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Artificial Intelligence and Their Applications in Smart Cities

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

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 50907

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Dr. Tomás Mateo Sanguino

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Department of Electronic Engineering, Computer Systems and Automatics, University of Huelva, Av. de las Artes s/n, 21007 Huelva, Spain
Interests: road safety; communications; cybersecurity; smart city
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Sasho Gramatikov

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Ss. Cyril and Methodius University in Skopje, Faculty of Computer Science and Engineering "Rugjer Boshkovikj" 16, P.O. Box 393 1000 Skopje, Republic of North Macedonia
Interests: computer networks; simulation and modelling; linked data; machine learning

Special Issue Information

Dear Colleagues,

Cities are experiencing a digital transformation that requires immediate attention in terms of energy, transport, mobility, communication, security, education, tourism, and social aspects, as well as promoting all those actions that seek to improve quality of life and sustainable development. ICT has brought revolutionary applications with the potential to drastically improve the way people live. In this context, smartphones and the IoT have made the Internet and people sensory, thus enabling more proactive and less reactive intelligent applications. This Special Issue encourages original and high-quality works with an applied purpose addressing infrastructure, transportation, and human mobility in the context of Smart Cities. This includes emerging technologies with integrated sensors, wireless communications, and artificial intelligence, such as WSN applications; security issues in IoT infrastructures; the development of real-time information systems through I2P (infrastructure to pedestrian), V2I (vehicle to infrastructure), V2V (vehicle to vehicle) or V2P (vehicle to pedestrian); and the use of machine learning, expert systems and intelligent control, among others. In addition, this Special Issue will accept review manuscripts which show the state-of-the-art and potential of both advanced systems and applications in the topic of intelligent solutions for Smart Cities.

Potential topics include but are not limited to the following:

Mobile apps and AI applications integrated in Smart Cities;
Simulation, deployment, and testbed platforms for Smart Cities;
Accessibility, resilience, and security of IoT infrastructures for Smart Cities;
Real-time information systems for ITS in Smart Cities;
AI techniques for devices and embedded systems in Smart Cities;
Review manuscripts on intelligent advanced systems and their applications for infrastructures, pedestrians, and vehicles in Smart Cities.

Dr. Tomás Mateo Sanguino
Prof. Dr. Sasho Gramatikov
Guest Editor

Manuscript Submission Information

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Published Papers (10 papers)

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Research

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26 pages, 2277 KiB

Open AccessArticle

Multi-Agent Credit Assignment and Bankruptcy Game for Improving Resource Allocation in Smart Cities

by Hossein Yarahmadi, Mohammad Ebrahim Shiri, Moharram Challenger, Hamidreza Navidi and Arash Sharifi

Sensors 2023, 23(4), 1804; https://doi.org/10.3390/s23041804 - 6 Feb 2023

Cited by 3 | Viewed by 1819

Abstract

In recent years, the development of smart cities has accelerated. There are several issues to handle in smart cities, one of the most important of which is efficient resource allocation. For the modeling of smart cities, multi-agent systems (MASs) can be used. In this paper, an efficient approach is proposed for resource allocation in smart cities based on the multi-agent credit assignment problem (MCA) and bankruptcy game. To this end, the resource allocation problem is mapped to MCA and the bankruptcy game. To solve this problem, first, a task start threshold (TST) constraint is introduced. The MCA turns into a bankruptcy problem upon introducing such a constraint. Therefore, based on the concept of bankruptcy, three methods of TS-Only, TS + MAS, and TS + ExAg are presented to solve the MCA. In addition, this work introduces a multi-score problem (MSP) in which a different reward is offered for solving each part of the problem, and we used it in our experiments to examine the proposed methods. The proposed approach is evaluated based on the learning rate, confidence, expertness, efficiency, certainty, and correctness parameters. The results reveal the better performance of the proposed approach compared to the existing methods in five parameters. Full article

(This article belongs to the Special Issue Artificial Intelligence and Their Applications in Smart Cities)

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11 pages, 4686 KiB

Open AccessArticle

Development of Road Surface Detection Algorithm Using CycleGAN-Augmented Dataset

by Wansik Choi, Jun Heo and Changsun Ahn

Sensors 2021, 21(22), 7769; https://doi.org/10.3390/s21227769 - 22 Nov 2021

Cited by 14 | Viewed by 4368

Abstract

Road surface detection is important for safely driving autonomous vehicles. This is because the knowledge of road surface conditions, in particular, dry, wet, and snowy surfaces, should be considered for driving control of autonomous vehicles. With the rise of deep learning technology, road surface detection methods using deep neural networks (DNN) have been widely used for developing road surface detection algorithms. To apply DNN in road surface detection, the dataset should be large and well-balanced for accurate and robust performance. However, most of the images of road surfaces obtained through usual data collection processes are not well-balanced. Most of the collected surface images tend to be of dry surfaces because road surface conditions are highly correlated with weather conditions. This could be a challenge in developing road surface detection algorithms. This paper proposes a method to balance the imbalanced dataset using CycleGAN to improve the performance of a road surface detection algorithm. CycleGAN was used to artificially generate images of wet and snow-covered roads. The road surface detection algorithm trained using the CycleGAN-augmented dataset had a better IoU than the method using imbalanced basic datasets. This result shows that CycleGAN-generated images can be used as datasets for road surface detection to improve the performance of DNN, and this method can help make the data acquisition process easy. Full article

(This article belongs to the Special Issue Artificial Intelligence and Their Applications in Smart Cities)

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27 pages, 6343 KiB

Open AccessEditor’s ChoiceArticle

Attention-Based Multi-Scale Convolutional Neural Network (A+MCNN) for Multi-Class Classification in Road Images

by Elham Eslami and Hae-Bum Yun

Sensors 2021, 21(15), 5137; https://doi.org/10.3390/s21155137 - 29 Jul 2021

Cited by 36 | Viewed by 7520

Abstract

Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task remains challenging due to the high variation of defects in shapes and sizes, demanding a better incorporation of contextual information into deep networks. In this paper, we show that an attention-based multi-scale convolutional neural network (A+MCNN) improves the automated classification of common distress and non-distress objects in pavement images by (i) encoding contextual information through multi-scale input tiles and (ii) employing a mid-fusion approach with an attention module for heterogeneous image contexts from different input scales. A+MCNN is trained and tested with four distress classes (crack, crack seal, patch, pothole), five non-distress classes (joint, marker, manhole cover, curbing, shoulder), and two pavement classes (asphalt, concrete). A+MCNN is compared with four deep classifiers that are widely used in transportation applications and a generic CNN classifier (as the control model). The results show that A+MCNN consistently outperforms the baselines by 1∼26% on average in terms of the F-score. A comprehensive discussion is also presented regarding how these classifiers perform differently on different road objects, which has been rarely addressed in the existing literature. Full article

(This article belongs to the Special Issue Artificial Intelligence and Their Applications in Smart Cities)

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15 pages, 28092 KiB

Open AccessArticle

Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron

by Ali Usman Gondal, Muhammad Imran Sadiq, Tariq Ali, Muhammad Irfan, Ahmad Shaf, Muhammad Aamir, Muhammad Shoaib, Adam Glowacz, Ryszard Tadeusiewicz and Eliasz Kantoch

Sensors 2021, 21(14), 4916; https://doi.org/10.3390/s21144916 - 19 Jul 2021

Cited by 34 | Viewed by 9006

Abstract

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features. Full article

(This article belongs to the Special Issue Artificial Intelligence and Their Applications in Smart Cities)

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28 pages, 362 KiB

Open AccessArticle

Horizontal Review on Video Surveillance for Smart Cities: Edge Devices, Applications, Datasets, and Future Trends

by Mostafa Ahmed Ezzat, Mohamed A. Abd El Ghany, Sultan Almotairi and Mohammed A.-M. Salem

Sensors 2021, 21(9), 3222; https://doi.org/10.3390/s21093222 - 6 May 2021

Cited by 22 | Viewed by 5168

Abstract

The automation strategy of today’s smart cities relies on large IoT (internet of Things) systems that collect big data analytics to gain insights. Although there have been recent reviews in this field, there is a remarkable gap that addresses four sides of the problem. Namely, the application of video surveillance in smart cities, algorithms, datasets, and embedded systems. In this paper, we discuss the latest datasets used, the algorithms used, and the recent advances in embedded systems to form edge vision computing are introduced. Moreover, future trends and challenges are addressed. Full article

(This article belongs to the Special Issue Artificial Intelligence and Their Applications in Smart Cities)

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17 pages, 4233 KiB

Open AccessArticle

Human Mesh Reconstruction with Generative Adversarial Networks from Single RGB Images

by Rui Gao, Mingyun Wen, Jisun Park and Kyungeun Cho

Sensors 2021, 21(4), 1350; https://doi.org/10.3390/s21041350 - 14 Feb 2021

Cited by 2 | Viewed by 3523

Abstract

Applications related to smart cities require virtual cities in the experimental development stage. To build a virtual city that are close to a real city, a large number of various types of human models need to be created. To reduce the cost of acquiring models, this paper proposes a method to reconstruct 3D human meshes from single images captured using a normal camera. It presents a method for reconstructing the complete mesh of the human body from a single RGB image and a generative adversarial network consisting of a newly designed shape–pose-based generator (based on deep convolutional neural networks) and an enhanced multi-source discriminator. Using a machine learning approach, the reliance on multiple sensors is reduced and 3D human meshes can be recovered using a single camera, thereby reducing the cost of building smart cities. The proposed method achieves an accuracy of 92.1% in body shape recovery; it can also process 34 images per second. The method proposed in this paper approach significantly improves the performance compared with previous state-of-the-art approaches. Given a single view image of various humans, our results can be used to generate various 3D human models, which can facilitate 3D human modeling work to simulate virtual cities. Since our method can also restore the poses of the humans in the image, it is possible to create various human poses by given corresponding images with specific human poses. Full article

(This article belongs to the Special Issue Artificial Intelligence and Their Applications in Smart Cities)

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18 pages, 1607 KiB

Open AccessArticle

Multi-Horizon Air Pollution Forecasting with Deep Neural Networks

by Mirche Arsov, Eftim Zdravevski, Petre Lameski, Roberto Corizzo, Nikola Koteli, Sasho Gramatikov, Kosta Mitreski and Vladimir Trajkovik

Sensors 2021, 21(4), 1235; https://doi.org/10.3390/s21041235 - 10 Feb 2021

Cited by 27 | Viewed by 4090

Abstract

Air pollution is a global problem, especially in urban areas where the population density is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and waste. North Macedonia, as a developing country, has a serious problem with air pollution. The problem is highly present in its capital city, Skopje, where air pollution places it consistently within the top 10 cities in the world during the winter months. In this work, we propose using Recurrent Neural Network (RNN) models with long short-term memory units to predict the level of PM10 particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from sensors placed at multiple locations in Skopje and meteorological conditions such as temperature and humidity. We compare different deep learning models’ performance to an Auto-regressive Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models consistently outperform the baseline model and can be successfully employed for air pollution prediction. Ultimately, we demonstrate that these models can help decision-makers and local authorities better manage the air pollution consequences by taking proactive measures. Full article

(This article belongs to the Special Issue Artificial Intelligence and Their Applications in Smart Cities)

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22 pages, 5523 KiB

Open AccessArticle

Walking Secure: Safe Routing Planning Algorithm and Pedestrian’s Crossing Intention Detector Based on Fuzzy Logic App

by José Manuel Lozano Domínguez and Tomás de J. Mateo Sanguino

Sensors 2021, 21(2), 529; https://doi.org/10.3390/s21020529 - 13 Jan 2021

Cited by 11 | Viewed by 3605

Abstract

Improving road safety through artificial intelligence is now crucial to achieving more secure smart cities. With this objective, a mobile app based on the integration of the smartphone sensors and a fuzzy logic strategy to determine the pedestrian’s crossing intention around crosswalks is presented. The app developed also allows the calculation, tracing and guidance of safe routes thanks to an optimization algorithm that includes pedestrian areas on the paths generated over the whole city through a cloud database (i.e., zebra crossings, pedestrian streets and walkways). The experimentation carried out consisted in testing the fuzzy logic strategy with a total of 31 volunteers crossing and walking around a crosswalk. For that, the fuzzy logic approach was subjected to a total of 3120 samples generated by the volunteers. It has been proven that a smartphone can be successfully used as a crossing intention detector system with an accuracy of 98.63%, obtaining a true positive rate of 98.27% and a specificity of 99.39% according to a receiver operating characteristic analysis. Finally, a total of 30 routes were calculated by the proposed algorithm and compared with Google Maps considering the values of time, distance and safety along the routes. As a result, the routes generated by the proposed algorithm were safer than the routes obtained with Google Maps, achieving an increase in the use of safe pedestrian areas of at least 183%. Full article

(This article belongs to the Special Issue Artificial Intelligence and Their Applications in Smart Cities)

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17 pages, 2249 KiB

Open AccessArticle

Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks

by José Manuel Lozano Domínguez, Faroq Al-Tam, Tomás de J. Mateo Sanguino and Noélia Correia

Sensors 2020, 20(21), 6019; https://doi.org/10.3390/s20216019 - 23 Oct 2020

Cited by 12 | Viewed by 4628

Abstract

Improving road safety through artificial intelligence-based systems is now crucial turning smart cities into a reality. Under this highly relevant and extensive heading, an approach is proposed to improve vehicle detection in smart crosswalks using machine learning models. Contrarily to classic fuzzy classifiers, machine learning models do not require the readjustment of labels that depend on the location of the system and the road conditions. Several machine learning models were trained and tested using real traffic data taken from urban scenarios in both Portugal and Spain. These include random forest, time-series forecasting, multi-layer perceptron, support vector machine, and logistic regression models. A deep reinforcement learning agent, based on a state-of-the-art double-deep recurrent Q-network, is also designed and compared with the machine learning models just mentioned. Results show that the machine learning models can efficiently replace the classic fuzzy classifier. Full article

(This article belongs to the Special Issue Artificial Intelligence and Their Applications in Smart Cities)

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Review

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14 pages, 246 KiB

Open AccessReview

Regulatory and Technical Constraints: An Overview of the Technical Possibilities and Regulatory Limitations of Vehicle Telematic Data

by Kevin McDonnell, Finbarr Murphy, Barry Sheehan, Leandro Masello, German Castignani and Cian Ryan

Sensors 2021, 21(10), 3517; https://doi.org/10.3390/s21103517 - 18 May 2021

Cited by 8 | Viewed by 5011

Abstract

A telematics device is a vehicle instrument that comes preinstalled by the vehicle manufacturer or can be added later. The device records information about driving behavior, including speed, acceleration, and turning force. When connected to vehicle computers, the device can also provide additional information regarding the mechanical usage and condition of the vehicle. All of this information can be transmitted to a central database via mobile networks. The information provided has led to new services such as Usage Based Insurance (UBI). A range of consultants, industry commentators and academics have produced an abundance of projections on how telematics information will allow the introduction of services from personalized insurance, bespoke entertainment and advertise and vehicle energy optimization, particularly for Electric Vehicles (EVs). In this paper we examine these potential services against a backdrop of nascent regulatory limitations and against the technical capacity of the devices. Using a case study approach, we examine three applications that can use telematics information. We find that the expectations of service providers will be significantly tempered by regulatory and technical hurdles. In our discussion we detail these limitations and suggest a more realistic rollout of ancillary services. Full article

(This article belongs to the Special Issue Artificial Intelligence and Their Applications in Smart Cities)

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