Shounak Mandal¹, Subhadip Pati^1,#, Nirmallyadeb Ray^1,#, Bipasha Guha Roy^2,#, Priyanka Saha³, Deepsubhra Guha Roy^2,*

Digital Engineering and Digital Twin, Vol.4, pp. 27-47, 2026, DOI:10.32604/dedt.2026.081859 - 14 August 2026

Abstract HealthyBrain is a scalable, interoperable, and intelligent Remote Patient Monitoring (RPM) platform built on Internet of Things (IoT) technologies and a modular microservices architecture. The system integrates wearable IoT devices, MQTT (Message Queuing Telemetry Transport)-based lightweight messaging, and high-throughput real-time data streaming via Apache Kafka. Edge-side preprocessing enables low-latency analytics, while machine learning-based anomaly detection models facilitate early identification of critical health events. To ensure clinical interoperability, the platform adheres to the HL7 FHIR (Fast Healthcare Interoperability Resources) standard for electronic health record exchange. The system’s novel contribution lies in the unified integration of edge… More >

Zulfiqar Ali, Azhar Mahmood^*, Shaheen Khatoon, Seyed Ali Ghorashi

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084386 - 13 August 2026

Abstract Smart city systems increasingly depend on data analytics and visualization to support informed and timely decision making in complex urban environments. However, existing middleware solutions predominantly focus on data acquisition and communication, while analytical processing and visualization are typically delegated to external applications, resulting in increased development complexity, reduced reusability, and fragmented system architectures. This study presents analytics and visualization-centric middleware named “Service-Oriented Middleware for Smart City Applications” (SOMSCA), in which these capabilities are embedded directly within the middleware layer. SOMSCA adopts a service-oriented approach, exposing analytics and visualization functionalities as reusable platform services, and… More >

Md. Fahmid-Ul-Alam Juboraj¹, Fahmid Al Farid^2,3, Mahe Zabin⁴, Jia Uddin⁵, Muhammad Iqbal Hossain^1,*, Sarina Mansor^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081254 - 27 July 2026

Abstract The integration of Internet of Things (IoT) technologies in agriculture enables precision farming but introduces significant cybersecurity vulnerabilities. This paper presents FICNet (Feature Integrated Convolutional Network), a lightweight deep learning architecture for intrusion detection in agricultural IoT environments. Evaluated on the Farm-Flow AG-IoT security dataset, FICNet achieves 100% binary classification accuracy and 81.25% multiclass accuracy (macro F1: 80.43%, precision: 91.26%, ROC-AUC: 96.78%) across 8 traffic categories. A multi-dimensional component analysis confirms the contribution of each architectural component: multi-scale convolutions provide 5.3% noise robustness advantage, squeeze-and-excitation attention controls per-class detection trade-offs, and the full architecture achieves More >

Mohammad Ebrahimishadman, Alireza Souri^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084246 - 23 July 2026

Abstract Automatic Number Plate Recognition (ANPR) is widely used in Intelligent Transportation Systems (ITS) and smart parking applications, but running deep learning-based ANPR directly on low-power edge devices remains difficult because of computation time, memory, and latency limitations. In this study, we develop an edge-oriented ANPR pipeline for an Internet of Things (IoT)-based sensor-triggered stop-and-go smart parking platform, targeting deployment on a resource-constrained edge device. The pipeline combines YOLOv8 for license plate detection, PaddleOCR for text recognition, and a rule-based normalization stage to reduce Optical Character Recognition (OCR) errors caused by spacing inconsistencies and plate-format variations.… More >

Amir Ijaz^*, Hashem Haghbayan, Abdul Malik, Ethiopia Nigussie, Juha Plosila

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083797 - 23 July 2026

Abstract The exponential growth of the Internet of Things (IoT) has led to an urgent need for highly energy-efficient communication strategies, especially for battery-powered or self-sustaining devices. In this work, we present a comprehensive framework for minimizing communication energy in IoT nodes operating in swarm robotic systems. We examine and integrate multiple low-power wireless technologies (BLE, LoRaWAN, MQTT, CoAP) with advanced Medium Access Control (MAC) protocols. We additionally propose adaptive scenarios leveraging both ambient energy harvesting and passive backscatter transmission. Our solution employs adaptive scheduling and dynamic transmission power management. Specifically, a Deep Q-Learning (DQL) agent More >

Abdulaziz A. Alsulami¹, Qasem Abu Al-Haija^2,*, Rayed Alakhtar³, Ahmad J. Tayeb³, Badraddin Alturki³, Huda Alsobhi⁴, Rayan A. Alsemmeari³

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083321 - 23 July 2026

Abstract The rapid growth of the Internet of Things (IoT) devices has increased the attack area of modern networks, which makes effective intrusion detection systems (IDSs) essential to detect attacks that target IoT infrastructures. Federated learning is a promising approach for collaborative model training in the absence of centralized raw data. Conventional federated approaches rely on fixed client participation and static training configurations, which ensure symmetric treatment of clients despite heterogeneous local data distributions. This can limit convergence and degrade detection performance in non-IID conditions. This paper proposes an Adaptive Action-Based Federated Learning (AA-FL) framework for… More >

Jackson Diaz-Gorrin^1,*, Candido Caballero-Gil¹, Pino Caballero-Gil¹, Joanna Kolodziej^2,3

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082207 - 23 July 2026

Abstract A lightweight flow-based intrusion detection system is proposed for identifying Mirai-based distributed denial-of-service attacks in Internet of Things (IoT) environments. Efficient intrusion detection at the network edge is essential for resource-constrained IoT deployments, where devices operate with limited processing, memory, and energy resources, making centralized or computationally intensive solutions impractical in real-world scenarios. Network traffic is represented using statistical and temporal features extracted from unidirectional flows constructed from the TII-SSRC-23 dataset. A balanced subset of 10,000 samples is used for training and evaluation, ensuring balanced data distribution and improving generalization across different traffic conditions. Three… More >

Saken Tleuberdin¹, Dina Satybaldina^2,*, Raikhan Muratkhan³, Gulsipat Abisheva^2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081906 - 23 July 2026

Abstract We perform a cross-layer penetration testing on one of the most popular Wi-Fi smart locks (Tuya 902V). The methodology combines wireless traffic analysis using an Alfa AWUS036AXML adapter, forced re-association via deauthentication to make Wi-Fi Protected Access 2 (WPA2) 4-way Extensible Authentication Protocol over LAN (EAPOL) handshake visible with Airodump/Aireplay, offline dictionary attack with Aircrack-ng, Android app reverse engineering using Apktool, Jadx, and MobSF; denial-of-service experiment (DoS) executed by hping3; Near-Field Communications (NFC)/Radio-Frequency Identification (RFID) key-clone attempt by Flipper Zero. Handshake is empirically captured but no Wi-Fi passphrase found under 14M dictionary entries; DoS test… More >

Yimeng Liu¹, Xinyu Xu¹, Wangting Xue¹, Shigen Shen^1,*, Xiao-Zhi Gao²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081775 - 23 July 2026

Abstract The rapid growth of the Industrial Internet of Things (IIoT) has become a cornerstone of high-quality global economic development. By integrating sensor networks, edge computing, and cloud intelligence, IIoT has emerged as a key enabler for smart manufacturing and digital transformation across industries. However, this technological advancement introduces significant cybersecurity challenges that render traditional intrusion detection systems inadequate for IIoT environments. To address this critical gap, we propose a deep spiking Q-network (DSQN)-based intrusion detection system (DSQN-IDS) for the IIoT, formulating unknown intrusion detection as a Markov decision process (MDP). The system employs a hierarchical More >

Pardis Sadatian Moghaddam¹, Mahyar Mahmoudi², Nuria Serrano³, Francisco Hernando-Gallego⁴, Diego Martín^3,*, José Vicente Álvarez-Bravo³

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081311 - 23 July 2026

Abstract The rapid proliferation of the Internet of Things (IoT) and cyber-physical systems (CPS) within critical infrastructure sectors has significantly expanded the attack surface for advanced and stealthy cyber threats. Since these systems increasingly rely on real-time data exchange and autonomous control, developing intelligent, scalable, and adaptive anomaly detection mechanisms has become a pressing requirement. This paper proposes a novel hybrid framework, evolutionary-transformer-long short-term memory (Evo-Transformer-LSTM), that integrates the temporal modeling capability of LSTM networks, the global attention mechanism of Transformer encoders, and the optimization power of the improved chimp optimization algorithm (IChOA) for hyper-parameter tuning.… More >