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An anonymous and privacy-preserving lightweight authentication protocol for secure communication in UAV-assisted IoAV networks

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computer Communications 2025年 239卷

作者： Shariq, Mohd Jamil, Norziana Rawat, Gopal Singh Chaudhry, Shehzad Ashraf Masud, Mehedi Cangelosi, Angelo Department of Information Systems and Security College of IT UAE University P.O.Box 15551 Al Ain Abu Dhabi United Arab Emirates Bennett University Greater Noida201310 India Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul34398 Turkey Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Manchester Centre for Robotics and AI University of Manchester United Kingdom

With the rapid proliferation of the Internet of Things (IoT), autonomous vehicles (AVs), or self-driving cars, rely heavily on real-time data sharing and message exchanges over wireless networks. AVs use sensors, artificial intelligence, machine learning, and advanced algorithms to perform various functions, enabling users to operate without human intervention. Owing to the flexibility and high mobility of drones, they could aid in the operations of AVs. However, the security and privacy are the main concerns;specifically, the threat of physical capture and violation of anonymity are the main hurdles for realization of secure communication among the AVs and drones. To address these challenges, we propose an anonymous and provably Secure Lightweight Authentication Protocol for unmanned-aerial-vehicle-assisted Internet of Autonomous Vehicles (SLAP-IoAV). The proposed protocol uses cryptographic primitives such as exclusive-OR operations, elliptic-curve cryptography, collision-resistant one-way hashing, and concatenation to ensure robust security. An informal security analysis found that SLAP-IoAV is secure against several known attacks, and a performance analysis established that the protocol has less computational and communication overhead than existing competitive protocols. Additionally, Scyther simulation results confirm that no security vulnerabilities are present. Overall, our protocol delivers superior security and performance, making it well-suited to real-world applications in the AV industry. © 2025 Elsevier B.V.

关键词： Authentication Protocol

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Finger Knuckle Print classification: Leveraging vision Mamba for low complexity and high accuracy

Franklin Open

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Franklin Open 2025年 11卷

作者： Bang, Chiron altaher, ali Salem altaher, Ahmed Moamin, Hawraa alshammari, Thamer alkanjr, Basmh altaher, Hasan al-Jassani, Mohammed G. Zhuang, Hanqi Department of Electrical Engineering and Computer Science Florida Atlantic University 777 Glades Road Boca Raton 33431 FL United States College of Medicine Ibn Sina University of Medical and Pharmaceutical Science Baghdad Iraq Electronic Computer Center Al-Nahrain University Jadriya Baghdad Iraq Ministry of Education Baghdad Iraq Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Al-Jawf Saudi Arabia Department of Computer Science College of Computer and Information Sciences Jouf University Al-Jawf Saudi Arabia Information and Communication Technology Department Baghdad Institute of Technology Middle Technical University Baghdad Iraq

although FKP has been recognized as a viable alternative to other biometric modalities, its adoption remains in the early stages due to its accuracy being lower than that of more competitive options. This research aims to bridge this performance gap by presenting an advancement in the classification of Finger Knuckle Prints (FKPs) using the Vision Mamba (ViM) model. The experimental study, conducted on a dataset from Hong Kong Polytechnic University containing 7,920 images from 165 individuals, evaluated the ViM model's performance against several pretrained classification models. ViM achieved an impressive accuracy of 99.1%, outperforming other models such as alexNet (96.2%), SCNN (98.3%), and EfficientNet (98.0%), highlighting its superior capability in FKP classification. With around 7 million parameters, ViM balances complexity and performance, engineered specifically to capture fine-grained FKP features, such as texture and line patterns. Its use of weight decay mitigates overfitting, and it demonstrates resilience in occlusion scenarios by maintaining performance despite missing FKP components. Spatial attention mechanisms further enhance classification accuracy by prioritizing the most informative regions. Seventeen pretrained deep neural networks were evaluated for their effectiveness in classifying FKPs, with experimental results consistently demonstrating the superior performance of the ViM model. ViM exemplifies an advanced deep learning approach for biometric applications, combining high precision with efficient resource usage. Its versatile design – incorporating bidirectional SSMs for global context modeling and position embeddings for spatial awareness – extends its applicability to visual tasks beyond FKP identification. However, users should take its complexity and resource requirements into account for practical implementation. © 2025 The Authors

关键词： Deep learning Finger Knuckle Print Transfer learning Vision Mamba

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Exploring Speech Biosignatures for Traumatic Brain Injury and Neurodegeneration: Pilot Machine Learning Study

JMIR Neurotechnology

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JMIR Neurotechnology 2025年 4卷

作者： Rubaiat, Rahmina Templeton, John Michael Schneider, Sandra L. De Silva, Upeka Madanian, Samaneh Poellabauer, Christian Knight Foundation School of Computer and Information Sciences Florida International University Miami FL United States Department of Computer Science and Engineering University of South Florida Tampa FL United States Department of Communicative Sciences & Disorders Saint Mary’s College Notre Dame IN United States Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand

Background: Speech features are increasingly linked to neurodegenerative and mental health conditions, offering the potential for early detection and differentiation between disorders. As interest in speech analysis grows, distinguishing between conditions becomes critical for reliable diagnosis and assessment. Objective: This pilot study explores speech biosignatures in two distinct neurodegenerative conditions: (1) mild traumatic brain injuries (eg, concussions) and (2) Parkinson disease (PD) as the neurodegenerative condition. Methods: The study included speech samples from 235 participants (97 concussed and 94 age-matched healthy controls, 29 PD and 15 healthy controls) for the PaTaKa test and 239 participants (91 concussed and 104 healthy controls, 29 PD and 15 healthy controls) for the Sustained Vowel (/ah/) test. Age-matched healthy controls were used. Young age-matched controls were used for concussion and respective age-matched controls for neurodegenerative participants (15 healthy samples for both tests). Data augmentation with noise was applied to balance small datasets for neurodegenerative and healthy controls. Machine learning models (support vector machine, decision tree, random forest, and Extreme Gradient Boosting) were employed using 37 temporal and spectral speech features. A 5-fold stratified cross-validation was used to evaluate classification performance. Results: For the PaTaKa test, classifiers performed well, achieving F₁-scores above 0.9 for concussed versus healthy and concussed versus neurodegenerative classifications across all models. Initial tests using the original dataset for neurodege-nerative versus healthy classification yielded very poor results, with F₁-scores below 0.2 and accuracy under 30% (eg, below 12 out of 44 correctly classified samples) across all models. This underscored the need for data augmentation, which significantly improved performance to 60%‐70% (eg, 26‐31 out of 44 samples) accuracy. In

关键词： alS amyotrophic lateral sclerosis concussion detection diagnosis digital health machine learning mobile device mobile health mobile phone neurodegenerative disease neurological Parkinson's disease speech speech biosignatures speech feature analysis traumatic brain injury

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Quantum-Driven Anomaly Detection Framework for Consumer IoT Cyber-Physical Systems

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IEEE Transactions on Consumer Electronics 2025年

作者： Mahmood, Khalid Khan, Sonia Hassan, Mahmood Ul Awan, Kamran Ahmad Aurangzeb, Khursheed Anwar, Muhammad Shahid Applied College Muhayil Aseer Engineering and Technical Specializations Unit King Khalid University Saudi Arabia Government of Khyber Pakhtunkhwa Department of Youth Affairs Youth Office Haripur Haripur22620 Pakistan Najran University Department of Computer Skills Najran Saudi Arabia University of Haripur Department of Information Technology Hattar Road Khyber Pakhtunkhwa Haripur22620 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11543 Saudi Arabia Department of AI and Software Gachon University Seongnam-si13120 Korea Republic of

This study aims to enhance the security of Consumer IoT (CIoT) systems by addressing the limitations of traditional anomaly detection approaches. To achieve this, we propose the Quantum-Driven Adaptive Anomaly Detection Framework (QADAPT), a novel model designed to enable real-time anomaly detection through a quantum-inspired adaptive cognitive mapping function. The framework is built upon a multilayered architecture consisting of a Quantum-State Convolutional Layer, Synthetic Verification Layer, and Adaptive Mapping Layer, allowing simultaneous data state analysis and validation against synthetic signals. Q-ADAPT uses an adaptive deep learning model to recognize evolving CIoT behavior patterns, enhancing detection accuracy and resilience under varying noise conditions. The simulation environment spans a time frame of 340 minutes, designed to evaluate the robustness of the model in six distinct scenarios under Gaussian noise. Performance results reveal that Q-ADAPT achieves a detection accuracy of 97.8% in lowcomplexity environments and maintains 91.3% under high-noise conditions. © 1975-2011 IEEE.

关键词： Quantum computers

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P2P trading and Energy storage technologies for mitigating balancing challenges of RESs integration into multi-energy power systems: A new paradigm for DER integration

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Journal of Energy Storage 2025年 124卷

作者： Li, Yong Shaban, Mohamed Salih, Sinan Q. almadhor, Ahmad Aoudia, Mouloud Abdullaeva, Barno alkhalaf, Salem albalawi, Hind Mahariq, Ibrahim alalawi, Amr School of Business Shanghai Normal University Tianhua College Jiading Destrict Shanghai201815 China Department of Physics Faculty of Science Islamic University of Madinah Madinah42351 Saudi Arabia Technical College of Engineering Al-Bayan University Baghdad10011 Iraq Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Saudi Arabia Department of Industrial Engineering College of Engineering Northern Border University P.O. Box 1321 Arar91431 Saudi Arabia Department of Mathematics and Information Technologies Vice-Rector for Scientific Affairs Tashkent State Pedagogical University Tashkent Uzbekistan Department of Computer Engineering College of Computer Qassim University Buraydah Saudi Arabia Department of Physics College of Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia University College Korea University Seoul02481 Korea Republic of Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematical Science College of Engineering University of Business and Technology Jeddah21361 Saudi Arabia

Renewable energy (RES) integration is essential for sustainability but poses grid-balancing challenges. Shifting RES uncertainty to the gas grid can facilitate balancing issues, though it raises emissions. Energy storage (e.g., power to hydrogen gas (P2G), batteries) aids balancing but requires coordinated controlling action. Peer-to-peer (P2P) markets enable local distributed energy resource (DER) integration yet must address grid constraints, such as losses, voltage stability, and emissions, to ensure cost-effective, optimal performance. In response to these concerns, this research presents a multi-objective decentralized P2P energy trading framework to facilitate local energy trading in the modern integrated energy microgrid (IEM). The proposed framework consists of two levels: the scheduling stage and the energy trading stage. At the first level, the energy scheduling of the studied IEM's customers is determined through multi-objective optimization, considering the minimization of operating costs, carbon emission, and energy loss as three objective functions. Based on the first-level results, customers, P2Gs, gas-fired generators, RESs, and prosumers initiate energy trading in the second level. The proposed multi-objective optimization problem is solved through the Ε-constraint method, and the P2P decentralized energy market is performed through a continuous double auction mechanism (CDAM). also, the first level of the presented method is equipped with the TOPSIS framework to choose the optimum solution from the Pareto front. The proposed method is implemented on the 33-buse distribution power grid and 15-node natural gas network. The simulation results show that employing the proposed method reduces total operating cost, peak load, carbon emission, and energy loss by 20 %, 21 %, 26 %, and 58.3 % compared to without the presented optimization method. also, the results of the Ε-constraint method are compared with the widely used NSGA-II framework. The outcomes de

关键词： Battery management systems

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Designing artificial neural network of hydromagnetic bioconvection flow of micropolar nanofluid with gyrotactic microorganism past an inclined stretching sheet in a porous medium

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ZAMM Zeitschrift fur Angewandte Mathematik und Mechanik 2025年第5期105卷

作者： Khan, Zeeshan Chammam, Wathek ali, Aatif Meetei, Mutum Zico Islam, Saeed Department of Mathematics Abdul Wali Khan University Mardan Mardan Pakistan Department of Mathematics College of Science At Zulfi Majmaah University Al Majmaah Saudi Arabia School of Mathematical Sciences Jiangsu University Jiangsu Zhenjiang China Department of Software Engineering Faculty of Science & Technology ILMA University Karachi Pakistan Department of Mathematics College of Science Jazan University Jazan Saudi Arabia

The investigation of the thermal conductivity of nanofluids has been the central objective of an extensive number of scholars. The objective of this research is to examine the efficacy of artificial neural networks (ANNs) in predicting the heat and mass transport phenomena that occur during the bioconvection of micropolar nanofluids through a porous medium featuring a vertical stretching surface. The flow is influenced by thermal diffusion, chemical reaction, and heat generation/absorption. Buongiorno's model is successfully implemented for nanofluid phenomena, with an emphasis on Brownian motion and thermophoresis. Bioconvection pertains to gyrotactic unicellular organisms residing in aqueous environments, characterized by sporadic movement in the direction of gravity facilitated by indistinct horizontal verticilli. The discharge of micropolar fluid is symmetrical. In order to simplify the governing equations into a non-linear differential system, self-similar variables are introduced. These systems are then solved using neural networks based on the Levenberg–Marquardt (LM) algorithm in order to address the flow problem. The LM technique is employed in conjunction with the bvp4c numerical method to generate a dataset that encompasses various flow parameter scenarios. The Fitness summary, state transition, histogram error, and regression presentation are all further pieces of evidence that demonstrate the resilience and consistency of the LM solver. The strength of the suggested technique, where the accuracy level is attained in the ranges from 10−8 to 10−2, is shown by the strong convergence between the reference solutions and the solutions generated by adding the effectiveness of a constructed solver LMB-NN. © 2025 Wiley-VCH GmbH.

关键词： Thermophoresis

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Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

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computers, Materials & Continua 2023年第4期75卷 1253-1269页

作者： Waleed aldosari Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

This paper presents a machine-learning method for detecting jamming UAVs and classifying nodes during jamming attacks onWireless Sensor Networks(WSNs).Jamming is a type of Denial of Service(DoS)attack and intentional interference where a malicious node transmits a high-power signal to increase noise on the receiver side to disrupt the communication channel and reduce performance *** defend and prevent such attacks,the first step is to detect *** current detection approaches use centralized techniques to detect jamming,where each node collects information and forwards it to the base *** a result,overhead and communication costs *** this work,we present a jamming attack and classify nodes into different categories based on their location to the jammer by employing a single node *** a result,we introduced a machine learning model that uses distance ratios and power received as features to detect such ***,we considered several types of jammers transmitting at different power levels to evaluate the proposed metrics using *** a detection accuracy of 99.7%for the k-nearest neighbors(KNN)algorithm and average testing accuracy of 99.9%,the presented solution is capable of efficiently and accurately detecting jamming attacks in wireless sensor networks.

关键词： Jamming attacks machine learning unmanned aerial vehicle(UAV) WSNs

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Recent developments in the thermal radiative flow of dusty Ellis trihybrid nanofluid with activation energy using Hamilton-Crosser thermal conductivity model

International Journal of Thermofluids

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International Journal of Thermofluids 2025年 27卷

作者： Okasha, Mostafa Mohamed Abbas, Munawar Akgül, ali Formanova, Shoira Ibrahim, Talib K. Hassani, Murad Khan Department of Mechanical Engineering College of Engineering Northern Border University Arar Saudi Arabia Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai602105 India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai India Siirt University Art and Science Faculty Department of Mathematics Siirt56100 Turkey Department of Computer Engineering Biruni University Topkapı Istanbul34010 Turkey Department of Chemistry and Its Teaching Methods Tashkent State Pedagogical University Tashkent Uzbekistan Department of Petroleum Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Petroleum Engineering Al-Kitab University Altun Kupri Iraq Ghazni University Department of Mathematics Afghanistan Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Nicosia /Mersin 10 Turkey

This study scrutinizes the characteristics of activation energy on Darcy Forchheimer radiative flow of dusty Ellis trihybrid nanofluid over a Riga plate when dust and nanoparticles are present. The goal of the present work is to use the Hamilton-Crosser thermal conductivity model to scrutinize the heat transmission for the Darcy Forchheimer flow of dusty Ellis trihybrid nanofluid. The flow is impacted by heat source with the properties of Marangoni convection. The base fluid, propylene glycol (C3H8O2), is mixed with Ag, TiO2 and al2O3 nanoparticles. The model is applicable to sophisticated heat transfer systems, including solar energy harvesting and electronic device cooling technologies. Additionally, it finds application in thermal management of industrial processes using nanofluids and aerospace engineering. Using the shooting technique, the numerical results of the governing equations are obtained (RKF-45th). The impacts on dimensionless physical quantities of interest of geometrical and physical properties relevant to this study are analysed using the required tables and figures. The results demonstrated that the Ellis fluid parameter raised the heat transmission, mass transmission rate, and velocity profiles. As the chemical reaction parameter upsurges, the concentration distributions decrease. © 2025 The Author(s)

关键词： Activation energy

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Optimal energy generation of hybrid energy systems considering economic and environmental multi-objective functions

Science and Technology for Energy Transition (STET)

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Science and Technology for Energy Transition (STET) 2025年 80卷

作者： Raman, Arasu Sayed, Biju Theruvil alkhayyat, Ahmad Ved, Amit Chetan, M. Singh, Ashish Kumar, Raj Prasad, K.D.V. Sapaev, I.B. Mohammedi, M. Department of Faculty of Business and Communications Inti International University Putra Nilai 71800 Malaysia Department of Computer Science Dhofar University PO Box 2509 Salalah211 Oman Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Babylon Babylon Iraq Marwadi University Research Centre Department of Electrical Engineering Faculty of Engineering and Technology Marwadi University Gujarat Rajkot360003 India Karnataka Bangalore India Nims School of Electrical and Electronics Engineering Nims University Rajasthan Jaipur India Chandigarh College of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali140307 India Pune India Department Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan University of Tashkent for Applied Sciences Str. Gavhar 1 Tashkent100149 Uzbekistan Western Caspian University Baku Azerbaijan Department of Electrical Engineering Islamic Azad University Branch of Central Tehran Tehran Iran

This research is dedicated to exploring and identifying the most effective design for an energy source tailored specifically to meet the electricity demands of a residential community. In an era where energy efficiency and sustainability are paramount, this study emphasizes the importance of technical and economic considerations in energy sourcing. It posits that any viable solution must not only be efficient in its energy production and consumption but also reliable in its delivery and financially feasible for the residents who will depend on it. To address this multifaceted challenge, the study proposes the innovative use of a rotation-invariant coordinate convolutional neural network in conjunction with binary battle royale optimization techniques. These advanced methodologies are selected for their potential to enhance the modelling and optimization processes involved in energy source design. The primary goal of employing these methods is to minimize two critical factors: the net present cost of the energy system and the overall energy cost incurred by the residents. By focusing on these objectives, the research aims to ensure that the proposed energy solutions are not only cost-effective but also sustainable over the long term. To rigorously test the proposed model and evaluate its performance, the research is conducted using the MATLAB platform. The study employs established methodologies and performance metrics to assess the outcomes of the model, ensuring that the findings are both credible and applicable to real-world scenarios. Through comprehensive testing and detailed analysis, this research aims to provide significant insights and actionable recommendations for the optimal design of energy sources in residential areas. By contributing to the ongoing discourse on sustainable energy solutions, the study seeks to inform policymakers, energy planners, and community stakeholders about effective strategies for meeting residential energy demands while promotin

关键词： Clean energy

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A Chaotic Boost: The Chaotic Crayfish Optimization algorithm for Superior Solution Quality

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Optimal Control Applications and Methods 2025年

作者： Maiti, Binanda Biswas, Saptadeep Bera, Uttam Kumar Deb, Madhujit Jia, Heming Saleem, Kashif Migdady, Hazem Smerat, Aseel Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura India Department of Mechanical Engineering National Institute of Technology Agartala Tripura India School of Information Engineering Sanming University Sanming China Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh Saudi Arabia CSMIS Department Oman College of Management and Technology Barka Oman Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India Faculty of Educational Sciences Al-Ahliyya Amman University Amman Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Computer Science Department Al al-Bayt University Mafraq Jordan

This study introduces the Chaotic Crayfish Optimization algorithm (CCOA), an advanced variant of the Crayfish Optimization algorithm (COA) that integrates chaotic maps to enhance its performance in solving complex global optimization and engineering problems. The COA, inspired by the foraging behaviour of crayfish, has demonstrated effectiveness but is challenged by issues such as an imbalance between exploration and exploitation, a tendency to get trapped in local optima, and slower convergence rates in high-dimensional landscapes. By incorporating chaotic dynamics, the CCOA addresses these limitations, improving the algorithm's ability to navigate diverse regions of the solution space and refine promising solutions. The CCOA employs ten distinct chaotic maps to dynamically adapt its search strategies dynamically, optimizing the exploration-exploitation balance. The algorithm is rigorously evaluated against established benchmark test functions from recognized competitions, including CEC 2014, CEC 2017, CEC 2020, and CEC 2022, to assess its effectiveness in finding optimal solutions. A comprehensive comparative analysis is conducted against various well-known optimization algorithms, including Particle Swarm Optimization, Differential Evolution, and the traditional COA, among others. Statistical significance is established through average ranking and Wilcoxon Rank Sum Test evaluations. Additionally, the CCOA is applied to six real-world engineering design problems, such as the Welded Beam Design Problem and the Cantilever Beam Design Problem, demonstrating its practical applicability and effectiveness. The results indicate that the CCOA significantly enhances convergence speed and solution quality while effectively escaping local optima, establishing it as a robust tool for addressing a wide range of optimization challenges. This work contributes to the expanding field of metaheuristic optimization, showcasing the potential of chaotic maps to improve algorithmic per

关键词： Particle swarm optimization (PSO)

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