Table of Content

Computer Modeling for Smart Cities Applications

Submission Deadline: 31 January 2023 Submit to Special Issue

Guest Editors

Prof. Wenbing Zhao, Cleveland State University, USA
Dr. Chengxi Huang, Xiamen University, China
Dr. Yi-Zhang Jiang, Jiangnan University, China

Summary

More than half of the world population is living in cities. It requires extended infrastructure and various services to support the densely concentrated population, ranging from electric grid, private and public transportation, water supply and sewage sanitation, telecommunication, health care, banking, education, childcare, nursing homes, welfare, law enforcement, and governmental operations. Therefore, cities provide a huge opportunity to become smarter by utilizing the latest computer and information technologies. By smarter, we mean that the city operation will be more efficient, cost less, consumes less energy, more connected, more secure, and more environmentally friendly.

 

The list of enabling technologies that could be used towards smart cities is large. To name just a few, there are Internet of Things (IoT) and sensing technology, new generations of telecommunication technology such as 5G, cyber-physical systems, cloud and service-orientated computing, smart grid, intelligent transportation systems, unmanned aerial vehicles, autonomous driving, machine learning, artificial intelligence, computer vision, and the blockchain technology. One particular valuable opportunity in smart city research is the integration of multiple enabling technologies, which is a largely unexplored territory.

 

By using the enabling technologies, a wide variety of smart city applications could be developed. We have already seen smart city consortiums and hubs being created that focus on the cultivating and sharing of innovative applications. For example, Smart Cities Connect Media and Research frequently organize events such as conferences, exhibitions, and competitions. They organized the applications into five categories: (1) community engagement, (2) digital transformation, (3) smart mobility, (4) urban infrastructure, and (5) urban operations.

 

We envisage that many new interesting scientific and engineering problems will arise when integrating multiple enabling technologies together for smart cities. On the other hand, designing smart city applications would also expose areas in the individual enabling technologies that should be improved. Furthermore, in this proposed special section, we encourage researchers to develop coherency in smart city research. What are the common set of requirements for smart cities? How could the enabling technologies work together seamlessly towards future smart cities? These are very important questions to investigate. An important approach is the development of a robust and comprehensive model for smart cities that would be fundamental to the development of many smart city applications.

 

This special issue welcomes original research and review articles on all aspects of computer modeling for smart city enabling technologies and applications. Topics of interest include, but not limited to, the following areas:

· Computer Modeling for Smart City Enabling technologies:

  - Internet of Things (IoT)

  - 5G

  - Cyber-physical systems

  - Cloud and service-oriented computing

  - Pervasive computing

  - Smart grid

  - Intelligent transportation systems

  - Unmanned aerial vehicles

  - Autonomous driving

  - Virtual and mixed reality

  - Computer vision

  - Machine learning

  - Artificial intelligence

  - Security and privacy

  - Blockchain and distributed ledger

  - Integration of enabling technologies for smart city applications

· Computer Modeling for Smart City Applications:

  - Air quality monitoring

  - Water quality monitoring

  - Parking management

  - Natural disaster response system

  - Emergence response system

  - Smart home

  - Smart office

  - Smart building

  - Smart classroom

  - Smart factory

  - Smart urban gardens

  - Smart public park

  - Smart transit

  - Airport security check system

  - Smart healthcare

  - Transparent government operations



Published Papers


  • Open Access

    REVIEW

    Application of Automated Guided Vehicles in Smart Automated Warehouse Systems: A Survey

    Zheng Zhang, Juan Chen, Qing Guo
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1529-1563, 2023, DOI:10.32604/cmes.2022.021451
    (This article belongs to this Special Issue: Computer Modeling for Smart Cities Applications)
    Abstract Automated Guided Vehicles (AGVs) have been introduced into various applications, such as automated warehouse systems, flexible manufacturing systems, and container terminal systems. However, few publications have outlined problems in need of attention in AGV applications comprehensively. In this paper, several key issues and essential models are presented. First, the advantages and disadvantages of centralized and decentralized AGVs systems were compared; second, warehouse layout and operation optimization were introduced, including some omitted areas, such as AGVs fleet size and electrical energy management; third, AGVs scheduling algorithms in chessboardlike environments were analyzed; fourth, the classical route-planning algorithms for single AGV and multiple… More >

    Graphic Abstract

    Application of Automated Guided Vehicles in Smart Automated Warehouse Systems: A Survey

  • Open Access

    ARTICLE

    Water Quality Index Using Modified Random Forest Technique: Assessing Novel Input Features

    Wen Yee Wong, Ayman Khallel Ibrahim Al-Ani, Khairunnisa Hasikin, Anis Salwa Mohd Khairuddin, Sarah Abdul Razak, Hanee Farzana Hizaddin, Mohd Istajib Mokhtar, Muhammad Mokhzaini Azizan
    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 1011-1038, 2022, DOI:10.32604/cmes.2022.019244
    (This article belongs to this Special Issue: Computer Modeling for Smart Cities Applications)
    Abstract Water quality analysis is essential to understand the ecological status of aquatic life. Conventional water quality index (WQI) assessment methods are limited to features such as water acidic or basicity (pH), dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal nitrogen (NH3-N), and suspended solids (SS). These features are often insufficient to represent the water quality of a heavy metal–polluted river. Therefore, this paper aims to explore and analyze novel input features in order to formulate an improved WQI. In this work, prospective insights on the feasibility of alternative water quality input variables as new discriminant features… More >

  • Open Access

    ARTICLE

    Partitioning of Water Distribution Network into District Metered Areas Using Existing Valves

    Aniket N. Sharma, Shilpa R. Dongre, Rajesh Gupta, Prerna Pandey, Neeraj Dhanraj Bokde
    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1515-1537, 2022, DOI:10.32604/cmes.2022.018867
    (This article belongs to this Special Issue: Computer Modeling for Smart Cities Applications)
    Abstract Water distribution network (WDN) leakage management has received increased attention in recent years. One of the most successful leakage-control strategies is to divide the network into District Metered Areas (DMAs). As a multi-staged technique, the generation of DMAs is a difficult task in design and implementation (i.e., clustering, sectorization, and performance evaluation). Previous studies on DMAs implementation did not consider the potential use of existing valves in achieving the objective. In this work, a methodology is proposed for detecting clusters and reducing the cost of additional valves and DMA sectorization by considering existing valves as much as possible. The procedure… More >

  • Open Access

    ARTICLE

    Multi-Feature Fusion-Guided Multiscale Bidirectional Attention Networks for Logistics Pallet Segmentation

    Weiwei Cai, Yaping Song, Huan Duan, Zhenwei Xia, Zhanguo Wei
    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1539-1555, 2022, DOI:10.32604/cmes.2022.019785
    (This article belongs to this Special Issue: Computer Modeling for Smart Cities Applications)
    Abstract In the smart logistics industry, unmanned forklifts that intelligently identify logistics pallets can improve work efficiency in warehousing and transportation and are better than traditional manual forklifts driven by humans. Therefore, they play a critical role in smart warehousing, and semantics segmentation is an effective method to realize the intelligent identification of logistics pallets. However, most current recognition algorithms are ineffective due to the diverse types of pallets, their complex shapes, frequent blockades in production environments, and changing lighting conditions. This paper proposes a novel multi-feature fusion-guided multiscale bidirectional attention (MFMBA) neural network for logistics pallet segmentation. To better predict… More >

  • Open Access

    ARTICLE

    An Acceptance Model of Using Mobile-Government Services (AMGS)

    Ahmad Althunibat, Mohammad Abdallah, Mohammed Amin Almaiah, Nour Alabwaini, Thamer Ahmad Alrawashdeh
    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 865-880, 2022, DOI:10.32604/cmes.2022.019075
    (This article belongs to this Special Issue: Computer Modeling for Smart Cities Applications)
    Abstract In recent years, the telecommunications sector is no longer limited to traditional communications, but has become the backbone for the use of data, content and digital applications by individuals, governments and companies to ensure the continuation of economic and social activity in light of social distancing and total closure in most countries in the world. Therefore, electronic government (e-Government) and mobile government (m-Government) are the results of technological evolution and innovation. Hence, it is important to investigate the factors that influence the intention to use m-Government services among Jordan’s society. This paper proposed a new m-Government acceptance model in Jordan… More >

  • Open Access

    ARTICLE

    Spectral Matching Classification Method of Multi-State Similar Pigments Based on Feature Differences

    Meng Da, Huiqin Wang, Ke Wang, Zhan Wang
    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 513-527, 2022, DOI:10.32604/cmes.2022.019040
    (This article belongs to this Special Issue: Computer Modeling for Smart Cities Applications)
    Abstract The properties of the same pigments in murals are affected by different concentrations and particle diameters, which cause the shape of the spectral reflectance data curve to vary, thus influencing the outcome of matching calculations. This paper proposes a spectral matching classification method of multi-state similar pigments based on feature differences. Fast principal component analysis (FPCA) was used to calculate the eigenvalue variance of pigment spectral reflectance, then applied to the original reflectance values for parameter characterization. We first projected the original spectral reflectance from the spectral space to the characteristic variance space to identify the spectral curve. Secondly, the… More >

  • Open Access

    ARTICLE

    Deep Neural Network with Strip Pooling for Image Classification of Yarn-Dyed Plaid Fabrics

    Xiaoting Zhang, Weidong Gao, Ruru Pan
    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1533-1546, 2022, DOI:10.32604/cmes.2022.018763
    (This article belongs to this Special Issue: Computer Modeling for Smart Cities Applications)
    Abstract Historically, yarn-dyed plaid fabrics (YDPFs) have enjoyed enduring popularity with many rich plaid patterns, but production data are still classified and searched only according to production parameters. The process does not satisfy the visual needs of sample order production, fabric design, and stock management. This study produced an image dataset for YDPFs, collected from 10,661 fabric samples. The authors believe that the dataset will have significant utility in further research into YDPFs. Convolutional neural networks, such as VGG, ResNet, and DenseNet, with different hyperparameter groups, seemed the most promising tools for the study. This paper reports on the authors’ exhaustive… More >

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