Post-quantum transport layer security(PQ-TLS)is capable of effectively defending against quantum threats to current network communications,whereas its larger public key and certificate sizes as well as higher computat...Post-quantum transport layer security(PQ-TLS)is capable of effectively defending against quantum threats to current network communications,whereas its larger public key and certificate sizes as well as higher computational overhead may result in a significant performance reduction compared with conventional TLS.In this paper,we present a systematic evaluation of PQ-TLS performance across diverse deployment scenarios to address the following critical research questions.(1)What is the performance behavior of PQ-TLS across different TLS modes?(2)How does PQ-TLS perform across varying client scales?(3)Which network topology is most suitable for PQ-TLS?(4)How does PQ-TLS perform on personal computers(PCs)compared to embedded IoT devices?To the best of our knowledge,this is the first work to comprehensively address these issues,offering implementers some insights into PQ-TLS performance and guidance for optimizing it across diverse scenarios.展开更多
With the transformation from websites to Internet platforms, Chinese young netizens (born in 1990-2005) have become key subjects in the evolution of cyber-nationalism. Based on survey data, this study classifies their...With the transformation from websites to Internet platforms, Chinese young netizens (born in 1990-2005) have become key subjects in the evolution of cyber-nationalism. Based on survey data, this study classifies their nationalism into four types and explores its transformation alongside globalization cognition. The result shows that moderate nationalism is the mainstream. This has raised their attention to globalization, with greater focus on relations between China and developing countries, and nations along the Belt and Road Initiative. Their personal experiences and cultural exposure foster a more inclusive global vision, shaping the evolution of nationalism and global dialogue.展开更多
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
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
The Internet of Things(IoT)ecosystem is inherently heterogeneous,comprising diverse devices that must interoperate seamlessly to enable federated message and data exchange.However,as the number of service requests gro...The Internet of Things(IoT)ecosystem is inherently heterogeneous,comprising diverse devices that must interoperate seamlessly to enable federated message and data exchange.However,as the number of service requests grows,existing approaches suffer from increased discovery time and degraded Quality of Service(QoS).Moreover,the massive data generated by heterogeneous IoT devices often contain redundancy and noise,posing challenges to efficient data management.To address these issues,this paper proposes a lightweight ontology-based architecture that enhances service discovery and QoS-aware semantic data management.The architecture employs Modified-Ordered Points to Identify theClustering Structure(M-OPTICS)to cluster and eliminate redundant IoT data.The clustered data are then modelled into a lightweight ontology,enabling semantic relationship inference and rule generation through an embedded inference engine.User requests,transmitted via theConstrainedApplication Protocol(CoAP),are semantically enriched and matched to QoS parameters using Dynamic Shannon Entropy optimized with the Salp Swarm Algorithm.Semantic matching is further refined using a bidirectional recurrent neural network(Bi-RNN),while a State–Action–Reward–State–Action(SARSA)reinforcement learning model dynamically defines and updates semantic rules to retrieve themost recent and relevant data across heterogeneous devices.Experimental results demonstrate that the proposed architecture outperforms existing methods in terms of response time,service delay,execution time,precision,recall,and F-score under varying CoAP request loads and communication overheads.The results confirm the effectiveness of the proposed lightweight ontology architecture for service discovery and data management in heterogeneous IoT environments.展开更多
Due to the growth of smart cities,many real-time systems have been developed to support smart cities using Internet of Things(IoT)and emerging technologies.They are formulated to collect the data for environment monit...Due to the growth of smart cities,many real-time systems have been developed to support smart cities using Internet of Things(IoT)and emerging technologies.They are formulated to collect the data for environment monitoring and automate the communication process.In recent decades,researchers have made many efforts to propose autonomous systems for manipulating network data and providing on-time responses in critical operations.However,the widespread use of IoT devices in resource-constrained applications and mobile sensor networks introduces significant research challenges for cybersecurity.These systems are vulnerable to a variety of cyberattacks,including unauthorized access,denial-of-service attacks,and data leakage,which compromise the network’s security.Additionally,uneven load balancing between mobile IoT devices,which frequently experience link interferences,compromises the trustworthiness of the system.This paper introduces a Multi-Agent secured framework using lightweight edge computing to enhance cybersecurity for sensor networks,aiming to leverage artificial intelligence for adaptive routing and multi-metric trust evaluation to achieve data privacy and mitigate potential threats.Moreover,it enhances the efficiency of distributed sensors for energy consumption through intelligent data analytics techniques,resulting in highly consistent and low-latency network communication.Using simulations,the proposed framework reveals its significant performance compared to state-of-the-art approaches for energy consumption by 43%,latency by 46%,network throughput by 51%,packet loss rate by 40%,and denial of service attacks by 42%.展开更多
The Internet of Vehicles(IoV)operates in highly dynamic and open network environments and faces serious challenges in secure and real-time authentication and consensus mechanisms.Existing methods often suffer from com...The Internet of Vehicles(IoV)operates in highly dynamic and open network environments and faces serious challenges in secure and real-time authentication and consensus mechanisms.Existing methods often suffer from complex certificate management,inefficient consensus protocols,and poor resilience in high-frequency communication,resulting in high latency,poor scalability,and unstable network performance.To address these issues,this paper proposes a secure and efficient distributed authentication scheme for IoV with reputation-driven consensus and SM9.First,this paper proposes a decentralized authentication architecture that utilizes the certificate-free feature of SM9,enabling lightweight authentication and key negotiation,thereby reducing the complexity of key management.To ensure the traceability and global consistency of authentication data,this scheme also integrates blockchain technology,applying its inherent invariance.Then,this paper introduces a reputation-driven dynamic node grouping mechanism that transparently evaluates and groups’node behavior using smart contracts to enhance network stability.Furthermore,a new RBSFT(Reputation-Based SM9 Friendly-Tolerant)consensus mechanism is proposed for the first time to enhance consensus efficiency by optimizing the PBFT algorithm.RBSFT aims to write authentication information into the blockchain ledger to achieve multi-level optimization of trust management and decision-making efficiency,thereby significantly improving the responsiveness and robustness in high-frequency IoV scenarios.Experimental results show that it excels in authentication,communication efficiency,and computational cost control,making it a feasible solution for achieving IoV security and real-time performance.展开更多
The Internet ofThings(IoT)is a new model that evolved with the rapid progress of advanced technology and gained tremendous popularity due to its applications.Anomaly detection haswidely attracted researchers’attentio...The Internet ofThings(IoT)is a new model that evolved with the rapid progress of advanced technology and gained tremendous popularity due to its applications.Anomaly detection haswidely attracted researchers’attention in the last few years,and its effects on diverse applications.This review article covers the various methods and tools developed to perform the task efficiently and automatically in a smart city.In this work,we present a comprehensive literature review(2011 onwards)of three major types of anomalies:network anomalies,sensor anomalies,and videobased anomalies,along with their methods and software tools.Furthermore,anomaly detection methods such as machine learning and deep learning are presented in this work,highlighting their detection strategy techniques,features,applications,issues,and challenges.Moreover,a generic algorithmis also developed to ease the user achieve the taskmore specifically by targeting a specific domain aswell as approach.Comparative studies of three anomalymethods and their analysis identify research discovery areas with their applications.As a result,researchers and practitioners can familiarize themselves with the existing methods for solving real problems,improving methods,and developing new optimum methods for anomaly detection in diverse applications.展开更多
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
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
With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and ...With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic.This paper presents a comparative benchmark of classic machine learning(ML)and state-of-the-art deep learning(DL)algorithms for IoT intrusion detection.Our methodology employs a twophased approach:a preliminary pilot study using a custom-generated dataset to establish baselines,followed by a comprehensive evaluation on the large-scale CICIoTDataset2023.We benchmarked algorithms including Random Forest,XGBoost,CNN,and StackedLSTM.The results indicate that while top-performingmodels frombothcategories achieve over 99%classification accuracy,this metric masks a crucial performance trade-off.We demonstrate that treebased ML ensembles exhibit superior precision(91%)in identifying benign traffic,making them effective at reducing false positives.Conversely,DL models demonstrate superior recall(96%),making them better suited for minimizing the interruption of legitimate traffic.We conclude that the selection of an optimal model is not merely a matter of maximizing accuracy but is a strategic choice dependent on the specific security priority either minimizing false alarms or ensuring service availability.Thiswork provides a practical framework for deploying context-aware security solutions in diverse IoT environments.展开更多
Practical applications of smart cities and the Internet of Things(IoT)have multiplied,posing many difficulties in network performance,dependability,and security.Concerns of accessibility,reliability,sustainability,and...Practical applications of smart cities and the Internet of Things(IoT)have multiplied,posing many difficulties in network performance,dependability,and security.Concerns of accessibility,reliability,sustainability,and security too have arisen correspondingly because of the decentralized character of the smart city and IoT systems.Fog computing offers a foundation for various applications,including cognitive support,health and social services,intelligent transportation systems,and pervasive computing and communications.Fog computing can help enhance these apps'productivity and lower the end-to-end delay experienced by such time-sensitive applications.In this research,we propose a reliable and secure service delivery strategy at the network edge for smart cities.To improve the availability and dependability,along with the security of smart city applications,the approach employs a combined method uniting distributed fog servers in addition to mist servers with the help of an intrusion detection system.Simulation findings suggest a reduction of 40.3%in the delay incurred by each service request for highly dense areas and 60.6%for moderately dense environments.Furthermore,the system has low false-negative rates and high detection and accuracy rates,decreasing service requests 2%.展开更多
Internet of Things(IoT)interconnects devices via network protocols to enable intelligent sensing and control.Resource-constrained IoT devices rely on cloud servers for data storage and processing.However,this cloudass...Internet of Things(IoT)interconnects devices via network protocols to enable intelligent sensing and control.Resource-constrained IoT devices rely on cloud servers for data storage and processing.However,this cloudassisted architecture faces two critical challenges:the untrusted cloud services and the separation of data ownership from control.Although Attribute-based Searchable Encryption(ABSE)provides fine-grained access control and keyword search over encrypted data,existing schemes lack of error tolerance in exact multi-keyword matching.In this paper,we proposed an attribute-based multi-keyword fuzzy searchable encryption with forward ciphertext search(FCS-ABMSE)scheme that avoids computationally expensive bilinear pairing operations on the IoT device side.The scheme supportsmulti-keyword fuzzy search without requiring explicit keyword fields,thereby significantly enhancing error tolerance in search operations.It further incorporates forward-secure ciphertext search to mitigate trapdoor abuse,as well as offline encryption and verifiable outsourced decryption to minimize user-side computational costs.Formal security analysis proved that the FCS-ABMSE scheme meets both indistinguishability of ciphertext under the chosen keyword attacks(IND-CKA)and the indistinguishability of ciphertext under the chosen plaintext attacks(IND-CPA).In addition,we constructed an enhanced variant based on type-3 pairings.Results demonstrated that the proposed scheme outperforms existing ABSE approaches in terms of functionalities,computational cost,and communication cost.展开更多
Industrial operators need reliable communication in high-noise,safety-critical environments where speech or touch input is often impractical.Existing gesture systems either miss real-time deadlines on resourceconstrai...Industrial operators need reliable communication in high-noise,safety-critical environments where speech or touch input is often impractical.Existing gesture systems either miss real-time deadlines on resourceconstrained hardware or lose accuracy under occlusion,vibration,and lighting changes.We introduce Industrial EdgeSign,a dual-path framework that combines hardware-aware neural architecture search(NAS)with large multimodalmodel(LMM)guided semantics to deliver robust,low-latency gesture recognition on edge devices.The searched model uses a truncated ResNet50 front end,a dimensional-reduction network that preserves spatiotemporal structure for tubelet-based attention,and localized Transformer layers tuned for on-device inference.To reduce reliance on gloss annotations and mitigate domain shift,we distill semantics from factory-tuned vision-language models and pre-train with masked language modeling and video-text contrastive objectives,aligning visual features with a shared text space.OnML2HP and SHREC’17,theNAS-derived architecture attains 94.7% accuracywith 86ms inference latency and about 5.9W power on Jetson Nano.Under occlusion,lighting shifts,andmotion blur,accuracy remains above 82%.For safetycritical commands,the emergency-stop gesture achieves 72 ms 99th percentile latency with 99.7% fail-safe triggering.Ablation studies confirm the contribution of the spatiotemporal tubelet extractor and text-side pre-training,and we observe gains in translation quality(BLEU-422.33).These results show that Industrial EdgeSign provides accurate,resource-aware,and safety-aligned gesture recognition suitable for deployment in smart factory settings.展开更多
The intelligent environmental sensing systems are quickly transforming the sparse and retrospective monitoring to dense and decision-oriented environmental intelligence.This review brings together the manner in which ...The intelligent environmental sensing systems are quickly transforming the sparse and retrospective monitoring to dense and decision-oriented environmental intelligence.This review brings together the manner in which integration of Internet of Things(IoT)sensing,edge computing,and real-time analytics facilitates timely detection,interpretation,and prediction of the environmental conditions across the applications,such as urban air quality,watershed and coastal surveillance,industrial safety,agriculture,and disaster response.We define end-to-end architectural patterns to organize devices,edge nodes,and cloud services to satisfy latency,reliability,bandwidth,and governance constraints with emphasis on event-time processing,adaptive offloading,and hierarchical aggregation.Then we look at sensing and infrastructure foundations,emphasizing the effects of sensor modality and power autonomy,connectivity,and the practices of calibration on the practicable analytics and eventual plausibility.It is on this basis that we examine real-time analytics pipelines and Artificial Intelligence(AI)techniques to preprocess,sensor combine,anomaly detect,and short-horizon forecast,with a focus on edge-deployable models,quantification of uncertainties,and query resistance to drift and domain shift.Lastly,we address the realities of deployment that condition operational success,such as lifecycle engineering,provenance-aware data management,security and privacy risks,ethical governance,and evaluation methodologies,which place end-to-end latency and field generalization as a priority.This review offers cohesion to algorithmic capabilities and systems engineering and governance to define an overall framework,show open areas of research directions,and provide practical recommendations on how to design trustworthy,scalable,and sustainable environmental monitoring systems.展开更多
The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous c...The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.展开更多
The Routing Protocol for Low-power and Lossy Networks(RPL)is widely used in Internet of Things(IoT)systems,where devices usually have very limited resources.However,RPL still faces several problems,such as high energy...The Routing Protocol for Low-power and Lossy Networks(RPL)is widely used in Internet of Things(IoT)systems,where devices usually have very limited resources.However,RPL still faces several problems,such as high energy usage,unstable links,and inefficient routing decisions,which reduce the overall network performance and lifetime.In this work,we introduce TABURPL,an improved routing method that applies Tabu Search(TS)to optimize the parent selection process.The method uses a combined cost function that considers Residual Energy,Transmission Energy,Distance to the Sink,Hop Count,Expected Transmission Count(ETX),and Link Stability Rate(LSR).Simulation results show that TABURPL improves link stability,lowers energy consumption,and increases the packet delivery ratio compared with standard RPL and other existing approaches.These results indicate that Tabu Search can handle the complex trade-offs in IoT routing and can provide a more reliable solution for extending the network lifetime.展开更多
Accurate prediction of environmental temperature is pivotal for promoting sustainable crop growth.At present,the most effective temperature sensing and prediction system is the Agricultural Internet of Things(AIoT),wh...Accurate prediction of environmental temperature is pivotal for promoting sustainable crop growth.At present,the most effective temperature sensing and prediction system is the Agricultural Internet of Things(AIoT),which deploys a large number of sensors to collect meteorological data and transmits them to the cloud server for prediction.However,this procedure is computationally and communicationally expensive for resourceconstrained AIoT.Recently,Semantic Communication(SC)has shown potential in efficient data transmission,but existing methods overlook the repetitive semantic information whilst sensing data,bringing additional overheads.With the resource-constraint nature of AIoT in mind,we propose the Semantic Communication-enabled Cognitive Agriculture Framework(SC-CAF)for delivering accurate temperature predictions.The proposed SC-CAF incorporates an intelligent analysis layer that performs the temperature prediction and model training and distribution,while a semantic layer transmitting the semantic information extracted from raw data based on the download model,ultimately to reduce communication overheads in AIoT.Furthermore,we propose a novel model called the Light Temperature Semantic Communication(LTSC)by adopting skip-attention and semantic compressor to avoid unnecessary computation and repetitive information,thereby addressing the semantic redundancy issues in sensing data.We also develop a Semantic-based Model Compression(SCMC)algorithm to alleviate the computation and bandwidth burden,enabling AIoT to explore the extensive usage of SC.Experimental results demonstrate that the proposed SC-CAF achieves the lowest prediction error while reducing Floating Point Operations(FLOPs)by 95.88%,memory requirements by 78.30%,Graphics Processing Unit(GPU)power by 50.77%,and time latency by 84.44%,outperforming notable state-of-the-art methods.展开更多
基金Special Fund for Key Technologies in Blockchain of Shanghai Scientific and Technological Committee(23511100300)。
文摘Post-quantum transport layer security(PQ-TLS)is capable of effectively defending against quantum threats to current network communications,whereas its larger public key and certificate sizes as well as higher computational overhead may result in a significant performance reduction compared with conventional TLS.In this paper,we present a systematic evaluation of PQ-TLS performance across diverse deployment scenarios to address the following critical research questions.(1)What is the performance behavior of PQ-TLS across different TLS modes?(2)How does PQ-TLS perform across varying client scales?(3)Which network topology is most suitable for PQ-TLS?(4)How does PQ-TLS perform on personal computers(PCs)compared to embedded IoT devices?To the best of our knowledge,this is the first work to comprehensively address these issues,offering implementers some insights into PQ-TLS performance and guidance for optimizing it across diverse scenarios.
文摘With the transformation from websites to Internet platforms, Chinese young netizens (born in 1990-2005) have become key subjects in the evolution of cyber-nationalism. Based on survey data, this study classifies their nationalism into four types and explores its transformation alongside globalization cognition. The result shows that moderate nationalism is the mainstream. This has raised their attention to globalization, with greater focus on relations between China and developing countries, and nations along the Belt and Road Initiative. Their personal experiences and cultural exposure foster a more inclusive global vision, shaping the evolution of nationalism and global dialogue.
基金funded in part by the Supported by Natural Science Foundation of Inner Mongolia Autonomous Region of China under Grants 2024QN06022 and 2023QN06008in part by the First-Class Discipline Research Special Project under Grant YLXKZX-NGD-015in part by the Inner Mongolia University of Technology Scientific Research Start-Up Project under Grant BS2024067.
文摘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.
文摘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.
文摘The Internet of Things(IoT)ecosystem is inherently heterogeneous,comprising diverse devices that must interoperate seamlessly to enable federated message and data exchange.However,as the number of service requests grows,existing approaches suffer from increased discovery time and degraded Quality of Service(QoS).Moreover,the massive data generated by heterogeneous IoT devices often contain redundancy and noise,posing challenges to efficient data management.To address these issues,this paper proposes a lightweight ontology-based architecture that enhances service discovery and QoS-aware semantic data management.The architecture employs Modified-Ordered Points to Identify theClustering Structure(M-OPTICS)to cluster and eliminate redundant IoT data.The clustered data are then modelled into a lightweight ontology,enabling semantic relationship inference and rule generation through an embedded inference engine.User requests,transmitted via theConstrainedApplication Protocol(CoAP),are semantically enriched and matched to QoS parameters using Dynamic Shannon Entropy optimized with the Salp Swarm Algorithm.Semantic matching is further refined using a bidirectional recurrent neural network(Bi-RNN),while a State–Action–Reward–State–Action(SARSA)reinforcement learning model dynamically defines and updates semantic rules to retrieve themost recent and relevant data across heterogeneous devices.Experimental results demonstrate that the proposed architecture outperforms existing methods in terms of response time,service delay,execution time,precision,recall,and F-score under varying CoAP request loads and communication overheads.The results confirm the effectiveness of the proposed lightweight ontology architecture for service discovery and data management in heterogeneous IoT environments.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University.
文摘Due to the growth of smart cities,many real-time systems have been developed to support smart cities using Internet of Things(IoT)and emerging technologies.They are formulated to collect the data for environment monitoring and automate the communication process.In recent decades,researchers have made many efforts to propose autonomous systems for manipulating network data and providing on-time responses in critical operations.However,the widespread use of IoT devices in resource-constrained applications and mobile sensor networks introduces significant research challenges for cybersecurity.These systems are vulnerable to a variety of cyberattacks,including unauthorized access,denial-of-service attacks,and data leakage,which compromise the network’s security.Additionally,uneven load balancing between mobile IoT devices,which frequently experience link interferences,compromises the trustworthiness of the system.This paper introduces a Multi-Agent secured framework using lightweight edge computing to enhance cybersecurity for sensor networks,aiming to leverage artificial intelligence for adaptive routing and multi-metric trust evaluation to achieve data privacy and mitigate potential threats.Moreover,it enhances the efficiency of distributed sensors for energy consumption through intelligent data analytics techniques,resulting in highly consistent and low-latency network communication.Using simulations,the proposed framework reveals its significant performance compared to state-of-the-art approaches for energy consumption by 43%,latency by 46%,network throughput by 51%,packet loss rate by 40%,and denial of service attacks by 42%.
基金supported by the National Natural Science Foundation of China(Grant No.61762071,Grant No.61163025).
文摘The Internet of Vehicles(IoV)operates in highly dynamic and open network environments and faces serious challenges in secure and real-time authentication and consensus mechanisms.Existing methods often suffer from complex certificate management,inefficient consensus protocols,and poor resilience in high-frequency communication,resulting in high latency,poor scalability,and unstable network performance.To address these issues,this paper proposes a secure and efficient distributed authentication scheme for IoV with reputation-driven consensus and SM9.First,this paper proposes a decentralized authentication architecture that utilizes the certificate-free feature of SM9,enabling lightweight authentication and key negotiation,thereby reducing the complexity of key management.To ensure the traceability and global consistency of authentication data,this scheme also integrates blockchain technology,applying its inherent invariance.Then,this paper introduces a reputation-driven dynamic node grouping mechanism that transparently evaluates and groups’node behavior using smart contracts to enhance network stability.Furthermore,a new RBSFT(Reputation-Based SM9 Friendly-Tolerant)consensus mechanism is proposed for the first time to enhance consensus efficiency by optimizing the PBFT algorithm.RBSFT aims to write authentication information into the blockchain ledger to achieve multi-level optimization of trust management and decision-making efficiency,thereby significantly improving the responsiveness and robustness in high-frequency IoV scenarios.Experimental results show that it excels in authentication,communication efficiency,and computational cost control,making it a feasible solution for achieving IoV security and real-time performance.
文摘The Internet ofThings(IoT)is a new model that evolved with the rapid progress of advanced technology and gained tremendous popularity due to its applications.Anomaly detection haswidely attracted researchers’attention in the last few years,and its effects on diverse applications.This review article covers the various methods and tools developed to perform the task efficiently and automatically in a smart city.In this work,we present a comprehensive literature review(2011 onwards)of three major types of anomalies:network anomalies,sensor anomalies,and videobased anomalies,along with their methods and software tools.Furthermore,anomaly detection methods such as machine learning and deep learning are presented in this work,highlighting their detection strategy techniques,features,applications,issues,and challenges.Moreover,a generic algorithmis also developed to ease the user achieve the taskmore specifically by targeting a specific domain aswell as approach.Comparative studies of three anomalymethods and their analysis identify research discovery areas with their applications.As a result,researchers and practitioners can familiarize themselves with the existing methods for solving real problems,improving methods,and developing new optimum methods for anomaly detection in diverse applications.
文摘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.
基金extend their appreciation to the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R760)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors also extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through small group research under grant number RGP2/714/46.
文摘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.
文摘With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic.This paper presents a comparative benchmark of classic machine learning(ML)and state-of-the-art deep learning(DL)algorithms for IoT intrusion detection.Our methodology employs a twophased approach:a preliminary pilot study using a custom-generated dataset to establish baselines,followed by a comprehensive evaluation on the large-scale CICIoTDataset2023.We benchmarked algorithms including Random Forest,XGBoost,CNN,and StackedLSTM.The results indicate that while top-performingmodels frombothcategories achieve over 99%classification accuracy,this metric masks a crucial performance trade-off.We demonstrate that treebased ML ensembles exhibit superior precision(91%)in identifying benign traffic,making them effective at reducing false positives.Conversely,DL models demonstrate superior recall(96%),making them better suited for minimizing the interruption of legitimate traffic.We conclude that the selection of an optimal model is not merely a matter of maximizing accuracy but is a strategic choice dependent on the specific security priority either minimizing false alarms or ensuring service availability.Thiswork provides a practical framework for deploying context-aware security solutions in diverse IoT environments.
基金co-funded by the European Union under the REFRESH-Research Excellence For REgion Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Programme Just Transitionsupported by the Ministry of Education,Youth and Sports of the Czech Republic conducted by VSB-Technical University of Ostrava,Czechia,under Grants SP2025/021 and SP2025/039。
文摘Practical applications of smart cities and the Internet of Things(IoT)have multiplied,posing many difficulties in network performance,dependability,and security.Concerns of accessibility,reliability,sustainability,and security too have arisen correspondingly because of the decentralized character of the smart city and IoT systems.Fog computing offers a foundation for various applications,including cognitive support,health and social services,intelligent transportation systems,and pervasive computing and communications.Fog computing can help enhance these apps'productivity and lower the end-to-end delay experienced by such time-sensitive applications.In this research,we propose a reliable and secure service delivery strategy at the network edge for smart cities.To improve the availability and dependability,along with the security of smart city applications,the approach employs a combined method uniting distributed fog servers in addition to mist servers with the help of an intrusion detection system.Simulation findings suggest a reduction of 40.3%in the delay incurred by each service request for highly dense areas and 60.6%for moderately dense environments.Furthermore,the system has low false-negative rates and high detection and accuracy rates,decreasing service requests 2%.
文摘Internet of Things(IoT)interconnects devices via network protocols to enable intelligent sensing and control.Resource-constrained IoT devices rely on cloud servers for data storage and processing.However,this cloudassisted architecture faces two critical challenges:the untrusted cloud services and the separation of data ownership from control.Although Attribute-based Searchable Encryption(ABSE)provides fine-grained access control and keyword search over encrypted data,existing schemes lack of error tolerance in exact multi-keyword matching.In this paper,we proposed an attribute-based multi-keyword fuzzy searchable encryption with forward ciphertext search(FCS-ABMSE)scheme that avoids computationally expensive bilinear pairing operations on the IoT device side.The scheme supportsmulti-keyword fuzzy search without requiring explicit keyword fields,thereby significantly enhancing error tolerance in search operations.It further incorporates forward-secure ciphertext search to mitigate trapdoor abuse,as well as offline encryption and verifiable outsourced decryption to minimize user-side computational costs.Formal security analysis proved that the FCS-ABMSE scheme meets both indistinguishability of ciphertext under the chosen keyword attacks(IND-CKA)and the indistinguishability of ciphertext under the chosen plaintext attacks(IND-CPA).In addition,we constructed an enhanced variant based on type-3 pairings.Results demonstrated that the proposed scheme outperforms existing ABSE approaches in terms of functionalities,computational cost,and communication cost.
文摘Industrial operators need reliable communication in high-noise,safety-critical environments where speech or touch input is often impractical.Existing gesture systems either miss real-time deadlines on resourceconstrained hardware or lose accuracy under occlusion,vibration,and lighting changes.We introduce Industrial EdgeSign,a dual-path framework that combines hardware-aware neural architecture search(NAS)with large multimodalmodel(LMM)guided semantics to deliver robust,low-latency gesture recognition on edge devices.The searched model uses a truncated ResNet50 front end,a dimensional-reduction network that preserves spatiotemporal structure for tubelet-based attention,and localized Transformer layers tuned for on-device inference.To reduce reliance on gloss annotations and mitigate domain shift,we distill semantics from factory-tuned vision-language models and pre-train with masked language modeling and video-text contrastive objectives,aligning visual features with a shared text space.OnML2HP and SHREC’17,theNAS-derived architecture attains 94.7% accuracywith 86ms inference latency and about 5.9W power on Jetson Nano.Under occlusion,lighting shifts,andmotion blur,accuracy remains above 82%.For safetycritical commands,the emergency-stop gesture achieves 72 ms 99th percentile latency with 99.7% fail-safe triggering.Ablation studies confirm the contribution of the spatiotemporal tubelet extractor and text-side pre-training,and we observe gains in translation quality(BLEU-422.33).These results show that Industrial EdgeSign provides accurate,resource-aware,and safety-aligned gesture recognition suitable for deployment in smart factory settings.
基金supported by Jiangxi Polytechnic Institute Key Research Topics in Educational Reform 2025-JGJG-07.
文摘The intelligent environmental sensing systems are quickly transforming the sparse and retrospective monitoring to dense and decision-oriented environmental intelligence.This review brings together the manner in which integration of Internet of Things(IoT)sensing,edge computing,and real-time analytics facilitates timely detection,interpretation,and prediction of the environmental conditions across the applications,such as urban air quality,watershed and coastal surveillance,industrial safety,agriculture,and disaster response.We define end-to-end architectural patterns to organize devices,edge nodes,and cloud services to satisfy latency,reliability,bandwidth,and governance constraints with emphasis on event-time processing,adaptive offloading,and hierarchical aggregation.Then we look at sensing and infrastructure foundations,emphasizing the effects of sensor modality and power autonomy,connectivity,and the practices of calibration on the practicable analytics and eventual plausibility.It is on this basis that we examine real-time analytics pipelines and Artificial Intelligence(AI)techniques to preprocess,sensor combine,anomaly detect,and short-horizon forecast,with a focus on edge-deployable models,quantification of uncertainties,and query resistance to drift and domain shift.Lastly,we address the realities of deployment that condition operational success,such as lifecycle engineering,provenance-aware data management,security and privacy risks,ethical governance,and evaluation methodologies,which place end-to-end latency and field generalization as a priority.This review offers cohesion to algorithmic capabilities and systems engineering and governance to define an overall framework,show open areas of research directions,and provide practical recommendations on how to design trustworthy,scalable,and sustainable environmental monitoring systems.
基金appreciation to the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R384)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.
文摘The Routing Protocol for Low-power and Lossy Networks(RPL)is widely used in Internet of Things(IoT)systems,where devices usually have very limited resources.However,RPL still faces several problems,such as high energy usage,unstable links,and inefficient routing decisions,which reduce the overall network performance and lifetime.In this work,we introduce TABURPL,an improved routing method that applies Tabu Search(TS)to optimize the parent selection process.The method uses a combined cost function that considers Residual Energy,Transmission Energy,Distance to the Sink,Hop Count,Expected Transmission Count(ETX),and Link Stability Rate(LSR).Simulation results show that TABURPL improves link stability,lowers energy consumption,and increases the packet delivery ratio compared with standard RPL and other existing approaches.These results indicate that Tabu Search can handle the complex trade-offs in IoT routing and can provide a more reliable solution for extending the network lifetime.
基金supported by the Key Research and Development Project of Hubei Province(No.2024BAB070),China。
文摘Accurate prediction of environmental temperature is pivotal for promoting sustainable crop growth.At present,the most effective temperature sensing and prediction system is the Agricultural Internet of Things(AIoT),which deploys a large number of sensors to collect meteorological data and transmits them to the cloud server for prediction.However,this procedure is computationally and communicationally expensive for resourceconstrained AIoT.Recently,Semantic Communication(SC)has shown potential in efficient data transmission,but existing methods overlook the repetitive semantic information whilst sensing data,bringing additional overheads.With the resource-constraint nature of AIoT in mind,we propose the Semantic Communication-enabled Cognitive Agriculture Framework(SC-CAF)for delivering accurate temperature predictions.The proposed SC-CAF incorporates an intelligent analysis layer that performs the temperature prediction and model training and distribution,while a semantic layer transmitting the semantic information extracted from raw data based on the download model,ultimately to reduce communication overheads in AIoT.Furthermore,we propose a novel model called the Light Temperature Semantic Communication(LTSC)by adopting skip-attention and semantic compressor to avoid unnecessary computation and repetitive information,thereby addressing the semantic redundancy issues in sensing data.We also develop a Semantic-based Model Compression(SCMC)algorithm to alleviate the computation and bandwidth burden,enabling AIoT to explore the extensive usage of SC.Experimental results demonstrate that the proposed SC-CAF achieves the lowest prediction error while reducing Floating Point Operations(FLOPs)by 95.88%,memory requirements by 78.30%,Graphics Processing Unit(GPU)power by 50.77%,and time latency by 84.44%,outperforming notable state-of-the-art methods.
