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Peer bullying victimisation and depressive symptoms as serial mediators between attention-deficit/hyperactivity disorder symptoms and internet gaming disorder among Chinese adolescents:A three-wave longitudinal study 认领 引用
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作者 Pu Peng Zhangming Chen +12 位作者 Silan Ren Ying He Jinguang Li Aijun Liao Linlin Zhao Xu Shao Shanshan Chen Ruini He Yudiao Liang Youguo Tan Xiaogang Chen Jinsong Tang Yanhui Liao 《General Psychiatry》 CAS CSCD 2026年第1期31-44,共14页
Background The association between attention-deficit/hyperactivity disorder(ADHD) symptoms and internet gaming disorder(IGD) is well-established,yet the psychological mechanisms underlying this comorbidity remain unde... Background The association between attention-deficit/hyperactivity disorder(ADHD) symptoms and internet gaming disorder(IGD) is well-established,yet the psychological mechanisms underlying this comorbidity remain underexplored.Aims Grounded in the dual failure model and the compensatory internet use model,this study examined peer bullying victimisation and depressive symptoms as serial mediators in the longitudinal association between ADHD symptoms and IGD severity among 20 137 Chinese adolescents.Methods Participants were assessed at baseline(T1,November 2020) and followed up at one(T2) and two years(T3).Standardised measures assessed peer bullying victimisation(Multidimensional Peer Victimisation Scale),ADHD symptoms(Strengths and Difficulties Questionnaire),depressive symptoms(9-item Patient Health Questionnaire) and IGD severity(Internet Gaming Disorder Scale-Short Form).Longitudinal path analysis with serial mediation tested the hypothesised pathway,adjusting for baseline covariates and prior symptoms.Subgroup analyses examined sex and developmental(early vs.late adolescence) differences.Sensitivity analyses included alternative mediation models,cross-lagged panel models and parallel-process latent growth curve models.Results Baseline ADHD symptoms directly predicted IGD severity and indirectly through peer bullying victimisation and depressive symptoms.These mediators accounted for one-third of the total effect.The bullying-related mediation pathway was evident only among boys and early adolescents,whereas depressive symptoms consistently mediated the association across sexes and age groups.Sensitivity analyses supported the robustness and temporal specificity of the proposed pathway.Conclusions ADHD symptoms increase the risk of subsequent IGD through both direct and indirect pathways operating through peer bullying victimisation and depressive symptoms.This social-emotional mediation process is developmentally and sex contingent.These findings suggest that effective prevention and intervention for IGD in adolescents with ADHD should incorporate developmentally and sexsensitive strategies that address peer victimisation and emotional distress in addition to core ADHD symptoms. 展开更多
关键词 peer bullying victimisation internet gaming disorder igd dual failure model psychological mechanisms compensatory internet use modelthis Attention Deficit Hyperactivity Disorder depressive symptoms Internet Gaming Disorder
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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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A synchronous encryption scheme for remote sensing images using 3D cross-coupled chaotic maps and fractal cubes in the internet of things 认领 引用
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作者 Ze YU Ying ZHOU Zhenlong MAN 《Optoelectronics Letters》 EI 2026年第5期289-294,共6页
With the rapid development and wide application of internet of things(IoT)technology,optical equipment is being promoted to collect and store multi-scale remote sensing images,and to apply them in various fields such ... With the rapid development and wide application of internet of things(IoT)technology,optical equipment is being promoted to collect and store multi-scale remote sensing images,and to apply them in various fields such as industry,agriculture,and ecological and environmental protection.However,the resulting security risks have also caused widespread concern.This paper designs a remote sensing image synchronization encryption scheme based on 3D cross-coupled chaotic map and block-based cubes for multi-scale remote sensing images.First,a new 3D chaotic map is constructed by coupling the traditional single-node sinusoidal map,which provides support for building complex chaotic mappings for devices with limited resources.Second,a fractal square based on the Hilbert curve combined with chaos achieves simultaneous pixel scrambling and diffusion operations,improving multi-scale graphics encryption.Finally,a multi-type remote sensing graphics dataset is used for testing and a series of analysis experiments are performed to prove the feasibility of the algorithm. 展开更多
关键词 Internet Things remote sensing image synchronization Fractal Cubes D Cross Coupled Chaotic Maps Remote Sensing Images security risks internet things iot technologyoptical equipment Synchronous Encryption
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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
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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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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
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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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A Multi-Objective Deep Reinforcement Learning Algorithm for Computation Offloading in Internet of Vehicles 认领 引用
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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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EDESC-IDS:An Efficient Deep Embedded Subspace Clustering-Based Intrusion Detection System for the Internet of Vehicles 认领 引用
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作者 Lixing Tan Liusiyu Chen +2 位作者 Yang Wang Zhenyu Song Zenan Lu 《Computers, Materials & Continua》 SCIE EI 2026年第5期997-1020,共24页
Anomaly detection is a vibrant research direction in controller area networks,which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles.However,ex... Anomaly detection is a vibrant research direction in controller area networks,which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles.However,existing unsupervised learning methods suffer from insufficient temporal and spatial constraints on shallow features,resulting in fragmented feature representations that compromise model stability and accuracy.To improve the extraction of valuable features,this paper investigates the influence of clustering constraints on shallow feature convergence paths at the model level and further proposes an end-to-end intrusion detection system based on efficient deep embedded subspace clustering(EDESC-IDS).Following the standard learning approach,continuous messages are encoded into two-dimensional data frames via a frame builder,which are then input into an extended convolutional autoencoder for extracting shallow features from high-dimensional data.On this basis,the dual constraints of these output features and the embedding clustering module facilitate end-to-end training of the EDESC-IDS in various attack scenarios.Extensive experimental results show that such a system exhibits significant detection performance on four types of attack datasets,including DoS,Gear,Fuzzy,and RPM,with precision,recall,and F1 scores consistently above 97.79%,while maintaining a false negative rate(FNR)and an error rate(ER)below 2.22%. 展开更多
关键词 Internet of vehicles control area network anomaly detection unsupervised learning deep embedded subspace clustering extended convolutional autoencoder
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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
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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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Cascading Failure Dynamics and Edge-Intelligent Defense in Space-Air-Ground Integrated Networks for Internet of Things 认领 引用
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作者 Peiying Zhang Yihong Yu +3 位作者 Lizhuang Tan Shuqing He Jian Wang Ameer El-Sayed 《Computers, Materials & Continua》 SCIE EI 2026年第8期865-880,共16页
As a core information infrastructure in the 6G era,the Space-Air-Ground Integrated Network(SAGIN)integrates space-based,air-based,and ground-based network resources to achieve seamless communication across all domains... As a core information infrastructure in the 6G era,the Space-Air-Ground Integrated Network(SAGIN)integrates space-based,air-based,and ground-based network resources to achieve seamless communication across all domains.However,its characteristics such as heterogeneous node coupling and dynamic topology changes make it prone to cascading failures,severely threatening critical business continuity in Internet of Things(IoT)applications spanning smart cities,healthcare,transportation,and industrial automation.This paper conducts systematic research addressing challenges including modeling difficulties in SAGIN cascading failure propagation,insufficient coordination of defense strategies,and poor resource adaptability.First,a multi-factor coupled dynamic model of cascading failure propagation is established to quantify the synergistic effects of node heterogeneity,link dynamics,and load redistribution.Second,a closed-loop collaborative defense system integrating“early warning-isolation-self-healing”is designed.The system incorporates a lightweight greedy-based self-healing algorithm and uses multi-criteria decision-making(Analytic Hierarchy Process)for resource optimization.These approaches ensure real-time performance and energy efficiency on resource-constrained edge nodes.Third,a joint simulation platform combining NS-3 and MATLAB is built to validate the model and strategies across diverse IoT application scenarios.Experimental results show that the proposed propagation model maintains prediction error within 10%,the defense strategies increase failure recovery rates to 85%–90%,reduce communication interruption duration by over 60%,and lower resource overhead by 20%–25%,providing theoretical support and technical guarantees for stable SAGIN operation in security and resiliency-critical environments. 展开更多
关键词 Space-Air-Ground Integrated Network cascading failure defense strategy edge intelligence Internet of Things resource allocation and optimization real-time and energy efficiency security and resiliency network reliability
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Efficient and Secure Data Storage in 5G Industrial Internet Collaborative Systems 认领 引用
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作者 Wang Jigang Liu Dong +1 位作者 Wan Changsheng Lu Ping 《ZTE Communications》 2026年第1期45-55,共11页
Security and access control for data storage in 5G industrial Internet collaborative systems are facing significant challenges.The characteristics of 5 G networks,such as low latency and high speed,facilitate data tra... Security and access control for data storage in 5G industrial Internet collaborative systems are facing significant challenges.The characteristics of 5 G networks,such as low latency and high speed,facilitate data transmission in the industrial Internet but also increase vulnerability to attacks like theft and tampering.Moreover,in 5G industrial Internet collaborative system environments,data flows across multiple entities and links,which necessitates a flexible access control model to meet specific data access requirements.Traditional role-based and attribute-based access control mechanisms are difficult to apply in such dynamic application scenarios.To address these challenges,we propose a novel data storage solution for 5G industrial Internet collaborative systems.Similar to existing approaches,it provides integrity and confidentiality protection for transmitted data.In terms of security,only authenticated data owners and users can obtain file decryption keys,preventing malicious attackers from data forgery.Regarding access control,decryption is permitted only to authorized data users,safeguarding against unauthorized file access.Furthermore,by introducing an attribute-based encryption mechanism,only data users with specific attributes can decrypt files.In terms of efficiency,our approach utilizes bilinear and modular exponentiation operations solely during the authentication process.For handling substantial data loads,lightweight cryptographic algorithms are employed.Consequently,our solution achieves higher efficiency compared with other known methods.Experimental results demonstrate the feasibility of our approach in real-world applications. 展开更多
关键词 5G industrial Internet collaborative systems data storage identity-based authentication access control
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Internet of Agents:Design of the Protocol System 认领 引用
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作者 Fu Yuexia Liu Peng +1 位作者 Lu Lu Duan Xiaodong 《ZTE Communications》 2026年第2期33-42,共10页
With the rapid advancement of generative artificial intelligence(AI)and large language model(LLM)technologies,AI agents are gradually becoming the core service units in networks,and their communication mode is evolvin... With the rapid advancement of generative artificial intelligence(AI)and large language model(LLM)technologies,AI agents are gradually becoming the core service units in networks,and their communication mode is evolving from local collaboration to wide-area interconnection.The construction of the Internet of Agents(IoA)faces multiple challenges,such as identity management,dynamic networking,and semantic routing,which urgently requires the design of a network protocol system that adapts to its new traffic characteristics and collaboration needs.Based on the application scenarios of agent communication,this paper systematically analyzes the management,control,and routing requirements that multi-agent collaboration imposes on IP networks,proposes a three-layer functional architecture for the IoA,and designs a protocol suite covering management,control,and routing around key issues such as agent registration and identification,service discovery,capability sensing,and cross-domain traffic assurance.By extending existing Internet protocols and introducing a semantically aware routing mechanism,this paper provides a scalable,efficient,and secure approach to implementing a protocol for end-to-end agent collaboration,thereby contributing to the construction of an open,large-scale agent collaboration ecosystem. 展开更多
关键词 Internet of Agents user agent service agent protocol framework
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Lightweight Hash-Based Post-Quantum Signature Scheme for Industrial Internet of Things 认领 引用
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作者 Chia-Hui Liu 《Computers, Materials & Continua》 SCIE EI 2026年第2期1041-1058,共18页
TheIndustrial Internet of Things(IIoT)has emerged as a cornerstone of Industry 4.0,enabling large-scale automation and data-driven decision-making across factories,supply chains,and critical infrastructures.However,th... TheIndustrial Internet of Things(IIoT)has emerged as a cornerstone of Industry 4.0,enabling large-scale automation and data-driven decision-making across factories,supply chains,and critical infrastructures.However,the massive interconnection of resource-constrained devices also amplifies the risks of eavesdropping,data tampering,and device impersonation.While digital signatures are indispensable for ensuring authenticity and non-repudiation,conventional schemes such as RSA and ECCare vulnerable to quantumalgorithms,jeopardizing long-termtrust in IIoT deployments.This study proposes a lightweight,stateless,hash-based signature scheme that achieves post-quantum security while addressing the stringent efficiency demands of IIoT.The design introduces two key optimizations:(1)Forest ofRandomSubsets(FORS)onDemand,where subset secret keys are generated dynamically via a PseudoRandom Function(PRF),thereby minimizing storage overhead and eliminating key-reuse risks;and(2)Winternitz One-Time Signature Plus(WOTS+)partial hash-chain caching,which precomputes intermediate hash values at edge gateways,reducing device-side computations,latency,and energy consumption.The architecture integrates a multi-layerMerkle authentication tree(Merkle tree)and role-based delegation across sensors,gateways,and a Signature Authority Center(SAC),supporting scalable cross-site deployment and key rotation.Froma theoretical perspective,we establish a formal(Existential Unforgeability under Chosen Message Attack)EUF-CMA security proof using a game-based reduction framework.The proof demonstrates that any successful forgerymust reduce to breaking the underlying assumptions of PRF indistinguishability,(second)preimage resistance,or collision resistance,thus quantifying adversarial advantage and ensuring unforgeability.On the implementation side,our design achieves a balanced trade-off between postquantum security and lightweight performance,offering concrete deployment guidelines for real-time industrial systems.In summary,the proposed method contributes both practical system design and formal security guarantees,providing IIoT with a deployable signature substrate that enhances resilience against quantum-era threats and supports future extensions such as device attestation,group signatures,and anomaly detection. 展开更多
关键词 Industrial Internet of Things(IIoT) post-quantum cryptography hash-based signatures SPHINCS+
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Exploration of New Models of Moral Education in Middle Schools in the Context of the Internet 认领 引用
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作者 Baozhou LIANG 《Asian Agricultural Research》 2026年第8期43-45,53,共3页
Based on data on internet usage by minors,this paper analyzes the environmental changes and practical foundations faced by moral education in middle schools,and constructs a new five-in-one model of secondary school m... Based on data on internet usage by minors,this paper analyzes the environmental changes and practical foundations faced by moral education in middle schools,and constructs a new five-in-one model of secondary school moral education consisting of"value guidance,digital empowerment,life practice,collaborative co-education,and development evaluation."The model takes cultivating virtue and nurturing talents as its fundamental principle,with a focus on internet literacy and digital responsibility.Through pathways such as building an internet literacy curriculum system,constructing integrated online-offline platforms,carrying out project-based practical activities,strengthening the teacher-student dual-subject relationship,and establishing a home-school-community collaborative governance mechanism,the model incorporates cyberspace as a significant field for students moral growth.It is also supported by operational mechanisms and safeguard measures,aiming to promote moral education from external discipline to internal identification,thus providing a reference for moral education practices in middle schools in the internet era. 展开更多
关键词 Internet Middle school moral education New model Internet literacy Digital moral education Home-school-community collaboration
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Data driven vehicular heterogeneity based intelligent collision avoidance system for Internet of Vehicles(IoV) 认领 引用 被引量：1
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作者 Iqra Adnan Tariq Umer +3 位作者 Ahmad Arsalan Maryam M.Al Dabel Ali Kashif Bashir Arooj Ansif 《Digital Communications and Networks》 SCIE EI CSCD 2026年第1期180-197,共18页
The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and... The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities.This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV.The system leverages Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication to collect real-time data about the environment and the vehicles.The data is collected to acknowledge the heterogeneity of vehicles and human behavior.The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions.The system takes into account the heterogeneity of vehicles,such as their size,speed,and maneuverability,to optimize collision avoidance strategies.The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems.The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5%using the SVM algorithm.The trial outcomes demonstrated that the new system,incorporating vehicular,weather,and human behavior factors,outperformed previous systems that only considered vehicular and weather aspects.This innovative approach is poised to lead transportation efforts,reducing accident rates and improving the quality of transportation systems in smart cities.By offering predictive capabilities,the proposed model not only helps control accident rates but also prevents them in advance,ensuring road safety. 展开更多
关键词 Internet of Vehicles Collision avoidance Machine learning Traffic safety Autonomous vehicles Vehicular networks Vehicular heterogeneity Smart transportation Traffic modeling
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MWaOA:A Bio-Inspired Metaheuristic Algorithm for Resource Allocation in Internet of Things 认领 引用
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作者 Rekha Phadke Abdul Lateef Haroon Phulara Shaik +3 位作者 Dayanidhi Mohapatra Doaa Sami Khafaga Eman Abdullah Aldakheel N.Sathyanarayana 《Computers, Materials & Continua》 SCIE EI 2026年第2期1285-1310,共26页
Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart ... Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices.Furthermore,the IoT plays a key role in multiple domains,including industrial automation,smart homes,and intelligent transportation systems.However,an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness.To address these issue,this research proposes a Modified Walrus Optimization Algorithm(MWaOA)for effective resource management in smart IoT systems.In the proposed MWaOA,a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability.During resource allocation,the MWaOA prevents early convergence,which aids in achieving a better balance between the exploration and exploitation phases during optimization.Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34%and minimizes the response time by 6% to 33% across different service arrival rates.Compared to traditional optimization algorithms,MWaOA reduces energy consumption by 5% to 30%and minimizes the response time by 4% to 28% across different simulation epochs.The proposed MWaOA provides adaptive and robust resource allocation,thereby minimizing transmission cost while considering network constraints and real-time performance parameters. 展开更多
关键词 Delay gateway internet of things resource allocation resource management walrus optimization algorithm
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Relationships between Internet Addiction,Self-Control,and Depression among Chinese Adolescents under Confucian Culture:A Cross-Lagged Panel Analysis 认领 引用
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作者 Ziyan Zhou Haiyun Peng +2 位作者 Menghao Ren Sufei Xin Daoqun Ding 《International Journal of Mental Health Promotion》 2026年第7期47-60,共14页
Background:Internet addiction and depression are important mental health concerns among adolescents.Although prior research has examined their bidirectional relationship,the underlying mechanisms remain unclear.Drawin... Background:Internet addiction and depression are important mental health concerns among adolescents.Although prior research has examined their bidirectional relationship,the underlying mechanisms remain unclear.Drawing on conservation of resources theory,this study examined their bidirectional relationship and tested the mediating role of self-control,with attention to gender differences.Methods:A two-wave longitudinal survey(T1:November 2021;T2:May 2022)was conducted in China among 1908 adolescents(1026 females,882 males;mean age=13.546,SD=1.463).At both waves,participants completed self-report measures of internet addiction,self-control,and depression(using the Internet Addiction Scale,Self-Control Scale,and CES-D).Results:The cross-lagged model revealed a bidirectional positive association between internet addiction and depression(β=0.088,p<0.001;β=0.082,p<0.001).Although effect sizes were modest,their cumulative effects may be clinically meaningful.The mediation analysis revealed bidirectional indirect effects through self-control.T1 internet addiction indirectly predicted T2 depression through self-control(indirect effect=0.009,95%CI[0.003,0.014]),and T1 depression indirectly predicted T2 internet addiction through self-control(indirect effect=0.016,95%CI[0.009,0.025]).These findings highlight self-control as a key linking mechanism.In addition,the negative effect of T1 internet addiction on T2 self-control was stronger in males than in females(β=−0.156,p<0.001;β=−0.049,p=0.014;Waldχ2=4.903,p=0.027).Conclusions:Internet addiction and depression show a bidirectional predictive relationship,and reduced self-control serves as the underlying mechanism sustaining this cycle.Males show greater depletion of self-control resources following internet addiction than females. 展开更多
关键词 Internet addiction depression self-control Chinese adolescents cross-lagged analysis
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Robust and Efficient Federated Learning for Machinery Fault Diagnosis in Internet of Things 认领 引用
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作者 Zhen Wu Hao Liu +4 位作者 Linlin Zhang Zehui Zhang Jie Wu Haibin He Bin Zhou 《Computers, Materials & Continua》 SCIE EI 2026年第4期1051-1069,共19页
Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Lever... Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Leveraging IoVtechnologies,operational data fromcore vehicle components can be collected and analyzed to construct fault diagnosis models,thereby enhancing vehicle safety.However,automakers often struggle to acquire sufficient fault data to support effective model training.To address this challenge,a robust and efficient federated learning method(REFL)is constructed for machinery fault diagnosis in collaborative IoV,which can organize multiple companies to collaboratively develop a comprehensive fault diagnosis model while keeping their data locally.In the REFL,the gradient-based adversary algorithm is first introduced to the fault diagnosis field to enhance the deep learning model robustness.Moreover,the adaptive gradient processing process is designed to improve the model training speed and ensure the model accuracy under unbalance data scenarios.The proposed REFL is evaluated on non-independent and identically distributed(non-IID)real-world machinery fault dataset.Experiment results demonstrate that the REFL can achieve better performance than traditional learning methods and are promising for real industrial fault diagnosis. 展开更多
关键词 Federated learning adversary algorithm Internet of Vehicles(IoV) fault diagnosis
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