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AI-IoT for New Challenges in Smart Cities
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AI-IoT for New Challenges in Smart Cities

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 14455

Special Issue Editors

Dr. Miguel Arevalillo-Herráez

E-Mail Website
Guest Editor
Departament d’Informàtica, Escola Tècnica Superior d’Enginyeria, Universitat de València, 46100 Burjassot, Valencia, Spain
Interests: computer networks; wireless sensor networks; multimedia networks; cloud computing
Special Issues, Collections and Topics in MDPI journals
Dr. Jaume Segura-Garcia

E-Mail Website
Guest Editor
Department of Computer Science, ETSE, Universitat de València, 46100 Burjassot, Valencia, Spain
Interests: IoT; WSN; Smart Cities; signal processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In between the disruptive technologies introduced in the last few years, we find AI–IoT as the combination of connected monitoring and actuating technologies, involved in IoT and the potential of artificial intelligence-based technologies to develop applications for classification, regression, etc. This combination enables each one of these fields to simultaneously address high-dimensional, time-varying and non-linear issues, as well as various challenges in Smart Cities with relatively low power. At the same time, unmanned monitoring technology, biological or hyperspectral imaging and other sensor combinations are constantly adapting their application requirements of wearability, high sensitivity, high integration, high accuracy and low costs. For this reason, both academia and industry are interested in realising the integration of applications of AI and IoT technologies. They are focused on developing intelligent systems that combine intelligent sensing with physical states, data, operational control and decision making. This Special Issue aims to discuss the application of AI and IoT technologies to optimise smart systems and sensors in Smart Cities oriented to the establishment of urban policies and services to citizens.

Dr. Miguel Arevalillo-Herráez
Dr. Jaume Segura-Garcia
Guest Editors

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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

  • Internet of Things
  • IoT
  • AI–IoT
  • smart cities
  • intelligent sensors
  • sensor networks
  • crowd sensing
  • intelligent sensing
  • smart systems
 

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Published Papers (4 papers)

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Research

19 pages, 10738 KiB  
Article
AI-IoT Low-Cost Pollution-Monitoring Sensor Network to Assist Citizens with Respiratory Problems
by Santiago Felici-Castell, Jaume Segura-Garcia, Juan J. Perez-Solano, Rafael Fayos-Jordan, Antonio Soriano-Asensi and Jose M. Alcaraz-Calero
Sensors 2023, 23(23), 9585; https://doi.org/10.3390/s23239585 - 3 Dec 2023
Cited by 2 | Viewed by 2318
Abstract
The proliferation and great variety of low-cost air quality (AQ) sensors, combined with their flexibility and energy efficiency, gives an opportunity to integrate them into Wireless Sensor Networks (WSN). However, with these sensors, AQ monitoring poses a significant challenge, as the data collection [...] Read more.
The proliferation and great variety of low-cost air quality (AQ) sensors, combined with their flexibility and energy efficiency, gives an opportunity to integrate them into Wireless Sensor Networks (WSN). However, with these sensors, AQ monitoring poses a significant challenge, as the data collection and analysis process is complex and prone to errors. Although these sensors do not meet the performance requirements for reference regulatory-equivalent monitoring, they can provide informative measurements and more if we can adjust and add further processing to their raw measurements. Therefore, the integration of these sensors aims to facilitate real-time monitoring and achieve a higher spatial and temporal sampling density, particularly in urban areas, where there is a strong interest in providing AQ surveillance services since there is an increase in respiratory/allergic issues among the population. Leveraging a network of low-cost sensors, supported by 5G communications in combination with Artificial Intelligence (AI) techniques (using Convolutional and Deep Neural Networks (CNN and DNN)) to predict 24-h-ahead readings is the goal of this article in order to be able to provide early warnings to the populations of hazards areas. We have evaluated four different neural network architectures: Multi-Linear prediction (with a dense Multi-Linear Neural Network (NN)), Multi-Dense network prediction, Multi-Convolutional network prediction, and Multi-Long Short-Term Memory (LSTM) network prediction. To perform the training of the prediction of the readings, we have prepared a significant dataset that is analyzed and processed for training and testing, achieving an estimation error for most of the predicted parameters of around 7.2% on average, with the best option being the Multi-LSTM network in the forthcoming 24 h. It is worth mentioning that some pollutants achieved lower estimation errors, such as CO2 with 0.1%, PM10 with 2.4% (as well as PM2.5 and PM1.0), and NO2 with 6.7%. Full article
(This article belongs to the Special Issue AI-IoT for New Challenges in Smart Cities)
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15 pages, 5649 KiB  
Article
Revolutionizing Urban Mobility: IoT-Enhanced Autonomous Parking Solutions with Transfer Learning for Smart Cities
by Qaiser Abbas, Gulzar Ahmad, Tahir Alyas, Turki Alghamdi, Yazed Alsaawy and Ali Alzahrani
Sensors 2023, 23(21), 8753; https://doi.org/10.3390/s23218753 - 27 Oct 2023
Cited by 7 | Viewed by 5375
Abstract
Smart cities have emerged as a specialized domain encompassing various technologies, transitioning from civil engineering to technology-driven solutions. The accelerated development of technologies, such as the Internet of Things (IoT), software-defined networks (SDN), 5G, artificial intelligence, cognitive science, and analytics, has played a [...] Read more.
Smart cities have emerged as a specialized domain encompassing various technologies, transitioning from civil engineering to technology-driven solutions. The accelerated development of technologies, such as the Internet of Things (IoT), software-defined networks (SDN), 5G, artificial intelligence, cognitive science, and analytics, has played a crucial role in providing solutions for smart cities. Smart cities heavily rely on devices, ad hoc networks, and cloud computing to integrate and streamline various activities towards common goals. However, the complexity arising from multiple cloud service providers offering myriad services necessitates a stable and coherent platform for sustainable operations. The Smart City Operational Platform Ecology (SCOPE) model has been developed to address the growing demands, and incorporates machine learning, cognitive correlates, ecosystem management, and security. SCOPE provides an ecosystem that establishes a balance for achieving sustainability and progress. In the context of smart cities, Internet of Things (IoT) devices play a significant role in enabling automation and data capture. This research paper focuses on a specific module of SCOPE, which deals with data processing and learning mechanisms for object identification in smart cities. Specifically, it presents a car parking system that utilizes smart identification techniques to identify vacant slots. The learning controller in SCOPE employs a two-tier approach, and utilizes two different models, namely Alex Net and YOLO, to ensure procedural stability and improvement. Full article
(This article belongs to the Special Issue AI-IoT for New Challenges in Smart Cities)
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21 pages, 1369 KiB  
Article
An Evidence Theory Based Embedding Model for the Management of Smart Water Environments
by Maha Driss, Wadii Boulila, Haithem Mezni, Mokhtar Sellami, Safa Ben Atitallah and Nouf Alharbi
Sensors 2023, 23(10), 4672; https://doi.org/10.3390/s23104672 - 11 May 2023
Cited by 1 | Viewed by 2174
Abstract
Having access to safe water and using it properly is crucial for human well-being, sustainable development, and environmental conservation. Nonetheless, the increasing disparity between human demands and natural freshwater resources is causing water scarcity, negatively impacting agricultural and industrial efficiency, and giving rise [...] Read more.
Having access to safe water and using it properly is crucial for human well-being, sustainable development, and environmental conservation. Nonetheless, the increasing disparity between human demands and natural freshwater resources is causing water scarcity, negatively impacting agricultural and industrial efficiency, and giving rise to numerous social and economic issues. Understanding and managing the causes of water scarcity and water quality degradation are essential steps toward more sustainable water management and use. In this context, continuous Internet of Things (IoT)-based water measurements are becoming increasingly crucial in environmental monitoring. However, these measurements are plagued by uncertainty issues that, if not handled correctly, can introduce bias and inaccuracy into our analysis, decision-making processes, and results. To cope with uncertainty issues related to sensed water data, we propose combining network representation learning with uncertainty handling methods to ensure rigorous and efficient modeling management of water resources. The proposed approach involves accounting for uncertainties in the water information system by leveraging probabilistic techniques and network representation learning. It creates a probabilistic embedding of the network, enabling the classification of uncertain representations of water information entities, and applies evidence theory to enable decision making that is aware of uncertainties, ultimately choosing appropriate management strategies for affected water areas. Full article
(This article belongs to the Special Issue AI-IoT for New Challenges in Smart Cities)
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20 pages, 10667 KiB  
Article
Machine-Learning-Based Carbon Dioxide Concentration Prediction for Hybrid Vehicles
by David Tena-Gago, Gelayol Golcarenarenji, Ignacio Martinez-Alpiste, Qi Wang and Jose M. Alcaraz-Calero
Sensors 2023, 23(3), 1350; https://doi.org/10.3390/s23031350 - 25 Jan 2023
Cited by 11 | Viewed by 3383
Abstract
The current understanding of CO2 emission concentrations in hybrid vehicles (HVs) is limited, due to the complexity of the constant changes in their power-train sources. This study aims to address this problem by examining the accuracy, speed and size of traditional and [...] Read more.
The current understanding of CO2 emission concentrations in hybrid vehicles (HVs) is limited, due to the complexity of the constant changes in their power-train sources. This study aims to address this problem by examining the accuracy, speed and size of traditional and advanced machine learning (ML) models for predicting CO2 emissions in HVs. A new long short-term memory (LSTM)-based model called UWS-LSTM has been developed to overcome the deficiencies of existing models. The dataset collected includes more than 20 parameters, and an extensive input feature optimization has been conducted to determine the most effective parameters. The results indicate that the UWS-LSTM model outperforms traditional ML and artificial neural network (ANN)-based models by achieving 97.5% accuracy. Furthermore, to demonstrate the efficiency of the proposed model, the CO2-concentration predictor has been implemented in a low-powered IoT device embedded in a commercial HV, resulting in rapid predictions with an average latency of 21.64 ms per prediction. The proposed algorithm is fast, accurate and computationally efficient, and it is anticipated that it will make a significant contribution to the field of smart vehicle applications. Full article
(This article belongs to the Special Issue AI-IoT for New Challenges in Smart Cities)
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