Han-Yu Lin, Tung-Tso Tsai^*, Yi-Chuan Wang

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073234 - 12 January 2026

Abstract Cloud services, favored by many enterprises due to their high flexibility and easy operation, are widely used for data storage and processing. However, the high latency, together with transmission overheads of the cloud architecture, makes it difficult to quickly respond to the demands of IoT applications and local computation. To make up for these deficiencies in the cloud, fog computing has emerged as a critical role in the IoT applications. It decentralizes the computing power to various lower nodes close to data sources, so as to achieve the goal of low latency and distributed processing.… 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 >

Muntaham Inaam Hashmi¹, Muhammad Ayaz Khan², Khwaja Mansoor ul Hassan¹, Suliman A. Alsuhibany^3,*, Ainur Abduvalova⁴, Asfandyar Khan⁵

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071452 - 12 January 2026

Abstract Cyber-criminals target smart connected devices for spyware distribution and security breaches, but existing Internet of Things (IoT) security standards are insufficient. Major IoT industry players prioritize market share over security, leading to insecure smart products. Traditional host-based protection solutions are less effective due to limited resources. Overcoming these challenges and enhancing the security of IoT Devices requires a security design at the network level that uses lightweight cryptographic parameters. In order to handle control, administration, and security concerns in traditional networking, the Gateway Node offers a contemporary networking architecture. By managing all network-level computations and… 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 >

Supriya Wadekar^1,*, Shailendra Mittal¹, Ganesh Wakte², Rajshree Shinde²

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.071761 - 27 December 2025

Abstract Rapid evolutions of the Internet of Electric Vehicles (IoEVs) are reshaping and modernizing transport systems, yet challenges remain in energy efficiency, better battery aging, and grid stability. Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand, thus increasing energy costs and battery aging. This study proposes a smart charging station with an AI-powered Battery Management System (BMS), developed and simulated in MATLAB/Simulink, to increase optimality in energy flow, battery health, and impractical scheduling within the IoEV environment. The system operates through… 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 >

Fatima Khan¹, Amna Khan¹, Tariq Ali², Tariq Shahzad³, Tehseen Mazhar^4,*, Sunawar Khan⁵, Muhammad Adnan Khan^6,*, Habib Hamam^7,8,9,10

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-27, 2026, DOI:10.32604/cmc.2025.068228 - 09 December 2025

Abstract The rise in noise and air pollution poses severe risks to human health and the environment. Industrial and vehicular emissions release harmful pollutants such as CO₂, SO₂, CO, CH₄, and noise, leading to significant environmental degradation. Monitoring and analyzing pollutant concentrations in real-time is crucial for mitigating these risks. However, existing systems often lack the capacity to monitor both indoor and outdoor environments effectively.This study presents a low-cost, IoT-based pollution detection system that integrates gas sensors (MQ-135 and MQ-4), a noise sensor (LM393), and a humidity sensor (DHT-22), all connected to a Node MCU (ESP8266) microcontroller. The… More >

Abdullah Alourani¹, Mehtab Alam^2,*, Ashraf Ali³, Ihtiram Raza Khan⁴, Chandra Kanta Samal²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-32, 2026, DOI:10.32604/cmc.2025.070161 - 10 November 2025

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 >