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Artificial Intelligence and Sensors Technology in Smart Cities
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Artificial Intelligence and Sensors Technology in Smart Cities

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

Deadline for manuscript submissions: 25 July 2025 | Viewed by 24010

Special Issue Editor

Dr. Md Palash Uddin

E-Mail Website
Guest Editor
School of Information Technology, Deakin University, Geelong, VIC 3220, Australia
Interests: machine learning; federated learning; blockchain; remote sensing image analysis

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for papers for a Special Issue of our esteemed journal that will focus on the intersection of artificial intelligence (AI) and sensors technology in the context of smart cities. As the world rapidly urbanizes, cities face significant challenges in terms of managing resources, enhancing efficiency, and improving the quality of life for their residents. This Special Issue aims to explore the potential of AI and sensor technologies in addressing these challenges and creating sustainable, intelligent urban environments.

This Special Issue aims to bring together cutting-edge research and innovative solutions that demonstrate the application of AI and sensors technology in various aspects of smart cities. Contributions are invited from researchers, practitioners, and industry experts to showcase their work, share insights, and foster a deeper understanding of the potential of these technologies. The key objectives of this Special Issue include:

  • Exploring the integration of AI algorithms and techniques with sensor networks in the context of smart cities.
  • Investigating the role of AI in data analysis, processing, and interpretation from sensor networks.
  • Examining the use of AI and sensor technologies for real-time monitoring, prediction, and decision-making in smart city systems.
  • Addressing the challenges and opportunities in implementing AI and sensor-based solutions for urban infrastructure, transportation, energy management, public safety, and environmental monitoring.
  • Evaluating the impact of AI and sensor technologies on citizen engagement, inclusivity, and overall quality of life in smart cities.
  • Discussing ethical considerations, privacy concerns, and legal frameworks related to the use of AI and sensor technologies in urban environments.

We invite submissions of original research papers, case studies, review articles, and survey papers on various topics related to AI and sensors technology in smart cities, including but not limited to:

  • AI-driven sensor networks for urban monitoring and control.
  • Machine learning algorithms for sensor data analysis in smart cities.
  • Edge machine learning, e.g., federated learning for smart cities applications
  • Intelligent transportation systems and traffic management using sensors and AI.
  • Energy-efficient buildings and smart grids enabled by AI and sensors.
  • Sensor-based environmental monitoring and pollution control in urban areas.
  • Public safety and emergency response systems employing AI and sensor technologies.
  • Citizen engagement platforms and participatory sensing in smart cities.
  • Privacy-preserving approaches and data governance in AI and sensor deployments.
  • Socio-economic impacts of AI and sensors in shaping future cities.
  • Smart governance and policymaking with AI and sensor-based solutions.

Dr. Md Palash Uddin
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 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

  • artificial intelligence
  • sensors technology
  • smart cities
  • intelligent transportation
  • edge intelligence
  • intelligent sensors

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

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Research

Jump to: Review

19 pages, 17708 KiB  
Article
A Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European Cities
by Yiannis Kiouvrekis, Ioannis Givisis, Theodor Panagiotakopoulos, Ioannis Tsilikas, Agapi Ploussi, Ellas Spyratou and Efstathios P. Efstathopoulos
Sensors 2025, 25(1), 53; https://doi.org/10.3390/s25010053 - 25 Dec 2024
Cited by 1 | Viewed by 1483
Abstract
The widespread propagation of wireless communication devices, from smartphones and tablets to Internet of Things (IoT) systems, has become an integral part of modern life. However, the expansion of wireless technology has also raised public concern about the potential health risks associated with [...] Read more.
The widespread propagation of wireless communication devices, from smartphones and tablets to Internet of Things (IoT) systems, has become an integral part of modern life. However, the expansion of wireless technology has also raised public concern about the potential health risks associated with prolonged exposure to electromagnetic fields. Our objective is to determine the optimal machine learning model for constructing electric field strength maps across urban areas, enhancing the field of environmental monitoring with the aid of sensor-based data collection. Our machine learning models consist of a novel and comprehensive dataset collected from a network of strategically placed sensors, capturing not only electromagnetic field readings but also additional urban features, including population density, levels of urbanization, and specific building characteristics. This sensor-driven approach, coupled with explainable AI, enables us to identify key factors influencing electromagnetic exposure more accurately. The integration of IoT sensor data with machine learning opens the potential for creating highly detailed and dynamic electromagnetic pollution maps. These maps are not merely static snapshots; they offer researchers the ability to track trends over time, assess the effectiveness of mitigation efforts, and gain a deeper understanding of electromagnetic field distribution in urban environments. Through the extensive dataset, our models can yield highly accurate and dynamic electric field strength maps. For this study, we performed a comprehensive analysis involving 566 machine learning models across eight French cities: Lyon, Saint-Étienne, Clermont-Ferrand, Dijon, Nantes, Rouen, Lille, and Paris. The analysis incorporated six core approaches: k-Nearest Neighbors, XGBoost, Random Forest, Neural Networks, Decision Trees, and Linear Regression. The findings underscore the superior predictive capabilities of ensemble methods such as Random Forests and XGBoost, which outperform individual models. Simpler approaches like Decision Trees and k-NN offer effective yet slightly less precise alternatives. Neural Networks, despite their complexity, highlight the potential for further refinement in this application. In addition, our results show that the machine learning models significantly outperform the linear regression baseline, demonstrating the added value of more complex techniques in this domain. Our SHAP analysis reveals that the feature importance rankings in tree-based machine learning models differ significantly from those in k-NN, neural network, and linear regression models. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors Technology in Smart Cities)
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16 pages, 1291 KiB  
Article
Silent Speech Eyewear Interface: Silent Speech Recognition Method Using Eyewear and an Ear-Mounted Microphone with Infrared Distance Sensors
by Yuya Igarashi, Kyosuke Futami and Kazuya Murao
Sensors 2024, 24(22), 7368; https://doi.org/10.3390/s24227368 - 19 Nov 2024
Viewed by 969
Abstract
As eyewear devices such as smart glasses become more common, it is important to provide input methods that can be used at all times for such situations and people. Silent speech interaction (SSI) has the potential to be useful as a hands-free input [...] Read more.
As eyewear devices such as smart glasses become more common, it is important to provide input methods that can be used at all times for such situations and people. Silent speech interaction (SSI) has the potential to be useful as a hands-free input method for various situations and people, including those who have difficulty with voiced speech. However, previous methods have involved sensor devices that are difficult to use anytime and anywhere. We propose a method for SSI that involves using an eyewear device equipped with infrared distance sensors. The proposed method measures facial skin movements associated with speech from the infrared distance sensor mounted on an eyewear device and recognizes silent speech commands by applying machine learning to time series sensor data. The proposed method was applied to a prototype system including a sensor device consisting of eyewear and ear-mounted microphones to measure the movements of the cheek, jaw joint, and jaw. Evaluations 1 and 2 showed that five speech commands could be recognized with an F value of 0.90 and ten longer speech commands with an F value of 0.83. Evaluation 3 showed how the recognition accuracy changes with the combination of sensor points. Evaluation 4 examined whether the proposed method can be used for a larger number of speech commands with 21 commands by using deep learning LSTM and a combination of DTW and kNN. Evaluation 5 examined the recognition accuracy in some situations affecting recognition accuracy such as re-attaching devices and walking. These results show the feasibility of the proposed method for a simple hands-free input interface, such as with media players and voice assistants. Our study provides the first wearable sensing method that can easily apply SSI functions to eyewear devices. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors Technology in Smart Cities)
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25 pages, 4024 KiB  
Article
A Novel Hybrid XAI Solution for Autonomous Vehicles: Real-Time Interpretability Through LIME–SHAP Integration
by H. Ahmed Tahir, Walaa Alayed, Waqar Ul Hassan and Amir Haider
Sensors 2024, 24(21), 6776; https://doi.org/10.3390/s24216776 - 22 Oct 2024
Cited by 2 | Viewed by 3004
Abstract
The rapid advancement in self-driving and autonomous vehicles (AVs) integrated with artificial intelligence (AI) technology demands not only precision but also output transparency. In this paper, we propose a novel hybrid explainable AI (XAI) framework that combines local interpretable model-agnostic explanations (LIME) and [...] Read more.
The rapid advancement in self-driving and autonomous vehicles (AVs) integrated with artificial intelligence (AI) technology demands not only precision but also output transparency. In this paper, we propose a novel hybrid explainable AI (XAI) framework that combines local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP). Our framework combines the precision and globality of SHAP and low computational requirements of LIME, creating a balanced approach for onboard deployment with enhanced transparency. We evaluate the proposed framework on three different state-of-the-art models: ResNet-18, ResNet-50, and SegNet-50 on the KITTI dataset. The results demonstrate that our hybrid approach consistently outperforms traditional approaches by achieving a fidelity rate of more than 85%, interpretability factor of more than 80%, and consistency of more than 70%, surpassing the conventional methods. Furthermore, the inference time of our proposed framework with ResNet-18 was 0.28 s; for ResNet-50, it was 0.571 s; and that for SegNet was 3.889 s with XAI layers. This is optimal for onboard computations and deployment. This research establishes a strong foundation for the deployment of XAI in safety-critical AV with balanced tradeoffs for real-time decision-making. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors Technology in Smart Cities)
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24 pages, 5627 KiB  
Article
Proactive Threat Hunting in Critical Infrastructure Protection through Hybrid Machine Learning Algorithm Application
by Ali Shan and Seunghwan Myeong
Sensors 2024, 24(15), 4888; https://doi.org/10.3390/s24154888 - 27 Jul 2024
Cited by 3 | Viewed by 2774
Abstract
Cyber-security challenges are growing globally and are specifically targeting critical infrastructure. Conventional countermeasure practices are insufficient to provide proactive threat hunting. In this study, random forest (RF), support vector machine (SVM), multi-layer perceptron (MLP), AdaBoost, and hybrid models were applied for proactive threat [...] Read more.
Cyber-security challenges are growing globally and are specifically targeting critical infrastructure. Conventional countermeasure practices are insufficient to provide proactive threat hunting. In this study, random forest (RF), support vector machine (SVM), multi-layer perceptron (MLP), AdaBoost, and hybrid models were applied for proactive threat hunting. By automating detection, the hybrid machine learning-based method improves threat hunting and frees up time to concentrate on high-risk warnings. These models are implemented on approach devices, access, and principal servers. The efficacy of several models, including hybrid approaches, is assessed. The findings of these studies are that the AdaBoost model provides the highest efficiency, with a 0.98 ROC area and 95.7% accuracy, detecting 146 threats with 29 false positives. Similarly, the random forest model achieved a 0.98 area under the ROC curve and a 95% overall accuracy, accurately identifying 132 threats and reducing false positives to 31. The hybrid model exhibited promise with a 0.89 ROC area and 94.9% accuracy, though it requires further refinement to lower its false positive rate. This research emphasizes the role of machine learning in improving cyber-security, particularly for critical infrastructure. Advanced ML techniques enhance threat detection and response times, and their continuous learning ability ensures adaptability to new threats. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors Technology in Smart Cities)
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17 pages, 2238 KiB  
Article
A Vehicle-Edge-Cloud Framework for Computational Analysis of a Fine-Tuned Deep Learning Model
by M. Jalal Khan, Manzoor Ahmed Khan, Sherzod Turaev, Sumbal Malik, Hesham El-Sayed and Farman Ullah
Sensors 2024, 24(7), 2080; https://doi.org/10.3390/s24072080 - 25 Mar 2024
Cited by 6 | Viewed by 1908
Abstract
The cooperative, connected, and automated mobility (CCAM) infrastructure plays a key role in understanding and enhancing the environmental perception of autonomous vehicles (AVs) driving in complex urban settings. However, the deployment of CCAM infrastructure necessitates the efficient selection of the computational processing layer [...] Read more.
The cooperative, connected, and automated mobility (CCAM) infrastructure plays a key role in understanding and enhancing the environmental perception of autonomous vehicles (AVs) driving in complex urban settings. However, the deployment of CCAM infrastructure necessitates the efficient selection of the computational processing layer and deployment of machine learning (ML) and deep learning (DL) models to achieve greater performance of AVs in complex urban environments. In this paper, we propose a computational framework and analyze the effectiveness of a custom-trained DL model (YOLOv8) when deployed in diverse devices and settings at the vehicle-edge-cloud-layered architecture. Our main focus is to understand the interplay and relationship between the DL model’s accuracy and execution time during deployment at the layered framework. Therefore, we investigate the trade-offs between accuracy and time by the deployment process of the YOLOv8 model over each layer of the computational framework. We consider the CCAM infrastructures, i.e., sensory devices, computation, and communication at each layer. The findings reveal that the performance metrics results (e.g., 0.842 mAP@0.5) of deployed DL models remain consistent regardless of the device type across any layer of the framework. However, we observe that inference times for object detection tasks tend to decrease when the DL model is subjected to different environmental conditions. For instance, the Jetson AGX (non-GPU) outperforms the Raspberry Pi (non-GPU) by reducing inference time by 72%, whereas the Jetson AGX Xavier (GPU) outperforms the Jetson AGX ARMv8 (non-GPU) by reducing inference time by 90%. A complete average time comparison analysis for the transfer time, preprocess time, and total time of devices Apple M2 Max, Intel Xeon, Tesla T4, NVIDIA A100, Tesla V100, etc., is provided in the paper. Our findings direct the researchers and practitioners to select the most appropriate device type and environment for the deployment of DL models required for production. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors Technology in Smart Cities)
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Review

Jump to: Research

23 pages, 1158 KiB  
Review
Review of Recent Advances in Predictive Maintenance and Cybersecurity for Solar Plants
by Younes Ledmaoui, Adila El Maghraoui, Mohamed El Aroussi and Rachid Saadane
Sensors 2025, 25(1), 206; https://doi.org/10.3390/s25010206 - 2 Jan 2025
Cited by 4 | Viewed by 3242
Abstract
This paper presents a systematic review that explores the latest advancements in predictive maintenance methods and cybersecurity for solar panel systems, shedding light on the advantages and challenges of the most recent developments in predictive maintenance techniques for solar plants. Numerous important research [...] Read more.
This paper presents a systematic review that explores the latest advancements in predictive maintenance methods and cybersecurity for solar panel systems, shedding light on the advantages and challenges of the most recent developments in predictive maintenance techniques for solar plants. Numerous important research studies, reviews, and empirical studies published between 2018 and 2023 are examined. These technologies help in detecting defects, degradation, and anomalies in solar panels by facilitating early intervention and reducing the probability of inverter failures. The analysis also emphasizes how challenging it is to adopt predictive maintenance in the renewable energy industry. Achieving a balance between model complexity and accuracy, dealing with system unpredictability, and adjusting to shifting environmental conditions are among the challenges. It also highlights the Internet of Things (IoT), machine learning (ML), and deep learning (DL), which are all incorporated into solar panel predictive maintenance. By enabling real-time monitoring, data analysis, and anomaly identification, these developments improve the accuracy and effectiveness of maintenance procedures. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors Technology in Smart Cities)
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22 pages, 5459 KiB  
Review
Trends in Digital Twin Framework Architectures for Smart Cities: A Case Study in Smart Mobility
by Evanthia Faliagka, Eleni Christopoulou, Dimitrios Ringas, Tanya Politi, Nikos Kostis, Dimitris Leonardos, Christos Tranoris, Christos P. Antonopoulos, Spyros Denazis and Nikolaos Voros
Sensors 2024, 24(5), 1665; https://doi.org/10.3390/s24051665 - 4 Mar 2024
Cited by 23 | Viewed by 9098
Abstract
The main aim of this paper is to present an innovative approach to addressing the challenges of smart mobility exploiting digital twins within the METACITIES initiative. We have worked on this issue due to the increasing complexity of urban transportation systems, coupled with [...] Read more.
The main aim of this paper is to present an innovative approach to addressing the challenges of smart mobility exploiting digital twins within the METACITIES initiative. We have worked on this issue due to the increasing complexity of urban transportation systems, coupled with the urgent need to improve efficiency, safety, and sustainability in cities. The work presented in this paper is part of the project METACITIES, an Excellence Hub that spans a large geographical area, that of Southeastern Europe. The approach of the Greek innovation ecosystem of METACITIES involves leveraging digital twin technology to create intelligent replicas of urban mobility environments, enabling real-time monitoring, analysis, and decision making. Through use cases such as “Smart Parking”, “Environmental Behavior Analysis on Traffic Incidents”, and “Emergency Management”, we demonstrate how digital twins can optimize traffic flow, mitigate environmental impact, and enhance emergency response; these use cases will be tested on a small scale, before deciding on implementation at a larger and more expensive scale. The final outcome is the METACITIES Architecture for smart mobility, which will be part of an Open Digital Twin Framework capable of evolving a smart city into a metacity. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors Technology in Smart Cities)
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