Zhihao Dong^1,2, Huakui Sun^1,2,*, Yueyue Tao², Daosen Zhai²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084301 - 23 July 2026

Abstract High-mobility Unmanned Aerial Vehicle (UAV) swarm networks suffer from fast-varying connectivity and interference, and therefore routing decisions must jointly account for link instability and topology changes. By leveraging mobile edge computing (MEC) capabilities, each UAV can perform online routing decisions locally without relying on centralized controllers. This paper develops a Predictive-Q learning framework for dynamic routing under interference and mobility, where the Q-value is trained by a multi-factor reward that explicitly models retransmission costs, predicts link lifetime from relative motion, and anticipates forward connectivity and neighbor redundancy. To further enhance reliability under harsh interference, we 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 >

MengDie Hu^#, Na Wang^*, XueHui Du^#, BaiDong Huang^#, KaiYuan Wang^#

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082658 - 23 July 2026

Abstract With the rapid development of the Internet of Things (IoT) and edge intelligence, the volume of data generated by edge devices has grown explosively. Federated learning (FL), characterized by the paradigm of “data remaining local while models are shared,” has emerged as a key approach for adapting to the distributed architecture of edge computing, breaking down data silos, and enabling privacy preservation. However, its practical deployment in edge computing environments still faces significant challenges, including limited device resources and pronounced data heterogeneity. Existing pruning strategies for federated learning are predominantly based on static and single-design… 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 >

Atef Ibrahim^1,*, Fayez Gebali²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081651 - 23 July 2026

Abstract The ongoing expansion of the Internet of Things (IoT) fundamentally alters industrial and economic paradigms by integrating intelligent nodes throughout operational frameworks. Nonetheless, vulnerabilities surrounding system integrity and data confidentiality present major bottlenecks to widespread adoption, a dilemma severely intensified by impending quantum computing capabilities. Defending these networks demands the integration of post-quantum cryptographic primitives; yet, the severe hardware constraints characterizing peripheral IoT components complicate practical deployment. Quantum-resistant lattice cryptography offers a highly promising pathway to overcome these limitations, largely because the foundational security and throughput of these protocols hinge on polynomial multiplication performance. Consequently,… More >

Hwa-Young Jeong^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080596 - 23 July 2026

Abstract The rapid growth of electric vehicle (EV) charging infrastructures has introduced new challenges in monitoring abnormal load behaviors under strict latency and resource constraints. Conventional anomaly detection approaches either rely on centralized processing or incur excessive false alarms, limiting their practical applicability in large-scale deployments. This paper proposes a hierarchical edge-fog anomaly detection framework that integrates lightweight edge-level filtering with a fog-level Temporal Convolutional Network (TCN) detector. The edge component suppresses non-informative patterns, while the fog layer performs temporal modeling on selectively forwarded data. This design enables controllable reduction of fog-level processing load. Under corrected… More >

Cesar Guevara^*, Victoria Lopez

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081473 - 30 June 2026

Abstract This study presents a mobile expert system for on-device detection and short-horizon forecasting of aggression using affordable edge hardware. The proposed framework combines lightweight on-body and ambient signals, compact sequential predictors, and an interpretable fuzzy decision layer that converts calibrated probabilities into actionable and auditable alerts. In a subject-held-out pilot study with 10 independent participants, the system achieved a macro-averaged F1 score of 98.3% and an area under the receiver operating characteristic curve of 0.998 on the held-out test split. These results should be interpreted as pilot-scale held-out estimates rather than as definitive evidence of… More >

Hong Min^1,*, Yousef Ibrahim Daradkeh², Jung Taek Seo^3,*, Mohd Anjum⁴, Sana Shahab⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080871 - 30 June 2026

Abstract This study presents a computational modeling framework for efficient and secure computation offloading in Internet of Things (IoT)-enabled smart contract systems. The integration of IoT, edge computing, and blockchain introduces significant challenges, including limited device capacity, high verification cost, and scalability constraints. Existing blockchain verification approaches depend on computationally intensive cryptographic operations that are inefficient for resource-constrained IoT devices, resulting in increased latency, energy consumption, and transaction costs. To address these issues, this study proposes the Zero-Knowledge Fuzzy Logic Offloading and Rollup (Z-FLOR) framework, an adaptive and energy-efficient model designed to enable secure and verifiable… More >

Chengyu Hou^1,2, Wenzao Li², Hanyun Li³, Kui Liu¹, Zhuoning Zhao¹, Hongping Shu^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081575 - 15 June 2026

Abstract The rapid growth of Internet of Things (IoT) and 5G technologies has led to a sharp increase in computing demands from wireless devices, making efficient task offloading a critical challenge. Key issues include reducing application latency, lowering the energy consumption of terminal devices, and improving overall system performance, all of which directly affect user experience. Traditional genetic algorithms (GA), inspired by biological evolution, have been widely used in task offloading, but they often suffer from slow convergence and a tendency to fall into local optima in complex scenarios, limiting their effectiveness. To address these drawbacks,… More >

Hitesh Mohapatra^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080961 - 15 June 2026

Abstract This review systematically analyzes Reinforcement Learning approaches for self-healing in energy-constrained secure edge IoT networks across 82 studies from 2020 to 2026. Unlike existing surveys that focus on general RL applications, the proposed review focuses on a three-level taxonomy that uniquely addresses edge IoT deployment realities through formulation-scope-hardware mapping. The work develops a novel three-level taxonomy classifying recovery scope (node, link, service, network), RL formulations (tabular, deep, multi-agent, model-based), and constraint integration (energy, latency, security, hybrid), revealing service migration dominance at 30% coverage and node recovery achieving 38% maximum energy savings. Normalized performance baselines establish More >