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 >

D. Bernal^*, O. Ledesma, P. Lamo, J. Bermejo

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-39, 2026, DOI:10.32604/cmc.2025.069746 - 10 November 2025

Abstract This work evaluates an architecture for decentralized authentication of Internet of Things (IoT) devices in Low Earth Orbit (LEO) satellite networks using IOTA Identity technology. To the best of our knowledge, it is the first proposal to integrate IOTA’s Directed Acyclic Graph (DAG)-based identity framework into satellite IoT environments, enabling lightweight and distributed authentication under intermittent connectivity. The system leverages Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) over the Tangle, eliminating the need for mining and sequential blocks. An identity management workflow is implemented that supports the creation, validation, deactivation, and reactivation of IoT devices,… More >

Yeasul Kim¹, Chaeeun Won¹, Hwankuk Kim^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-28, 2026, DOI:10.32604/cmc.2025.069608 - 10 November 2025

Abstract With the increasing emphasis on personal information protection, encryption through security protocols has emerged as a critical requirement in data transmission and reception processes. Nevertheless, IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices, spanning a range of devices from non-encrypted ones to fully encrypted ones. Given the limited visibility into payloads in this context, this study investigates AI-based attack detection methods that leverage encrypted traffic metadata, eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices. Using the UNSW-NB15 and CICIoT-2023 dataset, encrypted and… More >

Haoran Wang¹, Shuhong Yang², Kuan Shao², Tao Xiao², Zhenyong Zhang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069404 - 10 November 2025

Abstract With the rapid development of the Artificial Intelligence of Things (AIoT), convolutional neural networks (CNNs) have demonstrated potential and remarkable performance in AIoT applications due to their excellent performance in various inference tasks. However, the users have concerns about privacy leakage for the use of AI and the performance and efficiency of computing on resource-constrained IoT edge devices. Therefore, this paper proposes an efficient privacy-preserving CNN framework (i.e., EPPA) based on the Fully Homomorphic Encryption (FHE) scheme for AIoT application scenarios. In the plaintext domain, we verify schemes with different activation structures to determine the… More >

Kemahyanto Exaudi^1,2, Deris Stiawan^3,*, Bhakti Yudho Suprapto¹, Hanif Fakhrurroja⁴, Mohd. Yazid Idris⁵, Tami A. Alghamdi⁶, Rahmat Budiarto⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.069377 - 10 November 2025

Abstract Sudden wildfires cause significant global ecological damage. While satellite imagery has advanced early fire detection and mitigation, image-based systems face limitations including high false alarm rates, visual obstructions, and substantial computational demands, especially in complex forest terrains. To address these challenges, this study proposes a novel forest fire detection model utilizing audio classification and machine learning. We developed an audio-based pipeline using real-world environmental sound recordings. Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network (CNN), enabling the capture of distinctive fire acoustic signatures (e.g., crackling, roaring) that are minimally impacted by… 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 >