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

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

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 16591

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Special Issue Editors

Dr. Pierfrancesco Bellini

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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

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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

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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

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Published Papers (12 papers)

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Research

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20 pages, 6747 KiB

Open AccessArticle

Innovative Air-Preconditioning Method for Accurate Particulate Matter Sensing in Humid Environments

by Zdravko Kunić, Leo Mršić, Goran Đambić and Tomislav Ražov

Sensors 2024, 24(17), 5477; https://doi.org/10.3390/s24175477 - 23 Aug 2024

Viewed by 727

Abstract

Smart cities rely on a network of sensors to gather real-time data on various environmental factors, including air quality. This paper addresses the challenges of improving the accuracy of low-cost particulate matter sensors (LCPMSs) which can be compromised by environmental conditions, such as high humidity, which is common in many urban areas. Such weather conditions often lead to the overestimation of particle counts due to hygroscopic particle growth, resulting in a potential public concern, although most of the detected particles consist of just water. The paper presents an innovative design for an indicative air-quality measuring station that integrates the particulate matter sensor with a preconditioning subsystem designed to mitigate the impact of humidity. The preconditioning subsystem works by heating the incoming air, effectively reducing the relative humidity and preventing the hygroscopic growth of particles before they reach the sensor. To validate the effectiveness of this approach, parallel measurements were conducted using both preconditioned and non-preconditioned sensors over a period of 19 weeks. The data were analyzed to compare the performance of the sensors in terms of accuracy for PM₁, PM_2.5, and PM₁₀ particles. The results demonstrated a significant improvement in measurement accuracy for the preconditioned sensor, especially in environments with high relative humidity. When the conditions were too severe and both sensors started measuring incorrect values, the preconditioned sensor-measured values were closer to the actual values. Also, the period of measuring incorrect values was shorter with the preconditioned sensor. The results suggest that the implementation of air preconditioning subsystems in LCPMSs deployed in smart cities can provide a cost-effective solution to overcome humidity-related inaccuracies, thereby improving the overall quality of measured air pollution data. Full article

(This article belongs to the Special Issue Sensing Technology for Smart Cities: Data, Analytics, and Visualizations)

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27 pages, 56161 KiB

Open AccessArticle

Locating Insulation Defects in HV Substations Using HFCT Sensors and AI Diagnostic Tools

by Javier Ortego, Fernando Garnacho, Fernando Álvarez, Eduardo Arcones and Abderrahim Khamlichi

Sensors 2024, 24(16), 5312; https://doi.org/10.3390/s24165312 - 16 Aug 2024

Viewed by 820

Abstract

In general, a high voltage (HV) substation can be made up of multiple insulation subsystems: an air insulation subsystem (AIS), gas insulation subsystem (GIS), liquid insulation subsystem (power transformers), and solid insulation subsystem (power cables), all of them with their grounding structures interconnected and linked to the substation earth. Partial discharge (PD) pulses, which are generated in a HV apparatus belonging to a subsystem, travel through the grounding structures of the others. PD analyzers using high-frequency current transformer (HFCT) sensors, which are installed at the connections between the grounding structures, are sensitive to these traveling pulses. In a substation made up of an AIS, several non-critical PD sources can be detected, such as possible corona, air surface, or floating discharges. To perform the correct diagnosis, non-critical PD sources must be separated from critical PD sources related to insulation defects, such as a cavity in a solid dielectric material, mobile particles in SF6, or surface discharges in oil. Powerful diagnostic tools using PD clustering and phase-resolved PD (PRPD) pattern recognition have been developed to check the insulation condition of HV substations. However, a common issue is how to determine the subsystem in which a critical PD source is located when there are several PD sources, and a critical one is near the boundary between two HV subsystems, e.g., a cavity defect located between a cable end and a GIS. The traveling direction of the detected PD is valuable information to determine the subsystem in which the insulation defect is located. However, incorrect diagnostics are usually due to the constraints of PD measuring systems and inadequate PD diagnostic procedures. This paper presents a diagnostic procedure using an appropriate PD analyzer with multiple HFCT sensors to carry out efficient insulation condition diagnoses. This PD procedure has been developed on the basis of laboratory tests, transient signal modeling, and validation tests. The validation tests were carried out in a special test bench developed for the characterization of PD analyzers. To demonstrate the effectiveness of the procedure, a real case is also presented, where satisfactory results are shown. Full article

(This article belongs to the Special Issue Sensing Technology for Smart Cities: Data, Analytics, and Visualizations)

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23 pages, 4142 KiB

Open AccessArticle

Data Analytics for Predicting Situational Developments in Smart Cities: Assessing User Perceptions

by Alexander A. Kharlamov and Maria Pilgun

Sensors 2024, 24(15), 4810; https://doi.org/10.3390/s24154810 - 24 Jul 2024

Viewed by 729

Abstract

The analysis of large volumes of data collected from heterogeneous sources is increasingly important for the development of megacities, the advancement of smart city technologies, and ensuring a high quality of life for citizens. This study aimed to develop algorithms for analyzing and interpreting social media data to assess citizens’ opinions in real time and for verifying and examining data to analyze social tension and predict the development of situations during the implementation of urban projects. The developed algorithms were tested using an urban project in the field of transportation system development. The study’s material included data from social networks, messenger channels and chats, video hosting platforms, blogs, microblogs, forums, and review sites. An interdisciplinary approach was utilized to analyze the data, employing tools such as Brand Analytics, TextAnalyst 2.32, GPT-3.5, GPT-4, GPT-4o, and Tableau. The results of the data analysis showed identical outcomes, indicating a neutral perception among users and the absence of social tension surrounding the project’s implementation, allowing for the prediction of a calm development of the situation. Additionally, recommendations were developed to avert potential conflicts and eliminate sources of social tension for decision-making purposes. Full article

(This article belongs to the Special Issue Sensing Technology for Smart Cities: Data, Analytics, and Visualizations)

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18 pages, 11573 KiB

Open AccessArticle

Design and Implementation of a Two-Wheeled Vehicle Safe Driving Evaluation System

by Dongbeom Kim, Hyemin Kim, Suyun Lee, Qyoung Lee, Minwoo Lee, Jooyoung Lee and Chulmin Jun

Sensors 2024, 24(14), 4739; https://doi.org/10.3390/s24144739 - 22 Jul 2024

Viewed by 917

Abstract

The delivery market in Republic of Korea has experienced significant growth, leading to a surge in motorcycle-related accidents. However, there is a lack of comprehensive data collection systems for motorcycle safety management. This study focused on designing and implementing a foundational data collection system to monitor and evaluate motorcycle driving behavior. To achieve this, eleven risky behaviors were defined, identified using image-based, GIS-based, and inertial-sensor-based methods. A motorcycle-mounted sensing device was installed to assess driving, with drivers reviewing their patterns through an app and all data monitored via a web interface. The system was applied and tested using a testbed. This study is significant as it successfully conducted foundational data collection for motorcycle safety management and designed and implemented a system for monitoring and evaluation. Full article

(This article belongs to the Special Issue Sensing Technology for Smart Cities: Data, Analytics, and Visualizations)

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17 pages, 4432 KiB

Open AccessArticle

Towards Reliability in Smart Water Sensing Technology: Evaluating Classical Machine Learning Models for Outlier Detection

by Mimoun Lamrini, Bilal Ben Mahria, Mohamed Yassin Chkouri and Abdellah Touhafi

Sensors 2024, 24(13), 4084; https://doi.org/10.3390/s24134084 - 24 Jun 2024

Cited by 1 | Viewed by 928

Abstract

In recent years, smart water sensing technology has played a crucial role in water management, addressing the pressing need for efficient monitoring and control of water resources analysis. The challenge in smart water sensing technology resides in ensuring the reliability and accuracy of the data collected by sensors. Outliers are a well-known problem in smart sensing as they can negatively affect the viability of useful analysis and make it difficult to evaluate pertinent data. In this study, we evaluate the performance of four sensors: electrical conductivity (EC), dissolved oxygen (DO), temperature (Temp), and pH. We implement four classical machine learning models: support vector machine (SVM), artifical neural network (ANN), decision tree (DT), and isolated forest (iForest)-based outlier detection as a pre-processing step before visualizing the data. The dataset was collected by a real-time smart water sensing monitoring system installed in Brussels’ lakes, rivers, and ponds. The obtained results clearly show that the SVM outperforms the other models, showing 98.38%

F_{1}

-score rates for pH, 96.98%

F_{1}

-score rates for temp, 97.88%

F_{1}

-score rates for DO, and 98.11%

F_{1}

-score rates for EC. Furthermore, ANN also achieves a significant results, establishing it as a viable alternative. Full article

(This article belongs to the Special Issue Sensing Technology for Smart Cities: Data, Analytics, and Visualizations)

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23 pages, 10026 KiB

Open AccessArticle

Smart City as Cooperating Smart Areas: On the Way of Symbiotic Cyber–Physical Systems Environment

by Giuseppe Tricomi, Maurizio Giacobbe, Ilenia Ficili, Nicola Peditto and Antonio Puliafito

Sensors 2024, 24(10), 3108; https://doi.org/10.3390/s24103108 - 14 May 2024

Viewed by 2143

Abstract

The arising of the Cyber–Physical Systems’ vision and concepts drives technological evolution toward a new architectural design for the infrastructure of an environment referred to as a Smart Environment. This perspective alters the way systems within Smart City landscapes are conceived, designed, and ultimately realized. Modular architecture, resource-sharing techniques, and precise deployment approaches (such as microservices-oriented or reliant on the FaaS paradigm) serve as the cornerstones of a Smart City cognizant of multiple Cyber–Physical Systems composing it. This paper presents a framework integrating Digital Decisioning, encompassing the automated combination of human-derived knowledge and data-derived knowledge (e.g., business rules and machine learning), to enhance decision-making processes and application definition within the Smart City context. Full article

(This article belongs to the Special Issue Sensing Technology for Smart Cities: Data, Analytics, and Visualizations)

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22 pages, 4067 KiB

Open AccessArticle

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 - 8 Apr 2024

Cited by 5 | Viewed by 1630

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 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)

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25 pages, 1795 KiB

Open AccessArticle

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 - 8 Apr 2024

Cited by 1 | Viewed by 1473

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 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)

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23 pages, 5986 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1148

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 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)

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16 pages, 2248 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1254

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 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)

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19 pages, 592 KiB

Open AccessArticle

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 - 7 Jan 2024

Cited by 2 | Viewed by 1960

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 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)

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Review

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34 pages, 525 KiB

Open AccessReview

A Review of Recent Hardware and Software Advances in GPU-Accelerated Edge-Computing Single-Board Computers (SBCs) for Computer Vision

by Umair Iqbal, Tim Davies and Pascal Perez

Sensors 2024, 24(15), 4830; https://doi.org/10.3390/s24154830 - 25 Jul 2024

Cited by 1 | Viewed by 1876

Abstract

Computer Vision (CV) has become increasingly important for Single-Board Computers (SBCs) due to their widespread deployment in addressing real-world problems. Specifically, in the context of smart cities, there is an emerging trend of developing end-to-end video analytics solutions designed to address urban challenges such as traffic management, disaster response, and waste management. However, deploying CV solutions on SBCs presents several pressing challenges (e.g., limited computation power, inefficient energy management, and real-time processing needs) hindering their use at scale. Graphical Processing Units (GPUs) and software-level developments have emerged recently in addressing these challenges to enable the elevated performance of SBCs; however, it is still an active area of research. There is a gap in the literature for a comprehensive review of such recent and rapidly evolving advancements on both software and hardware fronts. The presented review provides a detailed overview of the existing GPU-accelerated edge-computing SBCs and software advancements including algorithm optimization techniques, packages, development frameworks, and hardware deployment specific packages. This review provides a subjective comparative analysis based on critical factors to help applied Artificial Intelligence (AI) researchers in demonstrating the existing state of the art and selecting the best suited combinations for their specific use-case. At the end, the paper also discusses potential limitations of the existing SBCs and highlights the future research directions in this domain. Full article

(This article belongs to the Special Issue Sensing Technology for Smart Cities: Data, Analytics, and Visualizations)

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