Innovative Computational Models for Smart Cities

Submission Deadline: 31 January 2026 (closed) View: 2391 Submit to Journal

Guest Editor(s)

Professor Luis Hernández-Callejo, Universidad de Valladolid, Spain
Professor Pedro Moreno-Bernal, Universidad Autónoma del Estado de Morelos, Mexico
Professor Sergio Nesmachnow, Universidad de la República, Uruguay

Summary

Cities are transforming, and this process requires intervention from various areas. One of the significant changes involves developing and implementing innovative computational models, to be applied for problem solving in diverse areas. Cities must integrate new computational models and applications to enhance infrastructure and services, develop smart and sustainable mobility, integrate IoT and sensor technologies, develop smart communications, improve energy systems and water management, improve citizen participation and governance, among others.

This special issue is open for research and development contributions based on the main topics related to developing and applying new computational models. Among the related topics are (but not exclusive to):

- Innovative computing solutions for smart cities

- Computing models for smart energy, energy efficiency, and sustainability

- Computing models for smart infrastructures and the environment

- Artificial intelligence for smart cities

- Computing models for smart mobility, IoT, sensor networks, and communications

- Computing models for smart public services

- Computing models for intelligent urban environments

The special issue welcomes submissions of original surveys, research articles, and case studies that have the potential to inspire individuals interested in the advancement of smart cities. Submitted articles are expected to provide valuable insights and contribute to the overall well-being and progress of smart cities.

Keywords

● Innovative computational models
● Computational intelligence for smart cities
● Renewable energies and storage
● Smart grid and Energy efficiency
● Cybersecurity and Artificial Intelligence
● Smart infrastructures
● Smart mobility, IoT, communication networks
● Smart public services
● Governance and citizenship

Published Papers

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Open Access

ARTICLE

A Resilient BIRCH-Based Smart Framework for Real-Time IoT Data Clustering
Prabhat Das, Dibya Jyoti Bora, Sajal Saha, Cheng-Chi Lee, Hirak Mazumdar
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079203
（This article belongs to the Special Issue: Innovative Computational Models for Smart Cities)
Abstract Real-time data processing is essential in the evolving landscape of IoT applications, ensuring efficiency, reliability, and adaptability. However, conventional clustering algorithms often face difficulties in managing high-frequency, continuous IoT data streams due to limited adaptability and high computational overhead. To address these challenges, this study proposes a resilient adaptation of the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) algorithm, tailored specifically for streaming IoT data. The enhanced approach dynamically recalculates clusters and determines the optimal number of clusters using the KneeLocator method. Unlike the original batch-oriented BIRCH, the modified version processes data incrementally, enabling More >

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ARTICLE

Optimizing IoT-Driven Smart Cities with the Dynamic Leader Sibha Algorithm: A Novel Approach to Feature Selection and Hyperparameter Tuning
Safaa Zaman, Marwa M. Eid, Ebrahim A. Mattar, Doaa Sami Khafaga, El-Sayed M. El-Kenawy
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079827
（This article belongs to the Special Issue: Innovative Computational Models for Smart Cities)
Abstract The rapid growth of Internet of Things (IoT) technologies has transformed modern urban environments into complex smart cities, generating vast amounts of high-dimensional, heterogeneous data. Effectively analyzing this data is crucial for optimizing urban infrastructure, enhancing quality of life, and supporting sustainable development. However, smart city data presents significant challenges, including non-linear dependencies, noisy signals, and high dimensionality. To address these challenges, this study proposes the Dynamic Leader Sibha Algorithm (DLSA), a novel metaheuristic optimization technique inspired by the structured counting dynamics of the Sibha. The DLSA was applied to the Smart Cities Index dataset,… More >

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ARTICLE

Forecasting Performance Indicators of a Single-Channel Solar Chimney Using Artificial Neural Networks
Carlos Torres-Aguilar, Pedro Moreno, Diego Rossit, Sergio Nesmachnow, Karla M. Aguilar-Castro, Edgar V. Macias-Melo, Luis Hernández-Callejo
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3859-3881, 2025, DOI:10.32604/cmes.2025.069996
（This article belongs to the Special Issue: Innovative Computational Models for Smart Cities)
Abstract Solar chimneys are renewable energy systems designed to enhance natural ventilation, improving thermal comfort in buildings. As passive systems, solar chimneys contribute to energy efficiency in a sustainable and environmentally friendly way. The effectiveness of a solar chimney depends on its design and orientation relative to the cardinal directions, both of which are critical for optimal performance. This article presents a supervised learning approach using artificial neural networks to forecast the performance indicators of solar chimneys. The dataset includes information from 2784 solar chimney configurations, which encompasses various factors such as chimney height, channel thickness, More >
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REVIEW

AI-Powered Digital Twin Frameworks for Smart Grid Optimization and Real-Time Energy Management in Smart Buildings: A Survey
Saeed Asadi, Hajar Kazemi Naeini, Delaram Hassanlou, Abolhassan Pishahang, Saeid Aghasoleymani Najafabadi, Abbas Sharifi, Mohsen Ahmadi
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1259-1301, 2025, DOI:10.32604/cmes.2025.070528
（This article belongs to the Special Issue: Innovative Computational Models for Smart Cities)
Abstract The growing energy demand of buildings, driven by rapid urbanization, poses significant challenges for sustainable urban development. As buildings account for over 40% of global energy consumption, innovative solutions are needed to improve efficiency, resilience, and environmental performance. This paper reviews the integration of Digital Twin (DT) technologies and Machine Learning (ML) for optimizing energy management in smart buildings connected to smart grids. A key enabler of this integration is the Internet of Things (IoT), which provides the sensor networks and real-time data streams that fee/d DT–ML frameworks, enabling accurate monitoring, forecasting, and adaptive control.… More >

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Innovative Computational Models for Smart Cities

Guest Editor(s)

Summary

Keywords

Published Papers

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84

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456

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3262

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594

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