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  • Open Access

    ARTICLE

    Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

    Othman S. Al-Heety1,*, Zahriladha Zakaria1,*, Ahmed Abu-Khadrah2, Mahamod Ismail3, Sarmad Nozad Mahmood4, Mohammed Mudhafar Shakir5, Sameer Alani6, Hussein Alsariera1

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2103-2127, 2024, DOI:10.32604/cmes.2023.029509

    Abstract Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision. In this article, these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data. The framework integrates Kalman filtering and Q-learning. Unlike smoothing Kalman filtering, our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error. Unlike traditional Q-learning, our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road… More >

  • Open Access

    ARTICLE

    Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

    Firas Abedi1, Hayder M. A. Ghanimi2, Abeer D. Algarni3, Naglaa F. Soliman3,*, Walid El-Shafai4,5, Ali Hashim Abbas6, Zahraa H. Kareem7, Hussein Muhi Hariz8, Ahmed Alkhayyat9

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3127-3144, 2023, DOI:10.32604/csse.2023.038959

    Abstract Computational intelligence (CI) is a group of nature-simulated computational models and processes for addressing difficult real-life problems. The CI is useful in the UAV domain as it produces efficient, precise, and rapid solutions. Besides, unmanned aerial vehicles (UAV) developed a hot research topic in the smart city environment. Despite the benefits of UAVs, security remains a major challenging issue. In addition, deep learning (DL) enabled image classification is useful for several applications such as land cover classification, smart buildings, etc. This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification (MDLS-UAVIC) model in a smart city environment.… More >

  • Open Access

    REVIEW

    Embracing the Future: AI and ML Transforming Urban Environments in Smart Cities

    Gagan Deep*, Jyoti Verma

    Journal on Artificial Intelligence, Vol.5, pp. 57-73, 2023, DOI:10.32604/jai.2023.043329

    Abstract This research explores the increasing importance of Artificial Intelligence (AI) and Machine Learning (ML) with relation to smart cities. It discusses the AI and ML’s ability to revolutionize various aspects of urban environments, including infrastructure, governance, public safety, and sustainability. The research presents the definition and characteristics of smart cities, highlighting the key components and technologies driving initiatives for smart cities. The methodology employed in this study involved a comprehensive review of relevant literature, research papers, and reports on the subject of AI and ML in smart cities. Various sources were consulted to gather information on the integration of AI… More >

  • Open Access

    ARTICLE

    Design the IoT Botnet Defense Process for Cybersecurity in Smart City

    Donghyun Kim1, Seungho Jeon2, Jiho Shin3, Jung Taek Seo4,*

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2979-2997, 2023, DOI:10.32604/iasc.2023.040019

    Abstract The smart city comprises various infrastructures, including healthcare, transportation, manufacturing, and energy. A smart city’s Internet of Things (IoT) environment constitutes a massive IoT environment encompassing numerous devices. As many devices are installed, managing security for the entire IoT device ecosystem becomes challenging, and attack vectors accessible to attackers increase. However, these devices often have low power and specifications, lacking the same security features as general Information Technology (IT) systems, making them susceptible to cyberattacks. This vulnerability is particularly concerning in smart cities, where IoT devices are connected to essential support systems such as healthcare and transportation. Disruptions can lead… More >

  • Open Access

    ARTICLE

    Network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous Autoencoders (E-HAE)

    Lelisa Adeba Jilcha1, Deuk-Hun Kim2, Julian Jang-Jaccard3, Jin Kwak4,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3261-3284, 2023, DOI:10.32604/csse.2023.037615

    Abstract Contemporary attackers, mainly motivated by financial gain, consistently devise sophisticated penetration techniques to access important information or data. The growing use of Internet of Things (IoT) technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation, as it facilitates multiple new attack vectors to emerge effortlessly. As such, existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems. To address this problem, we designed a blended threat detection approach, considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence.… More >

  • Open Access

    ARTICLE

    Intelligent System Application to Monitor the Smart City Building Lighting

    Tzu-Chia Chen1, Ngakan Ketut Acwin Dwijendra2,*, Saurabh Singhal3, R. Sivaraman4, Amr Mamdouh5

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3159-3169, 2023, DOI:10.32604/cmc.2023.035418

    Abstract A smart city incorporates infrastructure methods that are environmentally responsible, such as smart communications, smart grids, smart energy, and smart buildings. The city administration has prioritized the use of cutting-edge technology and informatics as the primary strategy for enhancing service quality, with energy resources taking precedence. To achieve optimal energy management in the multidimensional system of a city tribe, it is necessary not only to identify and study the vast majority of energy elements, but also to define their implicit interdependencies. This is because optimal energy management is required to reach this objective. The lighting index is an essential consideration… More >

  • Open Access

    ARTICLE

    Deep Consensus Network for Recycling Waste Detection in Smart Cities

    Manar Ahmed Hamza1,*, Hanan Abdullah Mengash2, Noha Negm3, Radwa Marzouk2, Abdelwahed Motwakel1, Abu Sarwar Zamani1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4191-4205, 2023, DOI:10.32604/cmc.2023.027050

    Abstract Recently, urbanization becomes a major concern for developing as well as developed countries. Owing to the increased urbanization, one of the important challenging issues in smart cities is waste management. So, automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste management. Effective recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw materials. This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection (DCNWO-RWOD) in Smart Cities. The goal of… More >

  • Open Access

    REVIEW

    Modeling Methods of 3D Model in Digital Twins

    Ruijun Liu1, Haisheng Li1,*, Zhihan Lv2

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 985-1022, 2023, DOI:10.32604/cmes.2023.023154

    Abstract To understand the current application and development of 3D modeling in Digital Twins (DTs), abundant literatures on DTs and 3D modeling are investigated by means of literature review. The transition process from 3D modeling to DTs modeling is analyzed, as well as the current application of DTs modeling in various industries. The application of 3D DTs modeling in the fields of smart manufacturing, smart ecology, smart transportation, and smart buildings in smart cities is analyzed in detail, and the current limitations are summarized. It is found that the 3D modeling technology in DTs has broad prospects for development and has… More >

  • Open Access

    ARTICLE

    The Role of Deep Learning in Parking Space Identification and Prediction Systems

    Faizan Rasheed1, Yasir Saleem2, Kok-Lim Alvin Yau3,*, Yung-Wey Chong4,*, Sye Loong Keoh5

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 761-784, 2023, DOI:10.32604/cmc.2023.034988

    Abstract In today’s smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement… More >

  • Open Access

    ARTICLE

    Deep Learning Implemented Visualizing City Cleanliness Level by Garbage Detection

    M. S. Vivekanandan1, T. Jesudas2,*

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1639-1652, 2023, DOI:10.32604/iasc.2023.032301

    Abstract In an urban city, the daily challenges of managing cleanliness are the primary aspect of routine life, which requires a large number of resources, the manual process of labour, and budget. Street cleaning techniques include street sweepers going away to different metropolitan areas, manually verifying if the street required cleaning taking action. This research presents novel street garbage recognizing robotic navigation techniques by detecting the city’s street-level images and multi-level segmentation. For the large volume of the process, the deep learning-based methods can be better to achieve a high level of classification, object detection, and accuracy than other learning algorithms.… More >

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