Abdullah Alshammari^*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078634 - 08 May 2026

Abstract The proliferation of Internet of Things (IoT) devices in the infrastructure of smart cities has posed cybersecurity risks like never before, which have direct implications on the sustainability and energy consumption of cities. In this paper, a multi-faceted Threat-Resilient Energy-Conscious Security Framework (TRECSF) is introduced that combines intrusion detection methods powered by deep learning, blockchain-driven data integrity verification mechanism, and energy-aware security protocols in smart city ecosystems to achieve their sustainability. The new Hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model is introduced to the proposed architecture, which fulfills the purpose of the study to… More >

Prabhat Das^1,2, Dibya Jyoti Bora¹, Sajal Saha², Cheng-Chi Lee^3,4,*, Hirak Mazumdar²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079203 - 27 April 2026

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 >

Mohammed M. Alenazi^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075966 - 12 March 2026

Abstract Multimodal spatiotemporal data from smart city consumer electronics present critical challenges including cross-modal temporal misalignment, unreliable data quality, limited joint modeling of spatial and temporal dependencies, and weak resilience to adversarial updates. To address these limitations, EdgeST-Fusion is introduced as a cross-modal federated graph transformer framework for context-aware smart city analytics. The architecture integrates cross-modal embedding networks for modality alignment, graph transformer encoders for spatial dependency modeling, temporal self-attention for dynamic pattern learning, and adaptive anomaly detection to ensure data quality and security during aggregation. A privacy-preserving federated learning protocol with differential privacy guarantees enables… More >

Jawad Ali^1,*, Ioan-Lucian Popa^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 857-889, 2025, DOI:10.32604/cmes.2025.064604 - 30 October 2025

Abstract With the rapid growth of urbanization, smart city development has become a strategic priority worldwide, requiring complex and uncertain decision-making processes. In this context, advanced decision-support tools are essential to evaluate and prioritize competing initiatives effectively. To support effective prioritization of smart city initiatives under uncertainty, this study introduces a robust decision-making framework based on the t-arbicular fuzzy (t-AF) set—a recent extension of the t-spherical fuzzy set that incorporates an additional parameter, the radius , to enhance the representation of uncertainty. Dombi-based operational laws are formulated within this context, leading to the development of four… More >

Ibrar Afzal¹, Noor ul Amin^1,*, Zulfiqar Ahmad^1,*, Abdulmohsen Algarni²

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1377-1399, 2025, DOI:10.32604/cmc.2024.057755 - 03 January 2025

Abstract The deployment of the Internet of Things (IoT) with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses, smart cities, and smart transportation systems. Fog computing tackles a range of challenges, including processing, storage, bandwidth, latency, and reliability, by locally distributing secure information through end nodes. Consisting of endpoints, fog nodes, and back-end cloud infrastructure, it provides advanced capabilities beyond traditional cloud computing. In smart environments, particularly within smart city transportation systems, the abundance of devices and nodes poses significant challenges related… More >

Chiranjeevi Karri^1,2,*, José J. M. Machado³, João Manuel R. S. Tavares¹, Deepak Kumar Jain⁴, Suresh Dannana⁵, Santosh Kumar Gottapu⁶, Amir H. Gandomi^7,8

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3617-3663, 2024, DOI:10.32604/cmc.2024.058461 - 19 December 2024

Abstract The rapid population growth, insecure lifestyle, wastage of natural resources, indiscipline behavior of human beings, urgency in the medical field, security of patient information, agricultural-related problems, and automation requirements in industries are the reasons for invention of technologies. Smart cities aim to address these challenges through the integration of technology, data, and innovative practices. Building a smart city involves integrating advanced technologies and data-driven solutions to enhance urban living, improve resource efficiency, and create sustainable environments. This review presents five of the most critical technologies for smart and/or safe cities, addressing pertinent topics such as More >

Nurhadi, Rabiah Abdul Kadir^*, Ely Salwana Mat Surin, Mahidur R. Sarker^*

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1405-1435, 2024, DOI:10.32604/csse.2024.051851 - 22 November 2024

Abstract A data lake (DL), abbreviated as DL, denotes a vast reservoir or repository of data. It accumulates substantial volumes of data and employs advanced analytics to correlate data from diverse origins containing various forms of semi-structured, structured, and unstructured information. These systems use a flat architecture and run different types of data analytics. NoSQL databases are nontabular and store data in a different manner than the relational table. NoSQL databases come in various forms, including key-value pairs, documents, wide columns, and graphs, each based on its data model. They offer simpler scalability and generally outperform… More >

Farhan Amin, Gyu Sang Choi^*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3507-3521, 2024, DOI:10.32604/cmc.2024.053183 - 12 September 2024

Abstract Mobile networks possess significant information and thus are considered a gold mine for the researcher’s community. The call detail records (CDR) of a mobile network are used to identify the network’s efficacy and the mobile user’s behavior. It is evident from the recent literature that cyber-physical systems (CPS) were used in the analytics and modeling of telecom data. In addition, CPS is used to provide valuable services in smart cities. In general, a typical telecom company has millions of subscribers and thus generates massive amounts of data. From this aspect, data storage, analysis, and processing… More >

Yu Zhou¹, Bosong Lin¹, Siqi Hu², Dandan Yu^3,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 161-182, 2024, DOI:10.32604/cmc.2024.047836 - 25 April 2024

Abstract This paper addresses the problem of predicting population density leveraging cellular station data. As wireless communication devices are commonly used, cellular station data has become integral for estimating population figures and studying their movement, thereby implying significant contributions to urban planning. However, existing research grapples with issues pertinent to preprocessing base station data and the modeling of population prediction. To address this, we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant data. The preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population shift. Further, we devise a multi-view More >

Othman S. Al-Heety^1,*, Zahriladha Zakaria^1,*, 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 - 15 December 2023

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… More >