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
BEIJING,Dec.31(Xinhua)-On New Year's Eve,Chinese President Xi Jinping delivered his 2026 New Year message through China Media Group and the Internet.The following is the full text of the message:Greetings to all!Y...BEIJING,Dec.31(Xinhua)-On New Year's Eve,Chinese President Xi Jinping delivered his 2026 New Year message through China Media Group and the Internet.The following is the full text of the message:Greetings to all!Year after year,life opens a fresh chapter.As the new year begins,I extend my best wishes to you from Beijing!展开更多
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
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
Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods a...Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.展开更多
The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)...The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)is increasingly measured by technical performance,operational usability,and adaptability.This study introduces and rigorously evaluates a Human-Computer Interaction(HCI)-Integrated IDS with the utilization of Convolutional Neural Network(CNN),CNN-Long Short Term Memory(LSTM),and Random Forest(RF)against both a Baseline Machine Learning(ML)and a Traditional IDS model,through an extensive experimental framework encompassing many performance metrics,including detection latency,accuracy,alert prioritization,classification errors,system throughput,usability,ROC-AUC,precision-recall,confusion matrix analysis,and statistical accuracy measures.Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets(CICIDS 2017,KDD Cup 1999,and UNSW-NB15).Experimental results indicate that the HCI-Integrated model outperforms its counterparts,achieving an AUC-ROC of 0.99,a precision of 0.93,and a recall of 0.96,while maintaining the lowest false positive rate(0.03)and the fastest detection time(~1.5 s).These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities,improve responsiveness,and reduce alert fatigue in critical smart city applications.It achieves markedly lower detection times,higher accuracy across all threat categories,reduced false positive and false negative rates,and enhanced system throughput under concurrent load conditions.The HCIIntegrated IDS excels in alert contextualization and prioritization,offering more actionable insights while minimizing analyst fatigue.Usability feedback underscores increased analyst confidence and operational clarity,reinforcing the importance of user-centered design.These results collectively position the HCI-Integrated IDS as a highly effective,scalable,and human-aligned solution for modern threat detection environments.展开更多
A digital project recounts the creation of the most comprehensive book collection in Chinese history and the efforts to protect it from the ravages of war As the 2025 World Internet Conference Wuzhen Summit concluded ...A digital project recounts the creation of the most comprehensive book collection in Chinese history and the efforts to protect it from the ravages of war As the 2025 World Internet Conference Wuzhen Summit concluded last November in East China’s Zhejiang Province,the role of digital technologies in transforming cultural heritage preservation and transmission came into focus.展开更多
The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an...The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.展开更多
With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices ge...With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.展开更多
As embodied intelligence(EI),large language models(LLMs),and cloud computing continue to advance,Industry5.0 facilitates the development of industrial artificial intelligence(Ind AI)through cyber-physical-social syste...As embodied intelligence(EI),large language models(LLMs),and cloud computing continue to advance,Industry5.0 facilitates the development of industrial artificial intelligence(Ind AI)through cyber-physical-social systems(CPSSs)with a human-centric focus.These technologies are organized by the system-wide approach of Industry 5.0,in order to empower the manufacturing industry to achieve broader societal goals of job creation,economic growth,and green production.This survey first provides a general framework of smart manufacturing in the context of Industry 5.0.Wherein,the embodied agents,like robots,sensors,and actuators,are the carriers for Ind AI,facilitating the development of the self-learning intelligence in individual entities,the collaborative intelligence in production lines and factories(smart systems),and the swarm intelligence within industrial clusters(systems of smart systems).Through the framework of CPSSs,the key technologies and their possible applications for supporting the single-agent,multi-agent and swarm-agent embodied Ind AI have been reviewed,such as the embodied perception,interaction,scheduling,multi-mode large language models,and collaborative training.Finally,to stimulate future research in this area,the open challenges and opportunities of applying Industry 5.0 to smart manufacturing are identified and discussed.The perspective of Industry 5.0-driven manufacturing industry aims to enhance operational productivity and efficiency by seamlessly integrating the virtual and physical worlds in a human-centered manner,thereby fostering an intelligent,sustainable,and resilient industrial landscape.展开更多
The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,s...The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.展开更多
Microbatteries(MBs)are crucial to power miniaturized devices for the Internet of Things.In the evolutionary journey of MBs,fabrication technology emerges as the cornerstone,guiding the intricacies of their configurati...Microbatteries(MBs)are crucial to power miniaturized devices for the Internet of Things.In the evolutionary journey of MBs,fabrication technology emerges as the cornerstone,guiding the intricacies of their configuration designs,ensuring precision,and facilitating scalability for mass production.Photolithography stands out as an ideal technology,leveraging its unparalleled resolution,exceptional design flexibility,and entrenched position within the mature semiconductor industry.However,comprehensive reviews on its application in MB development remain scarce.This review aims to bridge that gap by thoroughly assessing the recent status and promising prospects of photolithographic microfabrication for MBs.Firstly,we delve into the fundamental principles and step-by-step procedures of photolithography,offering a nuanced understanding of its operational mechanisms and the criteria for photoresist selection.Subsequently,we highlighted the specific roles of photolithography in the fabrication of MBs,including its utilization as a template for creating miniaturized micropatterns,a protective layer during the etching process,a mold for soft lithography,a constituent of MB active component,and a sacrificial layer in the construction of micro-Swiss-roll structure.Finally,the review concludes with a summary of the key challenges and future perspectives of MBs fabricated by photolithography,providing comprehensive insights and sparking research inspiration in this field.展开更多
Objective Systematically integrate nurses’experience with“Internet Nursing Service”to analysis the nurses’experiences with“Internet Nursing Service”,and to provide a theoretical reference for formulating a more ...Objective Systematically integrate nurses’experience with“Internet Nursing Service”to analysis the nurses’experiences with“Internet Nursing Service”,and to provide a theoretical reference for formulating a more rational“Internet Nursing Service”model.Methods A systematic search in PubMed,Embase,Web of Science,the Cochrane Library,CINAHL,China National Knowledge Infrastructure(CNKI),Wanfang Database,and Chinese Biomedical Literature Database was conducted to collect qualitative research on nurses’experiences with“Internet Nursing Service,”with a retrieval time limit from December 2019 to June 2024.Qualitative meta-synthesis was performed through line-by-line coding of relevant quotes,organization of codes into descriptive themes,and development of analytical themes.Results A total of 19 studies were included,one study was rated as Grade A in quality evaluation,and the remaining studies were rated as Grade B.Collectively synthesized into three integrated results:Harvest and growth,Difficulties and challenges,and Expectations and support.Harvest and growth,include 1)manifestation of self-value,2)enhancing nursing capabilities,3)optimizing nursing resources;Difficulties and challenges,include 1)lack of safety guarantee,2)role conflict;Expectations and support include,1)expectation for professional knowledge and skill training,2)expectations for service platform optimization,3)expectation for reasonable charges,4)expectation for related policy support.Conclusion“Internet Nursing Service”model benefits both nurses and patients,but still full of challenges.It aids in the decentralization of medical resources.Management departments still need to encourage nurses to actively invest in“Internet Nursing Service”while ensuring their safety and interests.展开更多
The Internet of Things(IoT)has gained substantial attention in both academic research and real-world applications.The proliferation of interconnected devices across various domains promises to deliver intelligent and ...The Internet of Things(IoT)has gained substantial attention in both academic research and real-world applications.The proliferation of interconnected devices across various domains promises to deliver intelligent and advanced services.However,this rapid expansion also heightens the vulnerability of the IoT ecosystem to security threats.Consequently,innovative solutions capable of effectively mitigating risks while accommodating the unique constraints of IoT environments are urgently needed.Recently,the convergence of Blockchain technology and IoT has introduced a decentralized and robust framework for securing data and interactions,commonly referred to as the Internet of Blockchained Things(IoBT).Extensive research efforts have been devoted to adapting Blockchain technology to meet the specific requirements of IoT deployments.Within this context,consensus algorithms play a critical role in assessing the feasibility of integrating Blockchain into IoT ecosystems.The adoption of efficient and lightweight consensus mechanisms for block validation has become increasingly essential.This paper presents a comprehensive examination of lightweight,constraint-aware consensus algorithms tailored for IoBT.The study categorizes these consensus mechanisms based on their core operations,the security of the block validation process,the incorporation of AI techniques,and the specific applications they are designed to support.展开更多
1.Introduction Mobile communications have catalyzed a new era of informa-tion technology revolution,significantly broadening and deepen-ing human-to-human,human-to-machine,and machine-to-machine connections.With their...1.Introduction Mobile communications have catalyzed a new era of informa-tion technology revolution,significantly broadening and deepen-ing human-to-human,human-to-machine,and machine-to-machine connections.With their incredible speed of development and wide-reaching impact,mobile communications serve as the cornerstone of the Internet of Everything,profoundly reshaping human cognitive abilities and ways of thinking.Furthermore,mobile communications are altering the patterns of production and life,driving leaps in productivity quality,and strongly promot-ing innovation within human civilization.展开更多
Wireless technologies and the Internet of Things(IoT)are being extensively utilized for advanced development in traditional communication systems.This evolution lowers the cost of the extensive use of sensors,changing...Wireless technologies and the Internet of Things(IoT)are being extensively utilized for advanced development in traditional communication systems.This evolution lowers the cost of the extensive use of sensors,changing the way devices interact and communicate in dynamic and uncertain situations.Such a constantly evolving environment presents enormous challenges to preserving a secure and lightweight IoT system.Therefore,it leads to the design of effective and trusted routing to support sustainable smart cities.This research study proposed a Genetic Algorithm sentiment-enhanced secured optimization model,which combines big data analytics and analysis rules to evaluate user feedback.The sentiment analysis is utilized to assess the perception of network performance,allowing the classification of device behavior as positive,neutral,or negative.By integrating sentiment-driven insights,the IoT network adjusts the system configurations to enhance the performance using network behaviour in terms of latency,reliability,fault tolerance,and sentiment score.Accordingly to the analysis,the proposed model categorizes the behavior of devices as positive,neutral,or negative,facilitating real-time monitoring for crucial applications.Experimental results revealed a significant improvement in the proposed model for threat prevention and network efficiency,demonstrating its resilience for real-time IoT applications.展开更多
In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),dee...In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),deeply embedded in the perception layer architecture of the IoT,play a crucial role as“tactile nerve endings.”A vast number of micro sensor nodes are widely distributed in monitoring areas according to preset deployment strategies,continuously and accurately perceiving and collecting real-time data on environmental parameters such as temperature,humidity,light intensity,air pressure,and pollutant concentration.These data are transmitted to the IoT cloud platform through stable and reliable communication links,forming a massive and detailed basic data resource pool.By using cutting-edge big data processing algorithms,machine learning models,and artificial intelligence analysis tools,in-depth mining and intelligent analysis of these multi-source heterogeneous data are conducted to generate high-value-added decision-making bases.This precisely empowers multiple fields,including agriculture,medical and health care,smart home,environmental science,and industrial manufacturing,driving intelligent transformation and catalyzing society to move towards a new stage of high-quality development.This paper comprehensively analyzes the technical cores of the IoT and WSNs,systematically sorts out the advanced key technologies of WSNs and the evolution of their strategic significance in the IoT system,deeply explores the innovative application scenarios and practical effects of the two in specific vertical fields,and looks forward to the technological evolution trends.It provides a detailed and highly practical guiding reference for researchers,technical engineers,and industrial decision-makers.展开更多
The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by...The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.展开更多
The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic developm...The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic development.This study provides valuable insights into optimizing wireless communication,paving the way for a more connected and productive future in the mining industry.The IoT revolution is advancing across industries,but harsh geometric environments,including open-pit mines,pose unique challenges for reliable communication.The advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency(RF)protocols such as Bluetooth,Wi-Fi,GSM/GPRS,Narrow Band(NB)-IoT,SigFox,ZigBee,and Long Range Wireless Area Network(LoRaWAN).This study addresses the optimization of network implementations by comparing two leading free-spreading IoT-based RF protocols such as ZigBee and LoRaWAN.Intensive field tests are conducted in various opencast mines to investigate coverage potential and signal attenuation.ZigBee is tested in the Tadicherla open-cast coal mine in India.Similarly,LoRaWAN field tests are conducted at one of the associated cement companies(ACC)in the limestone mine in Bargarh,India,covering both Indoor-toOutdoor(I2O)and Outdoor-to-Outdoor(O2O)environments.A robust framework of path-loss models,referred to as Free space,Egli,Okumura-Hata,Cost231-Hata and Ericsson models,combined with key performance metrics,is employed to evaluate the patterns of signal attenuation.Extensive field testing and careful data analysis revealed that the Egli model is the most consistent path-loss model for the ZigBee protocol in an I2O environment,with a coefficient of determination(R^(2))of 0.907,balanced error metrics such as Normalized Root Mean Square Error(NRMSE)of 0.030,Mean Square Error(MSE)of 4.950,Mean Absolute Percentage Error(MAPE)of 0.249 and Scatter Index(SI)of 2.723.In the O2O scenario,the Ericsson model showed superior performance,with the highest R^(2)value of 0.959,supported by strong correlation metrics:NRMSE of 0.026,MSE of 8.685,MAPE of 0.685,Mean Absolute Deviation(MAD)of 20.839 and SI of 2.194.For the LoRaWAN protocol,the Cost-231 model achieved the highest R^(2)value of 0.921 in the I2O scenario,complemented by the lowest metrics:NRMSE of 0.018,MSE of 1.324,MAPE of 0.217,MAD of 9.218 and SI of 1.238.In the O2O environment,the Okumura-Hata model achieved the highest R^(2)value of 0.978,indicating a strong fit with metrics NRMSE of 0.047,MSE of 27.807,MAPE of 27.494,MAD of 37.287 and SI of 3.927.This advancement in reliable communication networks promises to transform the opencast landscape into networked signal attenuation.These results support decision-making for mining needs and ensure reliable communications even in the face of formidable obstacles.展开更多
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BEIJING,Dec.31(Xinhua)-On New Year's Eve,Chinese President Xi Jinping delivered his 2026 New Year message through China Media Group and the Internet.The following is the full text of the message:Greetings to all!Year after year,life opens a fresh chapter.As the new year begins,I extend my best wishes to you from Beijing!
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘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.
文摘Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.
基金funded and supported by the Ongoing Research Funding program(ORF-2025-314),King Saud University,Riyadh,Saudi Arabia.
文摘The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)is increasingly measured by technical performance,operational usability,and adaptability.This study introduces and rigorously evaluates a Human-Computer Interaction(HCI)-Integrated IDS with the utilization of Convolutional Neural Network(CNN),CNN-Long Short Term Memory(LSTM),and Random Forest(RF)against both a Baseline Machine Learning(ML)and a Traditional IDS model,through an extensive experimental framework encompassing many performance metrics,including detection latency,accuracy,alert prioritization,classification errors,system throughput,usability,ROC-AUC,precision-recall,confusion matrix analysis,and statistical accuracy measures.Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets(CICIDS 2017,KDD Cup 1999,and UNSW-NB15).Experimental results indicate that the HCI-Integrated model outperforms its counterparts,achieving an AUC-ROC of 0.99,a precision of 0.93,and a recall of 0.96,while maintaining the lowest false positive rate(0.03)and the fastest detection time(~1.5 s).These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities,improve responsiveness,and reduce alert fatigue in critical smart city applications.It achieves markedly lower detection times,higher accuracy across all threat categories,reduced false positive and false negative rates,and enhanced system throughput under concurrent load conditions.The HCIIntegrated IDS excels in alert contextualization and prioritization,offering more actionable insights while minimizing analyst fatigue.Usability feedback underscores increased analyst confidence and operational clarity,reinforcing the importance of user-centered design.These results collectively position the HCI-Integrated IDS as a highly effective,scalable,and human-aligned solution for modern threat detection environments.
文摘A digital project recounts the creation of the most comprehensive book collection in Chinese history and the efforts to protect it from the ravages of war As the 2025 World Internet Conference Wuzhen Summit concluded last November in East China’s Zhejiang Province,the role of digital technologies in transforming cultural heritage preservation and transmission came into focus.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.
基金supported by the Shandong Province Science and Technology Project(2023TSGC0509,2022TSGC2234)Qingdao Science and Technology Plan Project(23-1-5-yqpy-2-qy)Open Topic Grants of Anhui Province Key Laboratory of Intelligent Building&Building Energy Saving,Anhui Jianzhu University(IBES2024KF08).
文摘With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.
基金supported by the National Key Research and Development Program of China(2021YFB1714300)the National Natural Science Foundation of China(62233005,U2441245,62173141)+3 种基金CNPC Innovation Found(2024DQ02-0507)Shanghai Natural Science(24ZR1416400)Shanghai Baiyu Lan Talent Program Pujiang Project(24PJD020)the Programme of Introducing Talents of Discipline to Universities(the 111 Project)(B17017)
文摘As embodied intelligence(EI),large language models(LLMs),and cloud computing continue to advance,Industry5.0 facilitates the development of industrial artificial intelligence(Ind AI)through cyber-physical-social systems(CPSSs)with a human-centric focus.These technologies are organized by the system-wide approach of Industry 5.0,in order to empower the manufacturing industry to achieve broader societal goals of job creation,economic growth,and green production.This survey first provides a general framework of smart manufacturing in the context of Industry 5.0.Wherein,the embodied agents,like robots,sensors,and actuators,are the carriers for Ind AI,facilitating the development of the self-learning intelligence in individual entities,the collaborative intelligence in production lines and factories(smart systems),and the swarm intelligence within industrial clusters(systems of smart systems).Through the framework of CPSSs,the key technologies and their possible applications for supporting the single-agent,multi-agent and swarm-agent embodied Ind AI have been reviewed,such as the embodied perception,interaction,scheduling,multi-mode large language models,and collaborative training.Finally,to stimulate future research in this area,the open challenges and opportunities of applying Industry 5.0 to smart manufacturing are identified and discussed.The perspective of Industry 5.0-driven manufacturing industry aims to enhance operational productivity and efficiency by seamlessly integrating the virtual and physical worlds in a human-centered manner,thereby fostering an intelligent,sustainable,and resilient industrial landscape.
基金supported in part by the National Natural Science Foundation of China under Grant 62371181in part by the Changzhou Science and Technology International Cooperation Program under Grant CZ20230029+1 种基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2021R1A2B5B02087169)supported under the framework of international cooperation program managed by the National Research Foundation of Korea(2022K2A9A1A01098051)。
文摘The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.
基金supported by the National Natural Science Foundation of China(22125903,22439003,22209175)the National Key R&D Program of China(Grant 2022YFA1504100,2023YFB4005204)+1 种基金the Energy Revolution S&T Program of Yulin Innovation Institute of Clean Energy(Grant E412010508)the State Key Laboratory of Catalysis(No:2024SKL-A-001)。
文摘Microbatteries(MBs)are crucial to power miniaturized devices for the Internet of Things.In the evolutionary journey of MBs,fabrication technology emerges as the cornerstone,guiding the intricacies of their configuration designs,ensuring precision,and facilitating scalability for mass production.Photolithography stands out as an ideal technology,leveraging its unparalleled resolution,exceptional design flexibility,and entrenched position within the mature semiconductor industry.However,comprehensive reviews on its application in MB development remain scarce.This review aims to bridge that gap by thoroughly assessing the recent status and promising prospects of photolithographic microfabrication for MBs.Firstly,we delve into the fundamental principles and step-by-step procedures of photolithography,offering a nuanced understanding of its operational mechanisms and the criteria for photoresist selection.Subsequently,we highlighted the specific roles of photolithography in the fabrication of MBs,including its utilization as a template for creating miniaturized micropatterns,a protective layer during the etching process,a mold for soft lithography,a constituent of MB active component,and a sacrificial layer in the construction of micro-Swiss-roll structure.Finally,the review concludes with a summary of the key challenges and future perspectives of MBs fabricated by photolithography,providing comprehensive insights and sparking research inspiration in this field.
基金supported by the General Project of the Cultivation Project of the Chinese Hospital Reform and Development Research Institute of Nanjing University(NDYG2022072)。
文摘Objective Systematically integrate nurses’experience with“Internet Nursing Service”to analysis the nurses’experiences with“Internet Nursing Service”,and to provide a theoretical reference for formulating a more rational“Internet Nursing Service”model.Methods A systematic search in PubMed,Embase,Web of Science,the Cochrane Library,CINAHL,China National Knowledge Infrastructure(CNKI),Wanfang Database,and Chinese Biomedical Literature Database was conducted to collect qualitative research on nurses’experiences with“Internet Nursing Service,”with a retrieval time limit from December 2019 to June 2024.Qualitative meta-synthesis was performed through line-by-line coding of relevant quotes,organization of codes into descriptive themes,and development of analytical themes.Results A total of 19 studies were included,one study was rated as Grade A in quality evaluation,and the remaining studies were rated as Grade B.Collectively synthesized into three integrated results:Harvest and growth,Difficulties and challenges,and Expectations and support.Harvest and growth,include 1)manifestation of self-value,2)enhancing nursing capabilities,3)optimizing nursing resources;Difficulties and challenges,include 1)lack of safety guarantee,2)role conflict;Expectations and support include,1)expectation for professional knowledge and skill training,2)expectations for service platform optimization,3)expectation for reasonable charges,4)expectation for related policy support.Conclusion“Internet Nursing Service”model benefits both nurses and patients,but still full of challenges.It aids in the decentralization of medical resources.Management departments still need to encourage nurses to actively invest in“Internet Nursing Service”while ensuring their safety and interests.
文摘The Internet of Things(IoT)has gained substantial attention in both academic research and real-world applications.The proliferation of interconnected devices across various domains promises to deliver intelligent and advanced services.However,this rapid expansion also heightens the vulnerability of the IoT ecosystem to security threats.Consequently,innovative solutions capable of effectively mitigating risks while accommodating the unique constraints of IoT environments are urgently needed.Recently,the convergence of Blockchain technology and IoT has introduced a decentralized and robust framework for securing data and interactions,commonly referred to as the Internet of Blockchained Things(IoBT).Extensive research efforts have been devoted to adapting Blockchain technology to meet the specific requirements of IoT deployments.Within this context,consensus algorithms play a critical role in assessing the feasibility of integrating Blockchain into IoT ecosystems.The adoption of efficient and lightweight consensus mechanisms for block validation has become increasingly essential.This paper presents a comprehensive examination of lightweight,constraint-aware consensus algorithms tailored for IoBT.The study categorizes these consensus mechanisms based on their core operations,the security of the block validation process,the incorporation of AI techniques,and the specific applications they are designed to support.
基金supported by the National Key Research and Develop-ment Program of China(2019YFB1803400).
文摘1.Introduction Mobile communications have catalyzed a new era of informa-tion technology revolution,significantly broadening and deepen-ing human-to-human,human-to-machine,and machine-to-machine connections.With their incredible speed of development and wide-reaching impact,mobile communications serve as the cornerstone of the Internet of Everything,profoundly reshaping human cognitive abilities and ways of thinking.Furthermore,mobile communications are altering the patterns of production and life,driving leaps in productivity quality,and strongly promot-ing innovation within human civilization.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University under Grant No.DGSSR-2024-02-01011.
文摘Wireless technologies and the Internet of Things(IoT)are being extensively utilized for advanced development in traditional communication systems.This evolution lowers the cost of the extensive use of sensors,changing the way devices interact and communicate in dynamic and uncertain situations.Such a constantly evolving environment presents enormous challenges to preserving a secure and lightweight IoT system.Therefore,it leads to the design of effective and trusted routing to support sustainable smart cities.This research study proposed a Genetic Algorithm sentiment-enhanced secured optimization model,which combines big data analytics and analysis rules to evaluate user feedback.The sentiment analysis is utilized to assess the perception of network performance,allowing the classification of device behavior as positive,neutral,or negative.By integrating sentiment-driven insights,the IoT network adjusts the system configurations to enhance the performance using network behaviour in terms of latency,reliability,fault tolerance,and sentiment score.Accordingly to the analysis,the proposed model categorizes the behavior of devices as positive,neutral,or negative,facilitating real-time monitoring for crucial applications.Experimental results revealed a significant improvement in the proposed model for threat prevention and network efficiency,demonstrating its resilience for real-time IoT applications.
文摘In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),deeply embedded in the perception layer architecture of the IoT,play a crucial role as“tactile nerve endings.”A vast number of micro sensor nodes are widely distributed in monitoring areas according to preset deployment strategies,continuously and accurately perceiving and collecting real-time data on environmental parameters such as temperature,humidity,light intensity,air pressure,and pollutant concentration.These data are transmitted to the IoT cloud platform through stable and reliable communication links,forming a massive and detailed basic data resource pool.By using cutting-edge big data processing algorithms,machine learning models,and artificial intelligence analysis tools,in-depth mining and intelligent analysis of these multi-source heterogeneous data are conducted to generate high-value-added decision-making bases.This precisely empowers multiple fields,including agriculture,medical and health care,smart home,environmental science,and industrial manufacturing,driving intelligent transformation and catalyzing society to move towards a new stage of high-quality development.This paper comprehensively analyzes the technical cores of the IoT and WSNs,systematically sorts out the advanced key technologies of WSNs and the evolution of their strategic significance in the IoT system,deeply explores the innovative application scenarios and practical effects of the two in specific vertical fields,and looks forward to the technological evolution trends.It provides a detailed and highly practical guiding reference for researchers,technical engineers,and industrial decision-makers.
基金described in this paper has been developed with in the project PRESECREL(PID2021-124502OB-C43)。
文摘The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.
文摘The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic development.This study provides valuable insights into optimizing wireless communication,paving the way for a more connected and productive future in the mining industry.The IoT revolution is advancing across industries,but harsh geometric environments,including open-pit mines,pose unique challenges for reliable communication.The advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency(RF)protocols such as Bluetooth,Wi-Fi,GSM/GPRS,Narrow Band(NB)-IoT,SigFox,ZigBee,and Long Range Wireless Area Network(LoRaWAN).This study addresses the optimization of network implementations by comparing two leading free-spreading IoT-based RF protocols such as ZigBee and LoRaWAN.Intensive field tests are conducted in various opencast mines to investigate coverage potential and signal attenuation.ZigBee is tested in the Tadicherla open-cast coal mine in India.Similarly,LoRaWAN field tests are conducted at one of the associated cement companies(ACC)in the limestone mine in Bargarh,India,covering both Indoor-toOutdoor(I2O)and Outdoor-to-Outdoor(O2O)environments.A robust framework of path-loss models,referred to as Free space,Egli,Okumura-Hata,Cost231-Hata and Ericsson models,combined with key performance metrics,is employed to evaluate the patterns of signal attenuation.Extensive field testing and careful data analysis revealed that the Egli model is the most consistent path-loss model for the ZigBee protocol in an I2O environment,with a coefficient of determination(R^(2))of 0.907,balanced error metrics such as Normalized Root Mean Square Error(NRMSE)of 0.030,Mean Square Error(MSE)of 4.950,Mean Absolute Percentage Error(MAPE)of 0.249 and Scatter Index(SI)of 2.723.In the O2O scenario,the Ericsson model showed superior performance,with the highest R^(2)value of 0.959,supported by strong correlation metrics:NRMSE of 0.026,MSE of 8.685,MAPE of 0.685,Mean Absolute Deviation(MAD)of 20.839 and SI of 2.194.For the LoRaWAN protocol,the Cost-231 model achieved the highest R^(2)value of 0.921 in the I2O scenario,complemented by the lowest metrics:NRMSE of 0.018,MSE of 1.324,MAPE of 0.217,MAD of 9.218 and SI of 1.238.In the O2O environment,the Okumura-Hata model achieved the highest R^(2)value of 0.978,indicating a strong fit with metrics NRMSE of 0.047,MSE of 27.807,MAPE of 27.494,MAD of 37.287 and SI of 3.927.This advancement in reliable communication networks promises to transform the opencast landscape into networked signal attenuation.These results support decision-making for mining needs and ensure reliable communications even in the face of formidable obstacles.
