Ahmad Zia¹, Nazia Azim², Bekarystankyzy Akbayan³, Khalid J. Alzahrani⁴, Ateeq Ur Rehman^5,*, Faheem Ullah Khan⁶, Nouf Al-Kahtani⁷, Hend Khalid Alkahtani^8,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073818 - 12 January 2026

Abstract The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks. Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay. In this network, the task processed at fog nodes reduces transmission delay. Still, it increases energy consumption, while routing tasks to the cloud server saves energy at the cost of higher communication delay. Moreover, the… More >

Ahmed Awad Mohamed¹, Eslam Abdelhakim Seyam^2,*, Ahmed R. Elsaeed³, Laith Abualigah⁴, Aseel Smerat^5,6, Ahmed M. AbdelMouty⁷, Hosam E. Refaat⁸

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073171 - 12 January 2026

Abstract In recent years, fog computing has become an important environment for dealing with the Internet of Things. Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing. Task scheduling is crucial for efficiently handling IoT user requests, thereby improving system performance, cost, and energy consumption across nodes in cloud computing. With the large amount of data and user requests, achieving the optimal solution to the task scheduling problem is challenging, particularly in terms of cost and energy efficiency. In this paper, we develop novel strategies to save energy consumption across… More >

Abdul Rahaman Wahab Sait^1,*, Yazeed Alkhurayyif²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072481 - 12 January 2026

Abstract Malware poses a significant threat to the Internet of Things (IoT). It enables unauthorized access to devices in the IoT environment. The lack of unique architectural standards causes challenges in developing robust malware detection (MD) models. The existing models demand substantial computational resources. This study intends to build a lightweight MD model to detect anomalies in IoT networks. The authors develop a transformation technique, converting the malware binaries into images. MobileNet V2 is fine-tuned using improved grey wolf optimization (IGWO) to extract crucial features of malicious and benign samples. The ResNeXt model is combined with… More >

Atheer Aleran¹, Hanan Almukhalfi¹, Ayman Noor¹, Reyadh Alluhaibi², Abdulrahman Hafez³, Talal H. Noor^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070741 - 12 January 2026

Abstract Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs. Conventional maintenance methods, such as reactive maintenance (i.e., run to failure) or time-based preventive maintenance (i.e., scheduled servicing), prove ineffective for complex systems with many Internet of Things (IoT) devices and sensors because they fall short in detecting faults at early stages when it is most crucial. This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory (LSTM) Networks and Convolutional Neural Networks (CNNs). The framework… More >

Chia-Hui Liu^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.072887 - 09 December 2025

Abstract The Industrial Internet of Things (IIoT) has emerged as a cornerstone of Industry 4.0, enabling large-scale automation and data-driven decision-making across factories, supply chains, and critical infrastructures. However, the massive interconnection of resource-constrained devices also amplifies the risks of eavesdropping, data tampering, and device impersonation. While digital signatures are indispensable for ensuring authenticity and non-repudiation, conventional schemes such as RSA and ECC are vulnerable to quantum algorithms, jeopardizing long-term trust in IIoT deployments. This study proposes a lightweight, stateless, hash-based signature scheme that achieves post-quantum security while addressing the stringent efficiency demands of IIoT. The… More >

He Duan, Shi Zhang^*, Dayu Li

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-25, 2026, DOI:10.32604/cmc.2025.069628 - 09 December 2025

Abstract Internet of Things (IoT) interconnects devices via network protocols to enable intelligent sensing and control. Resource-constrained IoT devices rely on cloud servers for data storage and processing. However, this cloud-assisted architecture faces two critical challenges: the untrusted cloud services and the separation of data ownership from control. Although Attribute-based Searchable Encryption (ABSE) provides fine-grained access control and keyword search over encrypted data, existing schemes lack of error tolerance in exact multi-keyword matching. In this paper, we proposed an attribute-based multi-keyword fuzzy searchable encryption with forward ciphertext search (FCS-ABMSE) scheme that avoids computationally expensive bilinear pairing… More >

Rekha Phadke¹, Abdul Lateef Haroon Phulara Shaik², Dayanidhi Mohapatra³, Doaa Sami Khafaga^4,*, Eman Abdullah Aldakheel⁴, N. Sathyanarayana⁵

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-26, 2026, DOI:10.32604/cmc.2025.067564 - 09 December 2025

Abstract Recently, the Internet of Things (IoT) technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices. Furthermore, the IoT plays a key role in multiple domains, including industrial automation, smart homes, and intelligent transportation systems. However, an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness. To address these issue, this research proposes a Modified Walrus Optimization Algorithm (MWaOA) for effective resource management in smart IoT systems. In the proposed MWaOA, a crowding process… More >

Abeer Alnuaim^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-33, 2026, DOI:10.32604/cmc.2025.069110 - 10 November 2025

Abstract The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats. In the evolving landscape of cybersecurity, the efficacy of Intrusion Detection Systems (IDS) is increasingly measured by technical performance, operational usability, and adaptability. This study introduces and rigorously evaluates a Human-Computer Interaction (HCI)-Integrated IDS with the utilization of Convolutional Neural Network (CNN), CNN-Long Short Term Memory (LSTM), and Random Forest (RF) against both a Baseline Machine Learning (ML) and a Traditional IDS model, through an extensive experimental framework encompassing many performance metrics, including detection latency, accuracy, alert prioritization, classification… More >

Fatima Al-Quayed^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.069102 - 10 November 2025

Abstract Due to the growth of smart cities, many real-time systems have been developed to support smart cities using Internet of Things (IoT) and emerging technologies. They are formulated to collect the data for environment monitoring and automate the communication process. In recent decades, researchers have made many efforts to propose autonomous systems for manipulating network data and providing on-time responses in critical operations. However, the widespread use of IoT devices in resource-constrained applications and mobile sensor networks introduces significant research challenges for cybersecurity. These systems are vulnerable to a variety of cyberattacks, including unauthorized access,… More >

Misbah Anwer^1,*, Ghufran Ahmed¹, Maha Abdelhaq², Raed Alsaqour³, Shahid Hussain⁴, Adnan Akhunzada^5,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.068673 - 10 November 2025

Abstract The exponential growth of the Internet of Things (IoT) has introduced significant security challenges, with zero-day attacks emerging as one of the most critical and challenging threats. Traditional Machine Learning (ML) and Deep Learning (DL) techniques have demonstrated promising early detection capabilities. However, their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints, high computational costs, and the costly time-intensive process of data labeling. To address these challenges, this study proposes a Federated Learning (FL) framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in… More >