the digital twin concept. We experimentally demonstrate a specific component of a digital twin of an NGFR, a near-real-time monitoring of the NGFR cognitive workload. Regarding our second result, a problem of emergency assistance for individuals with disabilities that originated as accessibility to assistive technologies to promote disability inclusion, we provide the NGFR specification focusing on interactions based on AI formalism and using a unified hub platform. This paper also discusses a technology roadmap using the notion of the Emergency Management Cycle (EMC), a commonly accepted doctrine for managing disasters through the steps of mitigation, preparedness, response, and recovery. It positions the NGFR hub as a benchmark of the smart city emergency service. Full article
(This article belongs to the Special Issue Sensing Technology for Smart Cities: Data, Analytics, and Visualizations)
►▼ Show Figures

Figure 1

23 pages, 5986 KiB  
Article
Smart City Scenario Editor for General What-If Analysis
by Lorenzo Adreani, Pierfrancesco Bellini, Stefano Bilotta, Daniele Bologna, Enrico Collini, Marco Fanfani and Paolo Nesi
Sensors 2024, 24(7), 2225; https://doi.org/10.3390/s24072225 - 30 Mar 2024
Viewed by 427
Abstract
Due to increasing urbanization, nowadays, cities are facing challenges spanning multiple domains such as mobility, energy, environment, etc. For example, to reduce traffic congestion, energy consumption, and excessive pollution, big data gathered from legacy systems (e.g., sensors not conformant with modern standards), geographic [...] Read more.
Due to increasing urbanization, nowadays, cities are facing challenges spanning multiple domains such as mobility, energy, environment, etc. For example, to reduce traffic congestion, energy consumption, and excessive pollution, big data gathered from legacy systems (e.g., sensors not conformant with modern standards), geographic information systems, gateways of public administrations, and Internet of Things technologies can be exploited to provide insights to assess the current status of a city. Moreover, the possibility to perform what-if analyses is fundamental to analyzing the impact of possible changes in the urban environment. The few available solutions for scenario definitions and analyses are limited to addressing a single domain and providing proprietary formats and tools, with scarce flexibility. Therefore, in this paper, we present a novel scenario model and editor integrated into the open-source Snap4City.org platform to enable several processing and what-if analyses in multiple domains. Different from state-of-the-art software, the proposed solution responds to a series of identified requirements, implements NGSIv2-compliant data models with formal descriptions of the urban context, and a scenario versioning method. Moreover, it allows us to carry out analyses on different domains, as shown with some examples. As a case study, a traffic congestion analysis is provided, confirming the validity and usefulness of the proposed solution. This work was developed in the context of CN MOST, the National Center on Sustainable Mobility in Italy, and for the Tourismo EC project. Full article
(This article belongs to the Special Issue Sensing Technology for Smart Cities: Data, Analytics, and Visualizations)
►▼ Show Figures

Figure 1

16 pages, 2248 KiB  
Article
Reshaping Smart Cities through NGSI-LD Enrichment
by Víctor González, Laura Martín, Juan Ramón Santana, Pablo Sotres, Jorge Lanza and Luis Sánchez
Sensors 2024, 24(6), 1858; https://doi.org/10.3390/s24061858 - 14 Mar 2024
Viewed by 581
Abstract
The vast amount of information stemming from the deployment of the Internet of Things and open data portals is poised to provide significant benefits for both the private and public sectors, such as the development of value-added services or an increase in the [...] Read more.
The vast amount of information stemming from the deployment of the Internet of Things and open data portals is poised to provide significant benefits for both the private and public sectors, such as the development of value-added services or an increase in the efficiency of public services. This is further enhanced due to the potential of semantic information models such as NGSI-LD, which enable the enrichment and linkage of semantic data, strengthened by the contextual information present by definition. In this scenario, advanced data processing techniques need to be defined and developed for the processing of harmonised datasets and data streams. Our work is based on a structured approach that leverages the principles of linked-data modelling and semantics, as well as a data enrichment toolchain framework developed around NGSI-LD. Within this framework, we reveal the potential for enrichment and linkage techniques to reshape how data are exploited in smart cities, with a particular focus on citizen-centred initiatives. Moreover, we showcase the effectiveness of these data processing techniques through specific examples of entity transformations. The findings, which focus on improving data comprehension and bolstering smart city advancements, set the stage for the future exploration and refinement of the symbiosis between semantic data and smart city ecosystems. Full article
(This article belongs to the Special Issue Sensing Technology for Smart Cities: Data, Analytics, and Visualizations)
►▼ Show Figures

Figure 1

19 pages, 592 KiB  
Article
Towards an AI-Driven Data Reduction Framework for Smart City Applications
by Laercio Pioli, Douglas D. J. de Macedo, Daniel G. Costa and Mario A. R. Dantas
Sensors 2024, 24(2), 358; https://doi.org/10.3390/s24020358 - 07 Jan 2024
Cited by 1 | Viewed by 1269
Abstract
The accelerated development of technologies within the Internet of Things landscape has led to an exponential boost in the volume of heterogeneous data generated by interconnected sensors, particularly in scenarios with multiple data sources as in smart cities. Transferring, processing, and storing a [...] Read more.
The accelerated development of technologies within the Internet of Things landscape has led to an exponential boost in the volume of heterogeneous data generated by interconnected sensors, particularly in scenarios with multiple data sources as in smart cities. Transferring, processing, and storing a vast amount of sensed data poses significant challenges for Internet of Things systems. In this sense, data reduction techniques based on artificial intelligence have emerged as promising solutions to address these challenges, alleviating the burden on the required storage, bandwidth, and computational resources. This article proposes a framework that exploits the concept of data reduction to decrease the amount of heterogeneous data in certain applications. A machine learning model that predicts a distortion rate and its corresponding reduction rate of the imputed data is also proposed, which uses the predicted values to select, among many reduction techniques, the most suitable approach. To support such a decision, the model also considers the context of the data producer that dictates the class of reduction algorithm that is allowed to be applied to the input stream. The achieved results indicate that the Huffman algorithm performed better considering the reduction of time-series data, with significant potential applications for smart city scenarios. Full article
(This article belongs to the Special Issue Sensing Technology for Smart Cities: Data, Analytics, and Visualizations)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop