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arXiv 2025年

作者： Li, Xiaolong Wei, Jianhao Wang, Haidong Dong, Li Chen, Ruoyang Yi, Changyan Cai, Jun Niyato, Dusit Shen, Xuemin The Xiangjiang Laboratory Hunan Provincial General University Key Laboratory of IoT Intelligent Sensing and Distributed Collaborative Optimization Hunan University of Technology and Business Changsha410205 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China The Department of Electrical and Computer Engineering University of Concordia Montreal Canada The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada

In intelligent transportation systems (ITSs), incorporating pedestrians and vehicles in-the-loop is crucial for developing realistic and safe traffic management solutions. However, there is falls short of simulating complex real-world ITS scenarios, primarily due to the lack of a digital twin implementation framework for characterizing interactions between pedestrians and vehicles at different locations in different traffic environments. In this article, we propose a surveillance video assisted federated digital twin (SV-FDT) framework to empower ITSs with pedestrians and vehicles in-the-loop. Specifically, SVFDT builds comprehensive pedestrian-vehicle interaction models by leveraging multi-source traffic surveillance videos. Its architecture consists of three layers: (i) the end layer, which collects traffic surveillance videos from multiple sources;(ii) the edge layer, responsible for semantic segmentation-based visual understanding, twin agent-based interaction modeling, and local digital twin system (LDTS) creation in local regions;and (iii) the cloud layer, which integrates LDTSs across different regions to construct a global DT model in realtime. We analyze key design requirements and challenges and present core guidelines for SVFDT’s system implementation. A testbed evaluation demonstrates its effectiveness in optimizing traffic management. Comparisons with traditional terminal-server frameworks highlight SV-FDT’s advantages in mirroring delays, recognition accuracy, and subjective evaluation. Finally, we identify some open challenges and discuss future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Towards Smarter Supply Chains: Machine Learning Methodologies for Delivery Risk Assessment

Towards Smarter Supply Chains: Machine Learning Methodologie...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad A Al Khaldy Feras Alnaimat Ahmad Al-Qerem Ali Mohd Ali Mohammed Rajab Business Intelligence Data Analytics University of Petra Amman Jordan Medical Engineering Faculty of Engineering Al-Ahliyya Amman University Amman Jordan Computer Science Department Zarqa University Jordan College of Engineering Technology Al-Balqa Applied University Jordan University Headquarter University Of Anbar Ramadi Anbar Iraq

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Supply chain management (SCM) has evolved into a core strategic component for enterprises seeking to gain a competitive edge in an increasingly interconnected global marketplace. With the ever-growing complexity and volume of data available-from procurement and production to distribution and last-mile delivery-organizations are turning to machine learning (ML) and big data analytics to optimize performance and stream-line operations. In this paper, we explore how integrating machine learning into various stages of the supply chain can unlock efficiencies, reduce delivery risks, and enable more informed, data-driven decisions. We employ the dataCo Global Supply Chain dataset as a case study to illustrate how ML classifiers, including K-Nearest Neighbors (KNN), Logistic Regression, Naive Bayes, and Random Forest, can be leveraged to predict late deliveries. Our results show that Random Forest achieved the highest accuracy (over 93%), demonstrating its superior ability to identify at-risk shipments. Furthermore, we emphasize the role of data exploration, feature engineering, and the critical need for addressing data imbalance in ensuring robust model performance. The discussion underscores the transformative potential of ML for improving supply chain efficiency, resilience, and strategic decision-making, while also highlighting future directions to handle evolving customer demands and emerging challenges in an era of rapidly expanding data.

关键词： Technological innovation Accuracy Costs Supply chains Customer satisfaction Nearest neighbor methods Big data Turning data models Random forests

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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Mathematical analysis and stability of thermally magnetized ternary hybrid nanofluid over porous moving wedge with suction/injection and entropy generation

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Results in engineering 2025年 26卷

作者： Zeeshan Khan, Waris Kouki, Marouan Boukhris, Imed Arishi, Ali Garalleh, Hakim AL Safi, Mohammad Sediq Department of Mathematics and Statistics Bacha Khan University Charsadda KP Pakistan Department of Mathematics and Statistics Hazara University KP Mansehra21120 Pakistan Department of Information System Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Department of Physics Faculty of Science King Khalid University AbhaP.O. Box 960 Saudi Arabia Department of Industrial Engineering King Khalid University Abha61421 Saudi Arabia Department of Mathematical Science College of Engineering University of Business and Technology Jeddah21361 Saudi Arabia Department Computer Science Allamah University Kabul Afghanistan

Present study investigates the ternary hybrid nanofluid to analyze thermally magnetized flow over a moving wedge with wall permeability and convective boundary conditions. Using stability analysis and suction and injection give the novel contributions to the study. Water host fluid containing titania, silver, and graphene nanoparticles is used. A self-similarity approach is applied to convert the Partial Differential Equations into dimensionless Ordinary Differential Equations. Numerical solution has been obtained through bvp4c method. Duality has been observed due to suction and injection. From the stability analysis it is perceived that first solution is stable while the second solution is unstable. The impact of embedded factors like magnetic factor, wedge angle parameter Prandtl and Eckert numbers, Suction parameter, and thermal radiation on flow characteristics, temperature distribution, and the entropy generation are demonstrated. Entropy generation is compared for the convectional fluid, nanofluid, hybrid nanofluid, and THNF. Similarly, entropy generation for the downhill flow (m=−0.8), flat flow (m=0), Wedge flow (m=0.2), and stagnation flow (m=1) has been investigated for both suction (S=1) and injection (S=−1) and it has been observed that entropy generation is higher fir the THNF compared to other flows. Additionally, for confirmation, the present study is compared with published work. © 2025 The Author(s)

关键词： Heat radiation

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Predictive Analytics for Ear Health Management Using IoT and Ensemble Learning

Predictive Analytics for Ear Health Management Using IoT and...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： S.K. Saravanan Palaniraj Rajidurai Parvathy R. Meenakshi Prabu K T. R. GaneshBabu B Meenakshi Department of Big Data and Network Security Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Mphasis Corporation Chandler Arizona USA Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Gojan School of Business and Technology Redhills Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Proactive health monitoring solutions are becoming more important due to a growing population of older people. To better manage the ear health of the elderly, this research investigates ensemble learning methods within the domain of predictive analytics using the Internet of Things (IoT). It provides an ensemble learning approach that uses IoT capabilities to predict potential ear health problems in this population. It evaluates the efficacy of many ensembles learning approaches, including bagging, boosting, and stacking, using real-world sensor data acquired from IoT devices. Our results show that ensemble learning methods considerably improve prediction accuracy compared to standalone models. Ensemble learning overcomes the limitations of individual algorithms and maximizes their combined prediction potential by efficiently combining varied models. Our findings have important implications for the management of healthcare for the elderly, providing new opportunities to enhance early diagnosis and intervention techniques for ear health concerns via the use of predictive analytics provided by the IoT. It highlights the potential of ensemble learning approaches to improve predictive analytics in the senior healthcare sector, especially with IoT-driven solutions.

关键词： Accuracy Stacking Ear Medical services Predictive models Ensemble learning Internet of Things Older adults Predictive analytics Monitoring

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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Learnable Operational Design Condition Monitor for Failure Prediction in Autonomous Driving

SSRN

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SSRN 2025年

作者： Xu, Haowei Bian, Yougang Li, Yang Qin, Hongmao Zhou, Hanchu Chang, Fangrong Wang, Shaofei Ye, Qing State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China Research and Development Center of Transport Industry of Self-driving Technology China Merchants Chongqing Communications Technology Research and Design Institute Co. Ltd. Chongqing China School of Traffic and Transportation Engineering Central South University Changsha China Department of Data and System Engineering The University of Hong Kong Hong Kong School of Resources and Safety Engineering Central South University Changsha China School of Data Science China

Ensuring the safety of Autonomous Driving systems (ADS) across diverse and unpredictable operational scenarios remains a significant challenge for accident prevention. Central to this effort is the continuous monitoring of Operational Design Conditions (ODC), which encompass both in-vehicle factors and external Operational Design Domain (ODD). However, the absence of robust methodologies to precisely identify ODC boundaries hinders the deployment and adoption of high-level ADS. This paper introduces a learnable ODC monitor designed to identify implicit operational design condition boundaries to prevent ADS failures. By integrating a state-space duality encoder and a Bayesian neural network decoder, we propose an efficient learnable ODC monitor, termed ODCNet. Furthermore, to further enhance prediction reliability, residual error analysis with linear discrimination is applied for calibrating prediction. Experimental results demonstrate ODCNet's superiority in failure prediction compared to baseline models, achieving a balance between safety and coverage. These findings contribute to advancing data-driven accident prediction and prevention strategies, ensuring safer deployments of ADS in complex environments. © 2025, The Authors. All rights reserved.

关键词： Autonomous vehicles

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An efficient binary salp swarm algorithm for user selection in multiuser MIMO antenna systems

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Scientific Reports 2025年第1期15卷 1-17页

作者： Sasikumar, A. Ravi, Logesh Devarajan, Malathi Almazyad, Abdulaziz S. De, Shuvodeep Xiong, Guojiang Mousavirad, Seyed Jalaleddin Mohamed, Ali Wagdy Department of Data Science and Business Systems Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Centre for Advanced Data Science Tamil Nadu Chennai 600127 India School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Chennai 600127 India School of Computer Science and Engineering Vellore Institute of Technology Chennai 600127 India Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia Virginia Tech Blacksburg United States Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical Engineering Guizhou University Guiyang 550025 China Department of Computer and Electrical Engineering Mid Sweden University Sundsvall Sweden Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza 12613 Egypt Applied Science Research Center Applied Science Private University Amman 11931 Jordan Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

The past ten years have seen notable research activity and significant advancements in multiuser multiple-input multiple-output (MU-MIMO) antennas. An MU-MIMO antenna system must accommodate many subscribers without additional bandwidth or energy. User scheduling becomes a critical strategy to take advantage of multiuser heterogeneity and acquire maximum gain in systems where the total number of recipients exceeds the number of transmitting antennas. Due to their high computational cost, many user selection methods currently in use, such as greedy algorithms and exhaustive search are unsuitable for MU-MIMO systems. A suitable scheduling mechanism is essential for the various users in an MU-MIMO system to utilise bandwidth and enhance the system’s total rate effectively. In this article, we proposed a user and antenna scheduling with a population-based meta-heuristic approach, namely the binary salp swarm algorithm (binary SSA), to increase the system sum rate with low computing complexity. We specifically used a population-based meta-heuristics optimisation technique to simulate the user scheduling problem in MU-MIMO systems, characterising complicated issues with binary decisions. Additionally, binary SSA significantly outperforms existing population-based models, such as the binary bat algorithm (binary BA), PSO, SSA, FPA and binary flower pollination algorithm (binary FPA), regarding system throughput/sum rate. The proposed binary SSA technique also effectively achieves a system sum rate compared to a random search scheme and other existing suboptimal scheduling methods. Compared to binary BA and binary FPA approaches, the binary SSA has a higher convergence rate and superior searching capabilities. The simulation outcomes show the proposed binary SSA-based scheduling scheme delivers noticeable performance benefits. © The Author(s) 2025.

关键词： Antenna Binary salp swarm algorithm Metaheuristics optimization Multiuser MIMO User scheduling

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Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0

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Computers, Materials and Continua 2025年第3期83卷 3675-3712页

作者： Bisni Fahad Mon Mohammad Hayajneh Najah Abu Ali Farman Ullah Hikmat Ullah Shayma Alkobaisi Computer & Network Engineering United Arab Emirates University Al Ain 15551 United Arab Emirates Big Data Analytics Centre United Arab Emirates University Al Ain 15551 United Arab Emirates Department of Business Information Technology Liwa College Abu Dhabi 41009 United Arab Emirates Information System & Security United Arab Emirates University Al Ain 15551 United Arab Emirates

In this paper, we explore the ever-changing field of Digital Twins (DT) in the Industrial Internet of Things (IIoT) context, emphasizing their critical role in advancing Industry 4.0 toward the frontiers of Industry 5.0. The article explores the applications of DT in several industrial sectors and their smooth integration into the IIoT, focusing on the fundamentals of digital twins and emphasizing the importance of virtual-real integration. It discusses the emergence of DT, contextualizing its evolution within the framework of IIoT. The study categorizes the different types of DT, including prototypes and instances, and provides an in-depth analysis of the enabling technologies such as IoT, Artificial Intelligence (AI), Extended Reality (XR), cloud computing, and the Application Programming Interface (API). The paper demonstrates the DT advantages through the practical integration of real-world case studies, which highlights the technology’s exceptional capacity to improve traceability and fault detection within the context of the IIoT. This paper offers a focused, application-driven perspective on DTs in IIoT, specifically highlighting their role in key production phases such as designing, intelligent manufacturing, maintenance, resource management, automation, security, and safety. By emphasizing their potential to support human-centric, sustainable advancements in Industry 5.0, this study distinguishes itself from existing literature. It provides valuable insights that connect theoretical advancements with practical implementation, making it a crucial resource for researchers, practitioners, and industry professionals.

关键词： Digital twin in industry integration of DT in IIoT artificial intelligence AI-Driven DT applications

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