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IoT and Big Data Analytics for Smart Cities
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IoT and Big Data Analytics for Smart Cities

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

Deadline for manuscript submissions: 25 March 2026 | Viewed by 8213

Special Issue Editors

Prof. Dr. Adela Bara

E-Mail Website
Guest Editor
Department of Economic Informatics and Cybernetics, University of Economic Studies, 010374 Bucharest, Romania
Interests: database systems; big data; machine learning; decision support systems; energy management systems; IoT; digital twins
Special Issues, Collections and Topics in MDPI journals
Dr. Simona-Vasilica Oprea

E-Mail Website
Guest Editor
Department of Economic Informatics and Cybernetics, University of Economic Studies, 010374 Bucharest, Romania
Interests: IoT; database systems; big data; deep learning; natural language processing; energy management systems; digital twins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart cities involve real-time communication between sensors and the Internet of Things (IoT), as well as the integration, processing, and analysis of large volumes of data collected from heterogenous sources that are referred to big data. Recent advances in IoT and big data contribute to the design of new models and development of innovative solutions for smart cities that can bring benefits to communities, environment, urban authorities, and industry. Even so, there are still many research gaps to be identified and solved to help urban communities in their transition towards a more green and sustainable development. This Special Issue aims to provide opportunities for researchers and practitioners to publish innovative and original papers that explore the challenges related to IoT and big data processing, integration, and analytics for smart cities. Both theoretical and practical approaches are welcome. Potential topics include, but are not limited to, the following: communication-based frameworks for IoT networks; data-driven architectures and models for real time data processing; IoT data integration; edge-fog-cloud computing for smart cities; automation and optimization models for smart cities; AI-based data processing; machine learning (ML) and deep learning (DL) for IoT networks and big data related to smart cities; natural language processing (NLP) for big data analytics; digital threads and digital twins for smart cities; and advanced analytics for smart cities.

Prof. Dr. Adela Bara
Dr. Simona-Vasilica Oprea
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

  • smart cities
  • IoT networks
  • big data processing
  • big data analytics
  • edge-fog-cloud computing
  • AI-based data processing
  • digital twins

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Published Papers (6 papers)

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Research

24 pages, 7548 KiB  
Article
Quantifying and Forecasting Emission Reductions in Urban Mobility: An IoT-Driven Bike-Sharing Analysis
by Manuel Uche-Soria, Bernardo Tabuenca, Gonzalo Halcón-Gibert and Yilsy Núñez-Guerrero
Sensors 2025, 25(7), 2163; https://doi.org/10.3390/s25072163 - 28 Mar 2025
Viewed by 315
Abstract
The growing urgency to address urban air quality and climate change has intensified the need for sustainable mobility solutions that mitigate vehicular emissions. Bike-sharing systems (BSSs) represent a viable alternative; however, their precise environmental impact remains insufficiently explored. This study quantifies and forecasts [...] Read more.
The growing urgency to address urban air quality and climate change has intensified the need for sustainable mobility solutions that mitigate vehicular emissions. Bike-sharing systems (BSSs) represent a viable alternative; however, their precise environmental impact remains insufficiently explored. This study quantifies and forecasts reductions in CO2 and NOx emissions resulting from BSS usage in Madrid by integrating real-time IoT sensor data with an advanced predictive model. The proposed framework effectively captures nonlinear and seasonal mobility and emission patterns, achieving high predictive accuracy while demonstrating significant energy savings. These findings confirm the environmental benefits of BSSs and provide urban planners and policymakers with a robust tool to extend and replicate this analysis in other cities, fostering sustainable urban mobility and improved air quality. Full article
(This article belongs to the Special Issue IoT and Big Data Analytics for Smart Cities)
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18 pages, 2863 KiB  
Article
Cooperative Intelligent Transport Systems: The Impact of C-V2X Communication Technologies on Road Safety and Traffic Efficiency
by Jingwen Wang, Ivan Topilin, Anastasia Feofilova, Mengru Shao and Yadong Wang
Sensors 2025, 25(7), 2132; https://doi.org/10.3390/s25072132 - 27 Mar 2025
Viewed by 363
Abstract
The advancement of intelligent road transport represents a promising direction in the evolution of transportation systems, aimed at improving road safety and reducing traffic accidents. The integration of artificial intelligence, sensors, and machine vision systems enables autonomous vehicles (AVs) to rapidly adapt to [...] Read more.
The advancement of intelligent road transport represents a promising direction in the evolution of transportation systems, aimed at improving road safety and reducing traffic accidents. The integration of artificial intelligence, sensors, and machine vision systems enables autonomous vehicles (AVs) to rapidly adapt to changes in the road environment, minimizing human error and significantly reducing collision risks. These technologies provide continuous and highly precise control, including adaptive acceleration, braking, and maneuvering, thereby enhancing overall road safety. Connected vehicles utilizing C-V2X (Cellular Vehicle-to-Everything) communication primarily feature real-time operation, safety, and stability. However, communication flaws, such as signal fading, time delays, packet loss, and malicious network attacks, can affect vehicle-to-vehicle interactions in cooperative intelligent transport systems (C-ITSs). This study explores how C-V2X technology, compared to traditional DSRC, improves communication latency and enhances vehicle communication efficiency. Using SUMO simulations, various traffic scenarios were modeled with different autonomous vehicle penetration rates and communication technologies, focusing on traffic conflict rates, travel time, and communication performance. The results demonstrated that C-V2X reduced latency by over 99% compared to DSRC, facilitating faster communication between vehicles and contributing to a 38% reduction in traffic conflicts at 60% AV penetration. Traffic flow and safety improved with increased AV penetration, particularly in congested conditions. While C-V2X offers substantial benefits, challenges such as data packet loss, communication delays, and security vulnerabilities must be addressed to fully realize its potential. Future advancements in 5G and subsequent wireless communication technologies are expected to further reduce latency and enhance the effectiveness of C-ITSs. This study underscores the potential of C-V2X to enhance collision avoidance, alleviate congestion, and improve traffic management, while also contributing to the development of more reliable and efficient transportation systems. The continued refinement of simulation models and collaboration among stakeholders will be crucial to addressing the challenges in CAV integration and realizing the full benefits of connected transportation systems in smart cities. Full article
(This article belongs to the Special Issue IoT and Big Data Analytics for Smart Cities)
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38 pages, 8935 KiB  
Article
Intersections of Big Data and IoT in Academic Publications: A Topic Modeling Approach
by Diana-Andreea Căuniac, Andreea-Alexandra Cîrnaru, Simona-Vasilica Oprea and Adela Bâra
Sensors 2025, 25(3), 906; https://doi.org/10.3390/s25030906 - 2 Feb 2025
Cited by 1 | Viewed by 1173
Abstract
As vast amounts of data are generated from various sources such as social media, sensors and online transactions, the analysis of Big Data offers organizations the ability to derive insights and make informed decisions. Simultaneously, IoT connects physical devices, enabling real-time data collection [...] Read more.
As vast amounts of data are generated from various sources such as social media, sensors and online transactions, the analysis of Big Data offers organizations the ability to derive insights and make informed decisions. Simultaneously, IoT connects physical devices, enabling real-time data collection and exchange that transforms interactions within smart homes, cities and industries. The intersection of these fields is essential, leading to innovations such as predictive maintenance, real-time traffic management and personalized solutions. Utilizing a dataset of 8159 publications sourced from the Web of Science database, our research employs Natural Language Processing (NLP) techniques and selective human validation to analyze abstracts, titles, keywords and other useful information, uncovering key themes and trends in both Big Data and IoT research. Six topics are extracted using Latent Dirichlet Allocation. In Topic 1, words like “system” and “energy” are among the most frequent, signaling that Topic 1 revolves around data systems and IoT technologies, likely in the context of smart systems and energy-related applications. Topic 2 focuses on the application of technologies, as indicated by terms such as “technologies”, “industry” and “research”. It deals with how IoT and related technologies are transforming various industries. Topic 3 emphasizes terms like learning and research, indicating a focus on machine learning and IoT applications. It is oriented toward research involving new methods and models in the IoT domain related to learning algorithms. Topic 4 highlights terms such as smart, suggesting a focus on smart technologies and systems. Topic 5 touches upon the role of digital chains and supply systems, suggesting an industrial focus on digital transformation. Topic 6 focuses on technical aspects such as modeling, system performance and prediction algorithms. It delves into the efficiency of IoT networks with terms like “accuracy”, “power” and “performance” standing out. Full article
(This article belongs to the Special Issue IoT and Big Data Analytics for Smart Cities)
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31 pages, 11682 KiB  
Article
Comparative Study of Time Series Analysis Algorithms Suitable for Short-Term Forecasting in Implementing Demand Response Based on AMI
by Myung-Joo Park and Hyo-Sik Yang
Sensors 2024, 24(22), 7205; https://doi.org/10.3390/s24227205 - 11 Nov 2024
Cited by 1 | Viewed by 1698
Abstract
This paper compares four time series forecasting algorithms—ARIMA, SARIMA, LSTM, and SVM—suitable for short-term load forecasting using Advanced Metering Infrastructure (AMI) data. The primary focus is on evaluating the applicability and performance of these forecasting models in predicting electricity consumption patterns, which is [...] Read more.
This paper compares four time series forecasting algorithms—ARIMA, SARIMA, LSTM, and SVM—suitable for short-term load forecasting using Advanced Metering Infrastructure (AMI) data. The primary focus is on evaluating the applicability and performance of these forecasting models in predicting electricity consumption patterns, which is a critical component for implementing effective demand response (DR) strategies. The study provides a comprehensive analysis of the predictive accuracy, computational efficiency, and scalability of each algorithm using a dataset of real-time electricity consumption collected from AMI systems over a designated period. Through extensive experiments, we demonstrate that each algorithm has distinct strengths and weaknesses depending on the characteristics of the dataset. Specifically, SVM exhibited superior performance in handling nonlinear patterns and high volatility, while SARIMA effectively captured seasonal trends. LSTM showed potential in modeling complex temporal dependencies but was sensitive to hyperparameter settings and required a substantial amount of training data. This research offers practical guidelines for selecting the optimal forecasting model based on data characteristics and application needs, contributing to the development of more efficient and dynamic energy management strategies. The findings highlight the importance of integrating advanced forecasting techniques into smart grid systems to enhance the reliability and responsiveness of DR programs. This study lays a solid foundation for future research on integrating these forecasting models into real-world AMI applications to support effective demand response and grid stability. Full article
(This article belongs to the Special Issue IoT and Big Data Analytics for Smart Cities)
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36 pages, 2362 KiB  
Article
A Predictive Model for Benchmarking the Performance of Algorithms for Fake and Counterfeit News Classification in Global Networks
by Nureni Ayofe Azeez, Sanjay Misra, Davidson Onyinye Ogaraku and Ademola Philip Abidoye
Sensors 2024, 24(17), 5817; https://doi.org/10.3390/s24175817 - 7 Sep 2024
Viewed by 1532
Abstract
The pervasive spread of fake news in online social media has emerged as a critical threat to societal integrity and democratic processes. To address this pressing issue, this research harnesses the power of supervised AI algorithms aimed at classifying fake news with selected [...] Read more.
The pervasive spread of fake news in online social media has emerged as a critical threat to societal integrity and democratic processes. To address this pressing issue, this research harnesses the power of supervised AI algorithms aimed at classifying fake news with selected algorithms. Algorithms such as Passive Aggressive Classifier, perceptron, and decision stump undergo meticulous refinement for text classification tasks, leveraging 29 models trained on diverse social media datasets. Sensors can be utilized for data collection. Data preprocessing involves rigorous cleansing and feature vector generation using TF-IDF and Count Vectorizers. The models’ efficacy in classifying genuine news from falsified or exaggerated content is evaluated using metrics like accuracy, precision, recall, and more. In order to obtain the best-performing algorithm from each of the datasets, a predictive model was developed, through which SG with 0.681190 performs best in Dataset 1, BernoulliRBM has 0.933789 in Dataset 2, LinearSVC has 0.689180 in Dataset 3, and BernoulliRBM has 0.026346 in Dataset 4. This research illuminates strategies for classifying fake news, offering potential solutions to ensure information integrity and democratic discourse, thus carrying profound implications for academia and real-world applications. This work also suggests the strength of sensors for data collection in IoT environments, big data analytics for smart cities, and sensor applications which contribute to maintaining the integrity of information within urban environments. Full article
(This article belongs to the Special Issue IoT and Big Data Analytics for Smart Cities)
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21 pages, 927 KiB  
Article
A Recommendation System for Prosumers Based on Large Language Models
by Simona-Vasilica Oprea and Adela Bâra
Sensors 2024, 24(11), 3530; https://doi.org/10.3390/s24113530 - 30 May 2024
Cited by 3 | Viewed by 1689
Abstract
As modern technologies, particularly home assistant devices and sensors, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information at 15-min [...] Read more.
As modern technologies, particularly home assistant devices and sensors, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information at 15-min or even 5-min intervals, including weather forecasts, outputs from renewable energy source (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the local energy market (LEM). The goal for these prosumers is to reduce costs while ensuring their home’s comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by large language models (LLMs), Scikit-llm and zero-shot classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy. A comparison with a content-based filtering system is provided considering the performance metrics that are relevant for prosumers. Full article
(This article belongs to the Special Issue IoT and Big Data Analytics for Smart Cities)
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