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Empowered Connected Futures of AI, IoT, and Cloud Computing in the Development of Cognitive Cities

Submission Deadline: 01 October 2025 (closed) View: 4434 Submit to Journal

Guest Editors

Prof. Mourade Azrour

Email: mo.azrour@umi.ac.ma

Affiliation: Faculty of sciences and techniques, Moulay Ismail University of Meknes; Morocco

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Research Interests: authentication protocol, computer security, Internet of things, smart systems, machine learning

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Prof. Abdulatif Alabdulatif

Email: ab.alabdulatif@qu.edu.sa

Affiliation: Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia

Homepage:

Research Interests: applied cryptography, blockchain technology, cloud computing security

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Prof. Sultan Ahmad

Email: s.alisher@psau.edu.sa

Affiliation: Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Saudi Arabia

Homepage:

Research Interests: distributed computing, big data, machine learning, the Internet of Things

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Prof. Azidine Guezzaz

Email: a.guezzaz@uca.ma

Affiliation: Technology Higher School Essaouira, Cadi Ayyad University, Morocco

Homepage:

Research Interests: computer security, cryptography, artificial intelligence, intrusion detection, IoT, cloud computing, smart cities

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Summary

Integrating Artificial Intelligence (AI), the Internet of Things (IoT), and Cloud Computing marks a rapidly evolving research frontier, signifying a substantial leap in technological progression within the digital era. The convergence of these technologies is pivotal in creating Cognitive Cities, leading to significant advancements across various domains, including smart urban development, healthcare, energy, and financial services. Cognitive Cities represents the next generation of smart cities, utilizing AI-powered predictive analytics within a cloud computing framework to enrich urban environments and operational processes. This synergy enhances data processing and analytics, improving efficiency and automation while providing scalable and flexible solutions. Furthermore, IoT is crucial within these interconnected frameworks, offering a dynamic and distributed network for real-time data exchange, analysis, and storage, which is essential for developing responsive, adaptive, and intelligent urban infrastructures.

Within this special issue, we aim to delve into the confluence of Artificial Intelligence (AI), the Internet of Things (IoT), Cloud Computing, and the emerging concept of Cognitive Cities as transformative forces in ecological optimization and refining management methodologies across key sectors. Cognitive Cities embody integrating these technologies in an urban context, enhancing industrial production, energy generation, and healthcare provision through advanced data analytics and interconnected infrastructures. We welcome contributions that share innovative technologies, forward-thinking policies, or best practices related to the development and implementation of Cognitive Cities from theoretical and practical perspectives. Contributions may include but are not limited to, discussions on the deployment of IoT in urban ecosystems, the application of AI for urban planning and management, the use of cloud-based platforms for city-wide data integration, and the socio-economic impact of transitioning to Cognitive Cities.

The scope of this special issue :

1. Theoretical advancements and models in AI that enhance IoT devices' capabilities within Cognitive Cities, leveraging cloud platforms to enhance intelligent urban ecosystems.

2. IoT architectures that utilize cloud services for improved scalability, performance, and reliability, underpinning the smart infrastructure of Cognitive Cities.

3. Case studies on cloud-enabled IoT solutions in Cognitive Cities that employ AI for real-time data analytics, decision-making, and urban planning.

4. Security, privacy, and ethical considerations in deploying AI-powered IoT systems within the complex and densely connected Cognitive Cities.

5. The impact of AI, IoT, and Cloud integration on the digital transformation of sectors such as healthcare, smart urban planning, manufacturing, and agriculture within the context of Cognitive Cities.

6. Future directions and challenges in standardizing practices for the seamless integration of AI, IoT, and Cloud Computing to support the sustainable development of Cognitive Cities.

Keywords

Artificial Intelligence, Cognitive Cities, IoT, Cloud Computing, Data Analytics, Connectivity Solutions, Security and Privacy

Published Papers

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Open Access

ARTICLE

Hybrid AI-IoT Framework with Digital Twin Integration for Predictive Urban Infrastructure Management in Smart Cities
Abdullah Alourani, Mehtab Alam, Ashraf Ali, Ihtiram Raza Khan, Chandra Kanta Samal
CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-32, 2026, DOI:10.32604/cmc.2025.070161
（This article belongs to the Special Issue: Empowered Connected Futures of AI, IoT, and Cloud Computing in the Development of Cognitive Cities)
Abstract The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management. Earlier approaches have often advanced one dimension—such as Internet of Things (IoT)-based data acquisition, Artificial Intelligence (AI)-driven analytics, or digital twin visualization—without fully integrating these strands into a single operational loop. As a result, many existing solutions encounter bottlenecks in responsiveness, interoperability, and scalability, while also leaving concerns about data privacy unresolved. This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing, distributed intelligence, and simulation-based decision support. The… More >

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ARTICLE

NeuroCivitas: A Federated Deep Learning Model for Adaptive Urban Intelligence in 6G Cognitive Cities
Nujud Aloshban, Abeer A.K. Alharbi
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4795-4826, 2025, DOI:10.32604/cmc.2025.067523
（This article belongs to the Special Issue: Empowered Connected Futures of AI, IoT, and Cloud Computing in the Development of Cognitive Cities)
Abstract The rise of 6G networks and the exponential growth of smart city infrastructures demand intelligent, real-time traffic forecasting solutions that preserve data privacy. This paper introduces NeuroCivitas, a federated deep learning framework that integrates Convolutional Neural Networks (CNNs) for spatial pattern recognition and Long Short-Term Memory (LSTM) networks for temporal sequence modeling. Designed to meet the adaptive intelligence requirements of cognitive cities, NeuroCivitas leverages Federated Averaging (FedAvg) to ensure privacy-preserving training while significantly reducing communication overhead—by 98.7% compared to centralized models. The model is evaluated using the Kaggle Traffic Prediction Dataset comprising 48,120 hourly records… More >

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127
Open Access

ARTICLE

FedCognis: An Adaptive Federated Learning Framework for Secure Anomaly Detection in Industrial IoT-Enabled Cognitive Cities
Abdulatif Alabdulatif
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1185-1220, 2025, DOI:10.32604/cmc.2025.066898
（This article belongs to the Special Issue: Empowered Connected Futures of AI, IoT, and Cloud Computing in the Development of Cognitive Cities)
Abstract FedCognis is a secure and scalable federated learning framework designed for continuous anomaly detection in Industrial Internet of Things-enabled Cognitive Cities (IIoTCC). It introduces two key innovations: a Quantum Secure Authentication (QSA) mechanism for adversarial defense and integrity validation, and a Self-Attention Long Short-Term Memory (SALSTM) model for high-accuracy spatiotemporal anomaly detection. Addressing core challenges in traditional Federated Learning (FL)—such as model poisoning, communication overhead, and concept drift—FedCognis integrates dynamic trust-based aggregation and lightweight cryptographic verification to ensure secure, real-time operation across heterogeneous IIoT domains including utilities, public safety, and traffic systems. Evaluated on the More >

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ARTICLE

Securing Internet of Things Devices with Federated Learning: A Privacy-Preserving Approach for Distributed Intrusion Detection
Sulaiman Al Amro
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4623-4658, 2025, DOI:10.32604/cmc.2025.063734
（This article belongs to the Special Issue: Empowered Connected Futures of AI, IoT, and Cloud Computing in the Development of Cognitive Cities)
Abstract The rapid proliferation of Internet of Things (IoT) devices has heightened security concerns, making intrusion detection a pivotal challenge in safeguarding these networks. Traditional centralized Intrusion Detection Systems (IDS) often fail to meet the privacy requirements and scalability demands of large-scale IoT ecosystems. To address these challenges, we propose an innovative privacy-preserving approach leveraging Federated Learning (FL) for distributed intrusion detection. Our model eliminates the need for aggregating sensitive data on a central server by training locally on IoT devices and sharing only encrypted model updates, ensuring enhanced privacy and scalability without compromising detection accuracy.… More >

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Open Access

ARTICLE

Detecting and Mitigating Distributed Denial of Service Attacks in Software-Defined Networking
Abdullah M. Alnajim, Faisal Mohammed Alotaibi, Sheroz Khan
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4515-4535, 2025, DOI:10.32604/cmc.2025.063139
（This article belongs to the Special Issue: Empowered Connected Futures of AI, IoT, and Cloud Computing in the Development of Cognitive Cities)
Abstract Distributed denial of service (DDoS) attacks are common network attacks that primarily target Internet of Things (IoT) devices. They are critical for emerging wireless services, especially for applications with limited latency. DDoS attacks pose significant risks to entrepreneurial businesses, preventing legitimate customers from accessing their websites. These attacks require intelligent analytics before processing service requests. Distributed denial of service (DDoS) attacks exploit vulnerabilities in IoT devices by launching multi-point distributed attacks. These attacks generate massive traffic that overwhelms the victim’s network, disrupting normal operations. The consequences of distributed denial of service (DDoS) attacks are typically… More >

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Open Access

ARTICLE

Bidirectional LSTM-Based Energy Consumption Forecasting: Advancing AI-Driven Cloud Integration for Cognitive City Energy Management
Sheik Mohideen Shah, Meganathan Selvamani, Mahesh Thyluru Ramakrishna, Surbhi Bhatia Khan, Shakila Basheer, Wajdan Al Malwi, Mohammad Tabrez Quasim
CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2907-2926, 2025, DOI:10.32604/cmc.2025.063809
（This article belongs to the Special Issue: Empowered Connected Futures of AI, IoT, and Cloud Computing in the Development of Cognitive Cities)
Abstract Efficient energy management is a cornerstone of advancing cognitive cities, where AI, IoT, and cloud computing seamlessly integrate to meet escalating global energy demands. Within this context, the ability to forecast electricity consumption with precision is vital, particularly in residential settings where usage patterns are highly variable and complex. This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory (LSTM) network. Leveraging a dataset containing over two million multivariate, time-series observations collected from a single household over nearly four years, our model addresses the limitations of traditional time-series forecasting… More >

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Empowered Connected Futures of AI, IoT, and Cloud Computing in the Development of Cognitive Cities

Guest Editors

Summary

Keywords

Published Papers

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381

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101

View

435

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127

View

2482

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2087

View

2186

Download

667

View

1043

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442

View

1057

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641

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