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End-To-End Encryption Enabled Lightweight Mutual Authentication Scheme for Resource Constrained IoT Network

computers, Materials & Continua 2025年第2期82卷 3223-3249页

作者： Shafi Ullah Haidawati Muhammad Nasir Kushsairy Kadir Akbar Khan Ahsanullah Memon Shanila Azhar Ilyas Khan Muhammad Ashraf Department of Computer Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Computer Engineering Section Malaysian Institute of Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Electrical Engineering Section Universiti Kuala LumpurBritish Malaysian InstituteSalengor53100Malaysia Electrical Engineering Department Mehran UniversityKhairpur-Mirs76062Pakistan Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication networks consist of resource-constrained autonomous devices, also known as autonomous Internet of things (IoTs) or machine-type communication devices (MTCDs) which act as a backbone for Industrial IoT, smart cities, and other autonomous systems. Due to the limited computing and memory capacity, these devices cannot maintain strong security if conventional security methods are applied such as heavy encryption. This article proposed a novel lightweight mutual authentication scheme including elliptic curve cryptography (ECC) driven end-to-end encryption through curve25519 such as (i): efficient end-to-end encrypted communication with pre-calculation strategy using curve25519;and (ii): elliptic curve Diffie-Hellman (ECDH) based mutual authentication technique through a novel lightweight hash function. The proposed scheme attempts to efficiently counter all known perception layer security threats. Moreover, the pre-calculated key generation strategy resulted in cost-effective encryption with 192-bit curve security. It showed comparative efficiency in key strength, and curve strength compared with similar authentication schemes in terms of computational and memory cost, communication performance and encryption robustness.

关键词： Mutual authentication lightweight end-to-end encryption elliptic curve cryptography industrial internet of things curve25519 machine-to-machine communication

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Frozen Food Supply Chain Monitoring with IoT 19

Frozen Food Supply Chain Monitoring with IoT

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Mohd Kassim, Mohd Sallehin Rozman, Muhammad Haziq bin Yahya, Faridah Advanced Telecommunication Technology Cluster International Industrial and Institutional Partnership Department Computer Engineering Technology Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia Internet of Thing Computer Engineering Technology Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia Computer Engineering Technology Section Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

This project aimed to implement a prototype capable of real-time monitoring and control of various parameters in information management setting with Internet of Things (IoT). Research methodology involved designing and implementing the system prototype to resemble a supply container or transportation truck with installed sensors controlled by a microcontroller. Main components used temperature sensors (AHT20), RFID reader for access control, GPS module for location tracking, and a web/mobile dashboard for remote monitoring. Additionally, a robust database system was developed to store sensor readings and transaction history, ensuring data integrity and facilitating seamless communication between components for real-time data transmission. Key findings indicate that the system effectively monitors temperature, manages access control through RFID authentication, tracks real-time location via GPS, and provides a user-friendly dashboard interface for data visualization. © 2025 IEEE.

关键词： Access control

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Offsets Reconstruction Method for Clock Skew Measurement Over High-Jitter Communication

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SN computer Science 2025年第5期6卷 1-14页

作者： Sastra, Nyoman Putra Saputra, Komang Oka Teng, Wei-Chung Study Program of Electrical Engineering Faculty of Engineering Udayana University Jalan Raya Kampus Bukit Jimbaran Bali Badung 80361 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology No. 43 Section 4 Keelung Rd Taipei 106 China

Measuring clock skew of devices over a network fully relies on the offsets, the differences between sending and receiving times. Offsets that shape a thick line are the most ideal one as their slope is directly the clock skew sought. When some outliers separate from the thick line due to higher delay jitter, current advanced approaches use the lower bound or the majority of offsets to derive accurate clock skew. However, outliers in offsets that shape parallel dotted lines, reduce the availability of offsets in the lower region, and scatter widely the offsets from their majority zone. These conditions drive the current skew estimation approaches to lose their accuracy. This paper then developed a new method, which reconstructs back the outliers into their origin positions, in order to provide ideal offsets for clock skew measurement. The experimental evaluations have demonstrated that the clock skew accuracy of dotted-lines offsets tampered by outliers was improved from 2.1 to 0.9 ppm by the proposed reconstruction method. For a broader purpose, the reconstruction method has been adjusted to be implemented also on non-dotted-lines offsets. The robustness of the proposed method for this case has also been exhibited by the very ideal reconstructed offsets that shape like a thick line. © The Author(s) 2025.

关键词： Clock skew Delay jitter Dotted lines Hough transform

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Internet of Things-Based Smart Infant-Incubators Using Machine Learning Analysis

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IEEE Access 2025年 13卷 77673-77685页

作者： Gamal, Mahmoud Radi, Ibrahim Yousef, Amr Ali, Ali Gaber Mohamed Alexandria University Faculty of Computers and Data Science Alexandria21544 Egypt Higher Institute of Engineering and Technology Department of Computer Engineering Gharbia Governorate Egypt University of Business and Technology Electrical Engineering Department Jeddah23435 Saudi Arabia Alexandria University Faculty of Engineering Engineering Mathematics Department Alexandria21544 Egypt Electronics Engineering and Communications Faculty of Engineering New Alamein Egypt Alexandria University Faculty of Engineering Department of Electrical Engineering Communications and Electronics Section Alexandria21544 Egypt

Many international manufacturers of infant incubators use IoT technology, presenting tough competition for local manufacturers. This research examines methods to fulfill parents’ need to monitor their babies directly when they are lying inside an incubator. This bridges the gap present in neonatal care units. As such, this study proposes a prototype for an incubator that employs IoT along with different sensors to monitor babies occupying these incubators in real time and send the data to a remote server. With the different technologies used in this monitoring system, parents have the ability to listen to their babies remotely through a mobile application. A convolutional neural network (CNN) algorithm is used to take neonatal care a step further. It then goes beyond monitoring by interpreting the nature of a baby’s cries inside the specified incubator. The proposed prototype represents a step forward in the applications of Industry 4.0 in healthcare, especially those related to infant care and, more specifically, incubators for babies. This research is part of advancements in the use of IoT and handheld applications for the future development of baby incubators, fulfilling the needs of parents to constantly monitor their infants and neonatal care, specifically within modern healthcare. © 2013 IEEE.

关键词： engineering education

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Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks

arXiv

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arXiv 2025年

作者： Orfanoudakis, Stavros Palensky, Peter Vergara, Pedro P. Section Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

Maintaining grid stability amid widespread electric vehicle (EV) adoption is vital for sustainable transportation. Traditional optimization methods and Reinforcement Learning (RL) approaches often struggle with the high dimensionality and dynamic nature of real-time EV charging, leading to sub-optimal solutions. To address these challenges, this study demonstrates that combining Large Language Models (LLMs), for sequence modeling, with Graph Neural Networks (GNNs), for relational information extraction, not only outperforms conventional EV smart charging methods, but also paves the way for entirely new research directions and innovative solutions. © 2025, CC BY.

关键词： Charging stations

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Advancing Blackboard Framework: Responsive Trigger Mechanisms and Dynamic Feasibility Conditions for Optimal Knowledge Source Selection 19

Advancing Blackboard Framework: Responsive Trigger Mechanism...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Muhd Mukhtar, Hana Munira Husin, Husna Sarirah Ismail, Suriana Rahmat, Azizah Ismail, Roslan Software Engineering Section Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia School of Computer Science Taylor’s University Kuala Lumpur Malaysia Centre of Innovative Digital Education & Emerging Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

Expert systems (ES) rely heavily on effective control mechanisms to navigate complex problem-solving tasks. These mechanisms govern how ES utilizes knowledge and data to make decisions and execute actions. Optimizing these mechanisms, or addressing the "control problem," remains a central challenge. Blackboard architectures, with their separation of knowledge and control processes, offer a promising approach. This study introduces an improved control mechanism for blackboard architectures. It focuses on a critical aspect—trigger mechanisms and feasible conditions—for optimal knowledge source selection to participate in the problem-solving process. By leveraging these features, this study aims to enhance decision-making and problem-solving capabilities within complex domains. This paper focuses on the technical details of exploring how it manages the flow of rule activation and execution throughout the problem-solving process. © 2025 IEEE.

关键词： Expert systems

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Detecting Advanced Persistent Threat Exfiltration with Ensemble Deep Learning Tree Models and Novel Detection Metrics

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IEEE Access 2025年 13卷 81803-81822页

作者： Cai, Xiaojuan Zhang, Haibo Ahmed, Chuadhry Mujeeb Koide, Hiroshi Kyushu University Faculty of Information Science and Electrical Engineering Department of Information Science and Technology Fukuoka819-0395 Japan Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Department of Artificial Intelligence Fukuoka Iizuka820-8502 Japan Newcastle University School of Computing Secure and Resilient Systems Group Newcastle upon TyneNE1 7RU United Kingdom Kyushu University Section of Cyber Security for Information Systems Research Institute for Information Technology Fukuoka819-0395 Japan

Advanced Persistent Threats (APTs) involve attackers maintaining a long-term presence on victim systems, leading to the stealthy exfiltration of sensitive data during network transfers. Despite existing methods to detect and halt APT data exfiltration, these attacks continue to pose significant threats to sensitive information and result in substantial commercial losses. Current approaches primarily focus on preemptive measures, which are insufficient once early-stage detection fails due to a lack of continuous monitoring. We propose an effective and efficient network monitoring method to address this gap and detect APT exfiltration during data transfer. Our approach assumes the presence of an undetected APT attacker within the victim system. We examine data exfiltration across three exfiltration traffic environments: exfiltration over command control channels, exfiltration over transfer size limitations, and their combinations. We introduce two detection metrics: Package Transfer Rate and Byte Transfer Rate. Utilizing these metrics, we measure network traffic, categorize APT attack environments, and train deep neural network models, named EDXGB, using ensembled decision trees to predict APT exfiltration. Our method is validated on two public datasets and compared against six baseline methods. Additionally, we simulate real-world exfiltration scenarios by creating three exfiltration traffic environments for each dataset. The results demonstrate that our method effectively detects APT exfiltration across various network environments, enhancing data protection and secure transfer. The code is open source and available at https://***/cxjuan/EDXGB-for-APT. © 2013 IEEE.

关键词： Sensitive data

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A Hybrid Deep Learning Framework for Deepfake Detection Using Temporal and Spatial Features

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IEEE Access 2025年 13卷 79560-79570页

作者： Zafar, Fazeel Khan, Talha Ahmed Akbar, Salas Ubaid, Muhammad Talha Javaid, Sameena Kadir, Kushsairy Abdul Bahria University Karachi Campus School of Engineering and Applied Sciences Department of Computer Sciences Karachi75290 Pakistan Section Malaysian Institute of Information Technology Kuala Lumpur53100 Malaysia University of Central Punjab Department of Information Technology Lahore54000 Pakistan Universiti Kuala Lumpur British Malaysian Institute Kuala Lumpur53100 Malaysia

The rise of deep-fake technology has sparked concerns as it blurs the distinction between fake media by harnessing Generative Adversarial Networks (GANs). This has raised issues surrounding privacy and security in the realm. This has led to a decrease in trust during online interactions;thus, emphasizing the importance of creating reliable methods for detection purposes. Our research introduces a model for detecting deepfakes by utilizing an Enhanced EfficientNet B0 structure in conjunction with Temporal Convolutional Neural Networks (TempCNNs). This approach aims to tackle the challenges presented by the evolving sophistication of deep-fake techniques. The system dissects video inputs into frames to extract features comprehensively by using Multi Test Convolutional Networks (MTCNN). This method ensures face detection and alignment by focusing on facial regions. To enhance the model’s adaptability, to different scenarios and datasets we implement data augmentation techniques such as CutMix, MixUp and Random Erasing. These strategies help the model maintain its strength, against distortions found in deepfake content. The backbone of EfficientNet B0 utilizes Mobile Inverted Bottleneck Convolutions (MBConv) and Squeeze and Excitation (SE) blocks to enhance feature extraction by adjusting channels to highlight details effectively. A Feature Pyramid Network (FPN) facilitates the fusion of scale features capturing intricate details as well, as broader context. When tested on the FFIW 10 K dataset, which comprises 10,000 videos evenly split between manipulated content, the model attained a training accuracy of 91.5 % and a testing accuracy of 92.45 %, after 40 epochs. The findings showcase the model’s proficiency, in identifying videos with precision and tackling the issue of class imbalances found in datasets – a valuable contribution, to advancing dependable deepfake detection solutions. Furthermore, the model achieves an impressive balance between accuracy and computati

关键词： Convolutional neural networks

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Modeling Method for DFIG-Based Wind Farm in High-Efficiency Real-Time Electromagnetic Transient (EMT) Simulations

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IEEE Transactions on Power Electronics 2025年

作者： Liu, Yifan Xu, Jianzhong Zhu, Yiyang Tian, Zhaoxuan Zhao, Chengyong Li, Gen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources China Energy Technology and Computer Science Section Department of Engineering Technology and Didactics Ballerup2750 Denmark

With the increasing integration of renewable energy into power systems, electromagnetic transient (EMT) simulation has become indispensable for accurate system analysis. However, the complexity of wind turbine (WT) modeling, characterized by a large number of electrical nodes, poses significant challenges and necessitates substantial real-time simulation hardware. Existing methods for reducing circuit complexity improve simulation efficiency but are each associated with inherent limitations. Aggregation methods sacrifice considerable internal station information, while existing decoupling techniques are constrained by specific requirements. This paper proposes a real-time simulation model for a Doubly Fed Induction Generator (DFIG)-based wind farm (WF) using latency decoupling and a multi-level nested fast and simultaneous solution (M-NFSS) approach, effectively reducing node count while preserving internal details. A WF test model is implemented in RTDS for validation. Results demonstrate highly accurate impedance characteristics and time-domain waveforms of the proposed model, utilizing only 33.3% of the hardware resources compared to the traditional detailed model. © 1986-2012 IEEE.

关键词： Electromagnetic transients

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Investigating the wound healing potential of low-power 661 nm laser light in a pigmented hairless murine model

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Photochemical and Photobiological Sciences 2025年第5期24卷 779-790页

作者： Statha, Dimitra Sfiniadakis, Ioannis Rallis, Michail Anastassopoulou, Jane Alexandratou, Eleni Laboratory of Biomedical Optics and Applied Biophysics School of Electrical and Computer Engineering National Technical University of Athens Zografou Campus Athens15780 Greece Section of Pharmaceutical Technology Department of Pharmacy National and Kapodistrian University of Athens Panepistimiopolis Zografou Athens15784 Greece Pathologoanatomic Laboratory Athens Naval Hospital Athens11521 Greece

Photobiomodulation (PBM) has emerged as a promising method for enhancing wound healing. However, a standardized therapeutic protocol has not yet been established. This study aimed to determine the optimal irradiation parameters for wound healing in pigmented hairless mice (SKH-hr2). Mice were irradiated daily with energy doses of 2 or 4 J/cm2, achieved with different power densities in each group: 20, 50, or 100 mW/cm2. Various methods were used to evaluate the therapeutic efficacy, including histopathological analysis, clinical observation, photo-documentation, assessment of biophysical skin parameters, and Fourier Transform infrared (FT-IR) spectroscopy. The results indicated that the most favorable outcomes regarding wound healing acceleration and inflammation reduction were achieved with an irradiation setting of 50 mW/cm2 and 2 J/cm2. However, the group subjected to prolonged irradiation times with a power density of 20 mW/cm2 and energy of 4 J/cm2 exhibited subcutaneous bleeding. The FT-IR spectral absorption bands of amide groups provided important molecular-level information about the secondary structure of collagen, particularly in relation to skin regeneration and the response to applied energy, in agreement with histological data. This study highlights the critical need for further investigation into the parameters of photobiomodulation to ensure its effective application to the different skin phototypes and to mitigate potential adverse effects arising from incorrect usage. © The Author(s) 2025.

关键词： Fourier transform infrared spectroscopy

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