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Introduction to the Special Issue on Cutting-Edge Security and Privacy Solutions for Next-Generation Intelligent Mobile Internet Technologies and Applications 认领 引用
1
作者 Ilsun You Gaurav Choudhary +1 位作者 Gökhan Kul Francesco Falmieri 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期34-36,共3页
1 Introduction The growing connectivity with mobile internet has significantly enhanced our day-to-day life support through various services and applications with on-demand availability at any time or anywhere.As emer... 1 Introduction The growing connectivity with mobile internet has significantly enhanced our day-to-day life support through various services and applications with on-demand availability at any time or anywhere.As emerging technologies with continuous revolutions in the digital transformations,various add-on technologies such as quantum computing,AI,and next-generation networks such as 6G are becoming an integral support to mobile internet systems.The emerging technologies in the next-generation mobile internet bring a lot of new security and privacy challenges. 展开更多
关键词 mobile internet emerging technologies next generation networks services applications AI quantum computing quantum computingaiand digital transformationsvarious
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Optimizing Routing Algorithms for Next-Generation Networks:A Resilience-Driven Framework for Space-Air-Ground Integrated Networks 认领 引用
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作者 Peiying Zhang Yihong Yu +3 位作者 Jia Luo Nguyen Gia Ba Lizhuang Tan Lei Shi 《Computers, Materials & Continua》 SCIE EI 2026年第5期1177-1190,共14页
Next-GenerationNetworks(NGNs)demand high resilience,dynamic adaptability,and efficient resource utilization to enable ubiquitous connectivity.In this context,the Space-Air-Ground Integrated Network(SAGIN)architecture ... Next-GenerationNetworks(NGNs)demand high resilience,dynamic adaptability,and efficient resource utilization to enable ubiquitous connectivity.In this context,the Space-Air-Ground Integrated Network(SAGIN)architecture is uniquely positioned to meet these requirements.However,conventional NGN routing algorithms often fail to account for SAGIN’s intrinsic characteristics,such as its heterogeneous structure,dynamic topology,and constrained resources,leading to suboptimal performance under disruptions such as node failures or cyberattacks.To meet these demands for SAGIN,this study proposes a resilience-oriented routing optimization framework featuring dynamic weighting and multi-objective evaluation.Methodologically,we define three core routing performance metrics,quantified through a four-dimensionalmodel,encompassing robustness Rd,resilience Rr,adaptability Ra,and resource utilization efficiency Ru,and integrate them into a comprehensive evaluation metric.In simulated SAGIN environments,the proposed Multi-Indicator Weighted Resilience Evaluation Algorithm(MIW-REA)demonstrates significant improvements in resilience enhancement,recovery acceleration,and resource optimization.It maintains 82.3%service availability even with a 30%node failure rate,reduces Distributed Denial of Service(DDoS)attack recovery time by 43%,decreases bandwidth waste by 23.4%,and lowers energy consumption by 18.9%.By addressing challenges unique to the SAGIN network,this research provides a flexible real-time solution for NGN routing optimization that balances resilience,efficiency,and adaptability,advancing the field. 展开更多
关键词 Space-air-ground integrated network next-generation networks routing optimization resiliencedriven routing dynamic weighting multi-metric assessment
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Structural vaccinology expedites rational design of next-generation vaccines for influenza and respiratory syncytial virus 认领 引用
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作者 Lei Deng Jia-Ye Liu Ya-Xuan Peng 《Virologica Sinica》 SCIE CAS CSCD 2026年第2期261-275,共15页
Vaccination stands as the single most effective and cost-efficient public health intervention in human history,serving as a cornerstone of modern medicine that profoundly transforms global health outcomes.Beyond preve... Vaccination stands as the single most effective and cost-efficient public health intervention in human history,serving as a cornerstone of modern medicine that profoundly transforms global health outcomes.Beyond preventing disease,it acts as a catalyst for equitable socioeconomic development.In recent decades,recurrent seasonal viral outbreaks and sporadic yet catastrophic pandemics have continued to pose challenges to global public health systems.Traditional vaccine technologies,however,not only often fall short in protection efficacy,but also fail to keep pace with the evolving demands of next-generation vaccine development.These scientific gaps have directed cutting-edge research to prioritize critical objectives in terms of enhancing antigen effectiveness,achieving stable pan-protection against diverse variant strains,and strengthening production robustness.The advent of genomics spurred the emergence of reverse vaccinology 1.0,leading to breakthroughs like the MenB vaccine.Today,the advanced reverse vaccinology 2.0 paradigm thoroughly redefines vaccine design process by organically integrating human immunology with state-of-the-art computational protein structure analysis tools.This review explores the transformative shifts in influenza and respiratory syncytial virus vaccine development,along with specific case studies,to deepen understanding of the evolving principles and methodologies in novel vaccine designs and offer strategic insights for addressing emerging infectious pathogens. 展开更多
关键词 Structural vaccinology Reverse vaccinology Structure-based rational design Next-generation vaccines Influenza virus Respiratory syncytial virus(RSV)
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Introduction to the Special Issue on Next-Generation Intelligent Networks and Systems:Advances in IoT,Edge Computing,and Secure Cyber-Physical Applications 认领 引用
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作者 Nishu Gupta Manuel J.C.S.Reis 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期25-28,共4页
The accelerating convergence of intelligent networking paradigms,data-driven modeling,and cyberphysical integration is reshaping the foundations of modern engineering systems.Within this context,this Special Issue of ... The accelerating convergence of intelligent networking paradigms,data-driven modeling,and cyberphysical integration is reshaping the foundations of modern engineering systems.Within this context,this Special Issue of Computer Modeling in Engineering&Sciences(CMES)is devoted to recent advances in next-generation intelligent networks and systems,with a particular emphasis on the synergistic roles of the Internet of Things(IoT),edge computing,and secure cyber-physical applications. 展开更多
关键词 intelligent networking paradigmsdata driven computer modeling internet things edge computing intelligent networking paradigms cyberphysical integration internet things iot edge computingand data driven modeling
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Isolated colonic aspergillosis in an immunocompetent individual diagnosed by metagenomic next-generation sequencing:A case report 认领 引用
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作者 Ting Guo Xian Wang +1 位作者 Shao-Yang Wang Tao-Fa Lin 《World Journal of Gastroenterology》 SCIE CAS 2026年第13期142-150,共9页
BACKGROUND Invasive aspergillosis(IA)is typically considered an opportunistic pulmonary infection in immunocompromised hosts.Its occurrence in immunocompetent individuals,particularly as an isolated infection of the l... BACKGROUND Invasive aspergillosis(IA)is typically considered an opportunistic pulmonary infection in immunocompromised hosts.Its occurrence in immunocompetent individuals,particularly as an isolated infection of the lower gastrointestinal tract,is exceedingly rare.Such cases are frequently misdiagnosed as inflammatory bowel disease or other infectious enteritides,leading to delays in appropriate treatment.To the best of our knowledge,only 2 cases of primary(or isolated)intestinal aspergillosis(PA)have been reported in the English literature.CASE SUMMARY We report a case of a 48-year-old male,who previously underwent thyroidectomy for thyroid cancer but lacked the typical risk factors for IA.He presented to our hospital with refractory diarrhea accompanied by a 6 kg weight loss.Initial colonoscopy revealed erosions in the transverse colon.Routine blood tests and standard microbiological investigations(including bacterial cultures,parasite screening,and tuberculosis testing)were negative.Empirical antibiotic therapy and conventional symptomatic management proved ineffective.Ultimately,metagenomic next-generation sequencing(mNGS)of colonic biopsy tissue detected a relatively high abundance of Aspergillus fumigatus.Diarrhea ceased completely within 48 hours of initiating oral voriconazole therapy.A total treatment course of 12 days was administered,and the patient remained symptom-free during a 2-month follow-up period without recurrence.CONCLUSION For immunocompetent patients with refractory diarrhea,PA should be considered and mNGS-guided antifungals should be initiated. 展开更多
关键词 Invasive aspergillosis Primary intestinal aspergillosis Immunocompetent individual Voriconazole Metagenomic next-generation sequencing Case report
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Research on Drug Resistance Mechanisms and Countermeasures of Targeted Therapy for Colorectal Cancer Based on Next-Generation Sequencing 认领 引用
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作者 Mengyi Li 《Journal of Clinical and Nursing Research》 2026年第4期259-265,共7页
Colorectal cancer,a malignant gastrointestinal tumor with high morbidity and mortality,has long been plagued by difficulties in treatment and high recurrence rates,seriously threatening patients’lives and health.With... Colorectal cancer,a malignant gastrointestinal tumor with high morbidity and mortality,has long been plagued by difficulties in treatment and high recurrence rates,seriously threatening patients’lives and health.With the continuous development of molecular biology technology,targeted therapy has emerged and shown great value in improving the survival and prognosis of patients with colorectal cancer.However,drug resistance is common in clinical practice,which directly impairs therapeutic efficacy.Next-Generation Sequencing(NGS),a high-efficiency and high-throughput molecular detection technology,can deeply analyze the genomic characteristics of tumors,accurately identify relevant molecular targets,and provide important support for the study of drug resistance mechanisms and the formulation of individualized countermeasures.This paper discusses the drug resistance mechanisms of targeted therapy for colorectal cancer based on NGS and the corresponding countermeasures,aiming to provide reference for relevant practitioners. 展开更多
关键词 Next-generation sequencing Colorectal cancer Targeted therapy Drug resistance mechanism Countermeasures
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Research on Next-Generation Internet Architecture 认领 引用 被引量：6
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作者 吴建平 徐恪 《Journal of Computer Science & Technology》 SCIE EI 2006年第5期723-731,共9页
The primary problem during the evolvement of next-generation Internet is the contradiction between growing requirements for Internet and the insufficient development of network theory and technology. As the fundamenta... The primary problem during the evolvement of next-generation Internet is the contradiction between growing requirements for Internet and the insufficient development of network theory and technology. As the fundamental principles to guide the developing direction of Internet, the study of Internet architecture is always a focus in the research community. To address the core issue of network scalability, we propose multi-dimension scalable architecture of next-generation Internet, the main idea of which is to extend the single-dimension scalability of traditional Internet on interconnection to multi-dimension scalability of next-generation Internet. The multi-dimension scalability is composed of scale-scalability, performance-scalability, security-scalability, function-scalability, and service-scalability. We suggest five elements, namely, IPv6, authentic IPv6 addressing, scalable processing capacity of routers, end-to-end connectionless Quality-of-Service control, and 4over6 mechanism to realize the multi-dimension scalability. The current research results show that the multi-dimension scalable architecture composed of these five elements will bring great influence on next-generation Internet. 展开更多
关键词 next-generation Internet multi-dimension scalability IPv6 authentic IPv6 addressing 4over6
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Revolutionizing Crop Breeding:Next-Generation Artificial Intelligence and Big Data-Driven Intelligent Design 认领 引用 被引量：10
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作者 Ying Zhang Guanmin Huang +5 位作者 Yanxin Zhao Xianju Lu Yanru Wang Chuanyu Wang Xinyu Guo Chunjiang Zhao 《Engineering》 SCIE EI CSCD 2025年第1期245-255,共11页
The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This... The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This era integrates biotechnology,artificial intelligence(AI),and big data information technology.In contrast,China is still in a transition period between stages 2.0 and 3.0,which primarily relies on conventional selection and molecular breeding.In the context of increasingly complex international situations,accurately identifying core issues in China's seed industry innovation and seizing the frontier of international seed technology are strategically important.These efforts are essential for ensuring food security and revitalizing the seed industry.This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding.It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives.These include highthroughput phenotype acquisition and analysis,multiomics big data database and management system construction,AI-based multiomics integrated analysis,and the development of intelligent breeding software tools based on biological big data and AI technology.Based on an in-depth analysis of the current status and challenges of China's seed industry technology development,we propose strategic goals and key tasks for China's new generation of AI and big data-driven intelligent design breeding.These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining,efficient gene manipulation,engineered variety design,and systematized biobreeding.This study provides a theoretical basis and practical guidance for the development of China's seed industry technology. 展开更多
关键词 Crop breeding Next-generation artificial intelligence Multiomics big data Intelligent design breeding
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Pathogen Detection in Spinal Infections:Next-Generation Sequencing Versus Conventional Microbiological Methods 认领 引用 被引量：2
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作者 Khan Akhtar Ali Ling-xiao He +4 位作者 Fang Gao Ze-an Xia Hui Huang Heng Zeng Wei-hua Hu 《Current Medical Science》 SCIE CAS CSCD 2025年第2期331-340,共10页
Objective and Background Early and accurate diagnosis of spinal infections,including spinal tuberculosis,is pivotal for effective treatment but remains challenging.This study aims to assess the diagnostic yield of met... Objective and Background Early and accurate diagnosis of spinal infections,including spinal tuberculosis,is pivotal for effective treatment but remains challenging.This study aims to assess the diagnostic yield of metagenomic next-generation sequencing(mNGS)compared with that of conventional microbiological tests(CMTs)in identifying pathogens associated with spinal pathologies,with a special focus on infections leading to surgical interventions.Methods We enrolled 85 patients who underwent spinal surgery,comprising 63 patients with clinically diagnosed spinal infections,including patients with spinal tuberculosis,and 22 patients with noninfectious spinal conditions.The procedures involved irrigation and debridement for persistent wound drainage,with subsequent DNA extraction from plasma and joint fluid for mNGS and CMT analysis.Results Significantly increased C-reactive protein(CRP)levels were observed in patients with infections.The mNGS approach showed greater diagnostic sensitivity(92.06%)for detecting pathogens,including Mycobacterium tuberculosis,than did CMTs(36.51%).Despite its low specificity,mNGS had considerable negative predictive value(70.59%),underscoring its utility in ruling out infections.Conclusions The mNGS offers superior sensitivity over CMTs in the diagnosis of a variety of spinal infections,notably spinal tuberculosis.This study highlights the potential of mNGS in enhancing the diagnosis of complex spinal infections,thereby informing targeted treatment strategies. 展开更多
关键词 Metagenomic next-generation sequencing Conventional microbiological tests Spinal infections-Sensitivity Specificity Predictive value D-dimers
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Identification of genomic regions and candidate genes underlying carotenoid accumulation in soybean using next-generation sequencing based bulk segregant analysis 认领 引用
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作者 Berhane S.Gebregziabher Shengrui Zhang +2 位作者 Jing Li Bin Li Junming Sun 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2025年第6期2063-2079,共17页
The improvement of soybean seed carotenoid contents is very important due to the beneficial role of carotenoids in human health and nutrition. However, the genetic architecture underlying soybean carotenoid biosynthes... The improvement of soybean seed carotenoid contents is very important due to the beneficial role of carotenoids in human health and nutrition. However, the genetic architecture underlying soybean carotenoid biosynthesis remains largely unknown. In the present study, we employed next generation sequencing-based bulked-segregant analysis to identify new genomic regions governing seed carotenoids in 1,551 natural soybean accessions. The genomic DNA samples of individual plants with extreme phenotypes were pooled to form two bulks with high(50 accessions) and low(50 accessions) carotenoid contents for Illumina sequencing. A total of 125.09 Gb of clean bases and 89.82% of Q30 were obtained, and the average alignment efficiency was 99.45% with an average coverage depth of 62.20× and 99.75% genome coverage. Based on the G prime statistic algorithm(G') method analysis, 16 candidate genomic loci with a total length 20.41 Mb were found to be related to the trait. Of these loci, the most significant regions displaying the highest elevated G' values were found in chromosome 06 at a position of 18.53–22.67 Mb, and chromosome 19 at genomic region intervals of 8.36–10.94, 12.06–13.79 and 18.45–20.26 Mb. These regions were then used to identify the key candidate genes. In these regions, 250 predicted genes were found and analyzed to obtain 90 significantly enriched(P<0.05) Gene Ontology(GO) terms. Based on ANNOVAR analysis, 50 genes with non-synonymous and stopgained mutations were preferentially selected as potential candidate genes. Of those 50 genes, following their gene annotation functions and high significant haplotype variations in various environments,five genes were identified as the most promising candidate genes regulating soybean seed carotenoid accumulation, and they should be investigated in further functional validation studies. Collectively, understanding the genetic basis of carotenoid pigments and identifying genes underpinning carotenoid accumulation via a bulked-segregant analysis-based sequencing(BSA-seq) approach provide new insights for exploring future molecular breeding efforts to produce soybean cultivars with high carotenoid content. 展开更多
关键词 soybean(Glycine max L.Merrill) carotenoid bulk segregant analysis next-generation sequencing candidategenes
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Age-and gender-specific dynamics and next-generation reference intervals for pepsinogen in northern China 认领 引用
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作者 Miao-Miao Zhang Dong Zhu +1 位作者 Hai-Bin Zhao Xiu-Ying Zhao 《World Journal of Gastroenterology》 SCIE CAS 2025年第31期63-74,共12页
BACKGROUND Pepsinogen(PG)and the PG I/II ratio(PGR)are critical indicators for diagnosing Helicobacter pylori infection and chronic atrophic gastritis,and assessing gastric cancer risk.Existing reference intervals(RIs... BACKGROUND Pepsinogen(PG)and the PG I/II ratio(PGR)are critical indicators for diagnosing Helicobacter pylori infection and chronic atrophic gastritis,and assessing gastric cancer risk.Existing reference intervals(RIs)often overlook age,sex,and demographic variations.Partitioned RIs,while considering these factors,fail to capture the gradual age-related physiological changes.Next-generation RIs offer a solution to this limitation.AIM To investigate age-and sex-specific dynamics of PG and establish next-generation RIs for adults and the elderly in northern China.METHODS After screening,708 healthy individuals were included in this observational study.Serum PG was measured using chemiluminescence immunoassay.Age-and sex-related effects on PG were analyzed with a two-way analysis of variance.RI partitioning was determined by the standard deviation ratio(SDR).Traditional RIs were established using a non-parametric approach.Generalized Additive Models for Location,Scale,and Shape(GAMLSS)modeled age-related trends and continuous reference percentiles for PG I and PG II.Reference limit flagging rates for both RI types were compared.RESULTS PG I and PG II levels were influenced by age(P<0.001)and sex(P<0.001),while PGR remained stable.Age-specific RIs were required for PG I(SDR=0.366)and PG II(SDR=0.424).Partitioned RIs were established for PG I and PG II,with a single RI for PGR.GAMLSS modeling revealed distinct age-dependent trajectories:PG I increased from a median of 39.75μg/L at age 20 years to 49.75μg/L at age 60 years,a 25.16%increase,after which it plateaued through age 80 years.In contrast,PG II showed a continuous rise throughout the age range,with the median value increasing from 5.07μg/L at age 20 years to 8.36μg/L at age 80 years,corresponding to a 64.89%increase.Continuous reference percentiles intuitively reflected these trends and were detailed in this study.Next-generation RIs demonstrated superior accuracy compared to partitioned RIs when applied to specific age subgroups.CONCLUSION This study elucidates the age-and sex-specific dynamics of PG and,to our knowledge,is the first to establish next-generation RIs for PG,supporting more individualized interpretation in laboratory medicine. 展开更多
关键词 Pepsinogen Reference intervals Next-generation reference intervals Age dynamics Gastric biomarkers Personalized medicine
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Expanding Viral Diversity in Rice Fields by Next-Generation Sequencing 认领 引用
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作者 WANG Haoran CHEN Guoqing FENG Guozhong 《Rice science》 SCIE CSCD 2025年第1期44-51,I0024-I0040,共8页
In rice fields,rice plants usually grow alongside wild weeds and are attacked by various invertebrate species.Viruses are abundant in plants and invertebrates,playing crucial ecological roles in controlling microbial ... In rice fields,rice plants usually grow alongside wild weeds and are attacked by various invertebrate species.Viruses are abundant in plants and invertebrates,playing crucial ecological roles in controlling microbial abundance and maintaining community structures.To date,only 16 rice viruses have been documented in rice-growing regions.These viruses pose serious threats to rice production and have traditionally been identified only from rice plants and insect vectors by isolation techniques.Advances in next-generation sequencing(NGS)have made it feasible to discover viruses on a global scale.Recently,numerous viruses have been identified in plants and invertebrates using NGS technologies.In this review,we discuss viral studies in rice plants,invertebrate species,and weeds in rice fields.Many novel viruses have been discovered in rice ecosystems through NGS technologies,with some also detected using metatranscriptomic and small RNA sequencing.These analyses greatly expand our understanding of viruses in rice fields and provide valuable insights for developing efficient strategies to manage insect pests and virus-mediated rice diseases. 展开更多
关键词 next-generation sequencing virus rice plant invertebrate species wild weed ecosystem viral transmission
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Blockchain-as-a-Utility for Next-Generation Healthcare Internet of Things 认领 引用 被引量：2
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作者 Alaa Omran Almagrabi Rashid Ali +2 位作者 Daniyal Alghazzawi Abdullah AlBarakati Tahir Khurshaid 《Computers, Materials & Continua》 SCIE EI 2021年第7期359-376,共18页
The scope of the Internet of Things(IoT)applications varies from strategic applications,such as smart grids,smart transportation,smart security,and smart healthcare,to industrial applications such as smart manufacturi... The scope of the Internet of Things(IoT)applications varies from strategic applications,such as smart grids,smart transportation,smart security,and smart healthcare,to industrial applications such as smart manufacturing,smart logistics,smart banking,and smart insurance.In the advancement of the IoT,connected devices become smart and intelligent with the help of sensors and actuators.However,issues and challenges need to be addressed regarding the data reliability and protection for signicant nextgeneration IoT applications like smart healthcare.For these next-generation applications,there is a requirement for far-reaching privacy and security in the IoT.Recently,blockchain systems have emerged as a key technology that changes the way we exchange data.This emerging technology has revealed encouraging implementation scenarios,such as secured digital currencies.As a technical advancement,the blockchain network has the high possibility of transforming various industries,and the next-generation healthcare IoT(HIoT)can be one of those applications.There have been several studies on the integration of blockchain networks and IoT.However,blockchain-as-autility(BaaU)for privacy and security in HIoT systems requires a systematic framework.This paper reviews blockchain networks and proposes BaaU as one of the enablers.The proposed BaaU-based framework for trustworthiness in the next-generation HIoT systems is divided into two scenarios.The rst scenario suggests that a healthcare service provider integrates IoT sensors such as body sensors to receive and transmit information to a blockchain network on the IoT devices.The second proposed scenario recommends implementing smart contracts,such as Ethereum,to automate and control the trusted devices’subscription in the HIoT services. 展开更多
关键词 blockchain next-generation IoT privacy and security healthcare IoT complex healthcare applications
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Scalable and Resilient AI Framework for Malware Detection in Software-Defined Internet of Things 认领 引用 被引量：1
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作者 Maha Abdelhaq Ahmad Sami Al-Shamayleh +2 位作者 Adnan Akhunzada Nikola Ivkovi´c Toobah Hasan 《Computers, Materials & Continua》 SCIE EI 2026年第4期1307-1321,共15页
The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expo... The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expose them to increasingly sophisticated and persistentmalware attacks.These adaptive and stealthy threats can evade conventional detection,establish remote control,propagate across devices,exfiltrate sensitive data,and compromise network integrity.This study presents a Software-Defined Internet of Things(SD-IoT)control-plane-based,AI-driven framework that integrates Gated Recurrent Units(GRU)and Long Short-TermMemory(LSTM)networks for efficient detection of evolving multi-vector,malware-driven botnet attacks.The proposed CUDA-enabled hybrid deep learning(DL)framework performs centralized real-time detection without adding computational overhead to IoT nodes.A feature selection strategy combining variable clustering,attribute evaluation,one-R attribute evaluation,correlation analysis,and principal component analysis(PCA)enhances detection accuracy and reduces complexity.The framework is rigorously evaluated using the N_BaIoT dataset under k-fold cross-validation.Experimental results achieve 99.96%detection accuracy,a false positive rate(FPR)of 0.0035%,and a detection latency of 0.18 ms,confirming its high efficiency and scalability.The findings demonstrate the framework’s potential as a robust and intelligent security solution for next-generation IoT ecosystems. 展开更多
关键词 AI-driven malware analysis advanced persistent malware(APM) AI-poweredmalware detection deep learning(DL) malware-driven botnets software-defined internet of things(SD-IoT)
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Overcoming Dynamic Connectivity in Internet of Vehicles:A DAG Lattice Blockchain with Reputation-Based Incentive 认领 引用
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作者 Xiaodong Zhang Wenhan Hou +2 位作者 Juanjuan Wang Leixiao Li Pengfei Yue 《Computers, Materials & Continua》 SCIE EI 2026年第2期1803-1822,共20页
Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic ... Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic Graph(DAG)structure often suffer from performance limitations.The DAG lattice structure is a novel blockchain model in which each node maintains its own account chain,and only the node itself is allowed to update it.This feature makes the DAG lattice structure particularly suitable for addressing the challenges in dynamically connected IoV environment.In this paper,we propose a blockchain architecture based on the DAG lattice structure,specifically designed for dynamically connected IoV.In the proposed system,nodes must obtain authorization from a trusted authority before joining,forming a permissioned blockchain.Each node is assigned an individual account chain,allowing vehicles with limited storage capacity to participate in the blockchain by storing transactions only from nearby vehicles’account chains.Every transmitted message is treated as a transaction and added to the blockchain,enablingmore efficient data transmission in a dynamic network environment.Areputation-based incentivemechanism is introduced to encourage nodes to behave normally.Experimental results demonstrate that the proposed architecture achieves better performance compared with traditional single-chain and DAG-based approaches in terms of average transmission delay and storage cost. 展开更多
关键词 Blockchain Internet of vehicles dynamic connectivity DAG lattice incentive
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Path planning of autonomous underwater vehicle for data collection of the Internet of everything 认领 引用
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作者 Desheng Chen Meng Xi +3 位作者 Jiabao Wen Jingyi He Huiao Dai Wenjie Li 《Digital Communications and Networks》 SCIE EI CSCD 2026年第3期520-529,共10页
Autonomous Underwater Vehicle(AUV)has become an important tool to accomplish various path planning tasks due to its high intelligence and good maneuverability.Aiming at the problem of data collection at underwater Int... Autonomous Underwater Vehicle(AUV)has become an important tool to accomplish various path planning tasks due to its high intelligence and good maneuverability.Aiming at the problem of data collection at underwater Internet of Everything(IoE)nodes,this paper constructs a complex 3D marine environment based on real marine current data,and proposes a path planning algorithm based on reinforcement learning to ensure that the AUV completes the data collection with a short path length.In particular,in order to address the problem of complex path planning tasks,the Parallel Dense neural Network(PDNet)is proposed to improve the performance of the agent by extracting the core features of the input state.In addition,to simplify the reward shaping,we constructed a marine environment with sparse rewards.Sparse rewards can greatly interfere with the agent’s exploration and learning.To solve the sparse reward problem,the Hindsight Experience Replay(HER)is introduced,which not only solves the sparse reward problem,but also improves the sampling efficiency and convergence of the algorithm. 展开更多
关键词 Internet of everything Autonomous underwater vehicles Path planning Deep reinforcement learning
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Explainable Hybrid AI Model for DDoS Detection in SDN-Enabled Internet of Vehicle 认领 引用
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作者 Oumaima Saidani Nazia Azim +5 位作者 Ateeq Ur Rehman Akbayan Bekarystankyzy Hala Abdel Hameed Mostafa Mohamed R.Abonazel Ehab Ebrahim Mohamed Ebrahim Sarah Abu Ghazalah 《Computers, Materials & Continua》 SCIE EI 2026年第5期499-526,共28页
The convergence of Software Defined Networking(SDN)in Internet of Vehicles(IoV)enables a flexible,programmable,and globally visible network control architecture across Road Side Units(RSUs),cloud servers,and automobil... The convergence of Software Defined Networking(SDN)in Internet of Vehicles(IoV)enables a flexible,programmable,and globally visible network control architecture across Road Side Units(RSUs),cloud servers,and automobiles.While this integration enhances scalability and safety,it also raises sophisticated cyberthreats,particularly Distributed Denial of Service(DDoS)attacks.Traditional rule-based anomaly detection methods often struggle to detectmodern low-and-slowDDoS patterns,thereby leading to higher false positives.To this end,this study proposes an explainable hybrid framework to detect DDoS attacks in SDN-enabled IoV(SDN-IoV).The hybrid framework utilizes a Residual Network(ResNet)to capture spatial correlations and a Bi-Long Short-Term Memory(BiLSTM)to capture both forward and backward temporal dependencies in high-dimensional input patterns.To ensure transparency and trustworthiness,themodel integrates the Explainable AI(XAI)technique,i.e.,SHapley Additive exPlanations(SHAP).SHAP highlights the contribution of each feature during the decision-making process,facilitating security analysts to understand the rationale behind the attack classification decision.The SDN-IoV environment is created in Mininet-WiFi and SUMO,and the hybrid model is trained on the CICDDoS2019 security dataset.The simulation results reveal the efficacy of the proposed model in terms of standard performance metrics compared to similar baseline methods. 展开更多
关键词 Explainable AI software defined networking Internet of vehicles DDoS attack ResNet BiLSTM
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LEAF:A Lightweight Edge Agent Framework with Expert SLMs for the Industrial Internet of Things 认领 引用
18
作者 Qingwen Yang Zhi Li +3 位作者 Jiawei Tang Yanyi Liu Tiezheng Guo Yingyou Wen 《Computers, Materials & Continua》 SCIE EI 2026年第5期716-730,共15页
Deploying Large LanguageModel(LLM)-based agents in the Industrial Internet ofThings(IIoT)presents significant challenges,including high latency from cloud-based APIs,data privacy concerns,and the infeasibility of depl... Deploying Large LanguageModel(LLM)-based agents in the Industrial Internet ofThings(IIoT)presents significant challenges,including high latency from cloud-based APIs,data privacy concerns,and the infeasibility of deploying monolithic models on resource-constrained edge devices.While smaller models(SLMs)are suitable for edge deployment,they often lack the reasoning power for complex,multi-step tasks.To address these issues,this paper introduces LEAF,a Lightweight Edge Agent Framework designed for efficiently executing complex tasks at the edge.LEAF employs a novel architecture where multiple expert SLMs—specialized for planning,execution,and interaction—work in concert,decomposing complex problems into manageable sub-tasks.To mitigate the resource overhead of this multi-model approach,LEAF implements an efficient parameter-sharing scheme based on Scalable Low-Rank Adaptation(S-LoRA).We introduce a two-stage training strategy combining Supervised Fine-Tuning(SFT)and Group Relative Policy Optimization(GRPO)to significantly enhance each expert’s capabilities.Furthermore,a Finite StateMachine(FSM)-based decision engine orchestrates the workflow,uniquely balancing deterministic control with intelligent flexibility,making it ideal for industrial environments that demand both reliability and adaptability.Experiments across diverse IIoT scenarios demonstrate that LEAF significantly outperforms baseline methods in both task success rate and user satisfaction.Notably,our fine-tuned 4-billion-parameter model achieves a task success rate over 90%in complex IIoT scenarios,demonstrating LEAF’s ability to deliver powerful and efficient autonomy at the industrial edge. 展开更多
关键词 Industrial internet of things edge computing LLM-based agents small language models
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FSL-TM:Review on the Integration of Federated Split Learning with TinyML in the Internet of Vehicles 认领 引用
19
作者 Meenakshi Aggarwal Vikas Khullar Nitin Goyal 《Computers, Materials & Continua》 SCIE EI 2026年第2期290-320,共31页
The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects.... The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects.IoV systems typically send massive volumes of raw data to central servers,which may raise privacy issues.Additionally,model training on IoV devices with limited resources normally leads to slower training times and reduced service quality.We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning(TinyML)approach,which operates on IoV edge devices without sharing sensitive raw data.Specifically,we focus on integrating split learning(SL)with federated learning(FL)and TinyML models.FL is a decentralisedmachine learning(ML)technique that enables numerous edge devices to train a standard model while retaining data locally collectively.The article intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the IoV domain,coupled with FL and TinyML.The approach starts with the IoV learning framework,which includes edge computing,FL,SL,and TinyML,and then proceeds to discuss how these technologies might be integrated.We elucidate the comprehensive operational principles of Federated and split learning by examining and addressingmany challenges.We subsequently examine the integration of SL with FL and various applications of TinyML.Finally,exploring the potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM.It is a superior method for preserving privacy as it conducts model training on individual devices or edge nodes,thereby obviating the necessity for centralised data aggregation,which presents considerable privacy threats.The insights provided aim to help both researchers and practitioners understand the complicated terrain of FL and SL,hence facilitating advancement in this swiftly progressing domain. 展开更多
关键词 Machine learning federated learning split learning TinyML internet of vehicles
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A Multi-Objective Deep Reinforcement Learning Algorithm for Computation Offloading in Internet of Vehicles 认领 引用
20
作者 Junjun Ren Guoqiang Chen +1 位作者 Zheng-Yi Chai Dong Yuan 《Computers, Materials & Continua》 SCIE EI 2026年第1期2111-2136,共26页
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain... Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively. 展开更多
关键词 Deep reinforcement learning internet of vehicles multi-objective optimization cloud-edge computing computation offloading service caching
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