The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare I...The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.展开更多
Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities f...Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities for the emergence of unprecedented knowledge.To ensure IoT securit,various approaches have been implemented,such as authentication,encoding,as well as devices to guarantee data integrity and availability.Among these approaches,Intrusion Detection Systems(IDS)is an actual security solution,whose performance can be enhanced by integrating various algorithms,including Machine Learning(ML)and Deep Learning(DL),enabling proactive and accurate detection of threats.This study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification algorithm.By combining the strengths of three powerful algorithms,Random Forest(RF),K-Nearest Neighbors(KNN),and Support Vector Machine(SVM)to detect both normal behavior and different categories of attack.Our analysis focuses primarily on the NSL-KDD dataset,while also integrating the recent Edge-IIoT dataset,tailored to industrial IoT environments.Experimental results show significant enhancements on the Edge-IIoT and NSL-KDD datasets,reaching accuracy levels between 72%to 99%,with precision between 87%and 99%,while recall values and F1-scores are also between 72%and 99%,for both normal and attack detection.Despite the promising results of this study,it suffers from certain limitations,notably the use of specific datasets and the lack of evaluations in a variety of environments.Future work could include applying this model to various datasets and evaluating more advanced ensemble strategies,with the aim of further enhancing the effectiveness of IDS.展开更多
The rapid advancement of the Internet of Things(IoT)has led to the proliferation of connected devices across various domains,including smart cities,industrial automation,and healthcare.However,interoperability challen...The rapid advancement of the Internet of Things(IoT)has led to the proliferation of connected devices across various domains,including smart cities,industrial automation,and healthcare.However,interoperability challenges arising from heterogeneous communication protocols,diverse data formats,and fragmented standardization efforts hinder the seamless integration of IoT systems.This paper explores the current state of IoT interoperability,analyzing key challenges,existing standardization initiatives,and emerging technological solutions.We examine the role of middleware,gateway solutions,artificial intelligence(AI),blockchain,and edge computing in facilitating interoperability.Furthermore,we provide a comparative analysis of major IoT standards and discuss the potential for greater convergence among standardization efforts.The findings highlight that while significant progress has been made,a unified and widely accepted interoperability framework remains elusive.Addressing these challenges requires collaborative efforts among industry stakeholders,researchers,and policymakers to establish robust and scalable interoperability solutions,ensuring the continued growth and efficiency of IoT ecosystems.展开更多
Due to their resource constraints,Internet of Things(IoT)devices require authentication mechanisms that are both secure and efficient.Elliptic curve cryptography(ECC)meets these needs by providing strong security with...Due to their resource constraints,Internet of Things(IoT)devices require authentication mechanisms that are both secure and efficient.Elliptic curve cryptography(ECC)meets these needs by providing strong security with shorter key lengths,which significantly reduces the computational overhead required for authentication algorithms.This paper introduces a novel ECC-based IoT authentication system utilizing our previously proposed efficient mapping and reverse mapping operations on elliptic curves over prime fields.By reducing reliance on costly point multiplication,the proposed algorithm significantly improves execution time,storage requirements,and communication cost across varying security levels.The proposed authentication protocol demonstrates superior performance when benchmarked against relevant ECC-based schemes,achieving reductions of up to 35.83%in communication overhead,62.51%in device-side storage consumption,and 71.96%in computational cost.The security robustness of the scheme is substantiated through formal analysis using the Automated Validation of Internet Security Protocols and Applications(AVISPA)tool and Burrows-Abadir-Needham(BAN)logic,complemented by a comprehensive informal analysis that confirms its resilience against various attack models,including impersonation,replay,and man-in-the-middle attacks.Empirical evaluation under simulated conditions demonstrates notable gains in efficiency and security.While these results indicate the protocol’s strong potential for scalable IoT deployments,further validation on real-world embedded platforms is required to confirm its applicability and robustness at scale.展开更多
The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability...The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.展开更多
Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
针对当前换流站一次设备温度监测中非接触式红外测温存在成本高、准确率低、时效性差等问题,提出一种面向高压场景的温度监控方案。该方案结合5G无源物联网(Passive Internet of Things,P-IoT)技术与Transformer模型。通过在高压设备关...针对当前换流站一次设备温度监测中非接触式红外测温存在成本高、准确率低、时效性差等问题,提出一种面向高压场景的温度监控方案。该方案结合5G无源物联网(Passive Internet of Things,P-IoT)技术与Transformer模型。通过在高压设备关键部位部署无源温度传感器,利用反向散射通信技术实现低功耗数据传输,并借助5G网络将数据传输至边缘服务器处理。随后,采用基于Transformer的异常检测模型,通过多头注意力机制有效捕捉温度数据中的时序特征,结合最大池化操作实现对异常温度的准确识别与预警。实验结果表明,该方案在高电磁干扰环境下的传输成功率达到99.0%,在温度异常检测任务中的精度、召回率和F1值分别为98.7%、97.5%和96.9%,显著优于LSTM和GRU等传统时序模型。研究成果验证了所提方法在复杂高压场景下的适用性和稳定性,可为后续在更高电压等级的特高压设备中推广应用奠定技术基础。展开更多
为了有效地实时监测肉鸡生产过程中的环境因子,提高肉鸡健康养殖水平,本文将传感器技术与窄带物联网(Narrow Band Internet of Things,NB-IoT)技术结合,设计并实现一种肉鸡养殖环境监测系统,使用无线传感器实时监测肉鸡养殖环境中的温...为了有效地实时监测肉鸡生产过程中的环境因子,提高肉鸡健康养殖水平,本文将传感器技术与窄带物联网(Narrow Band Internet of Things,NB-IoT)技术结合,设计并实现一种肉鸡养殖环境监测系统,使用无线传感器实时监测肉鸡养殖环境中的温度、湿度、光照、NH 3等环境因子,采用B/S(浏览器/服务器)模式,运用NB-IoT技术将信息传输至云端服务器。结果表明:通过该方法可实现环境数据查询、绘制环境变化曲线、远程向终端设备下发控制命令等功能。该系统具有低功耗、低成本的优势,操作简便、性能稳定,为养殖户的决策分析提供了有效信息和科学依据。展开更多
针对大规模分布式蓄电池管理在实时监控、数据融合及多厂商兼容等方面的挑战,引入基于窄带物联网(Narrow Band Internet of Things,NB-IoT)技术的解决方案。通过构建低功耗NB-IoT监控网络、边缘智能管控机制及标准化接入平台,实现对大...针对大规模分布式蓄电池管理在实时监控、数据融合及多厂商兼容等方面的挑战,引入基于窄带物联网(Narrow Band Internet of Things,NB-IoT)技术的解决方案。通过构建低功耗NB-IoT监控网络、边缘智能管控机制及标准化接入平台,实现对大规模异构蓄电池的高效接入、状态感知与统一管理。在数据中心和智能电网等场景的应用结果表明,NB-IoT方案显著提升了数据传输效率、监控响应速度以及故障预警能力,为分布式储能设施的智能化运维提供了有力支撑。展开更多
文摘The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.
文摘Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities for the emergence of unprecedented knowledge.To ensure IoT securit,various approaches have been implemented,such as authentication,encoding,as well as devices to guarantee data integrity and availability.Among these approaches,Intrusion Detection Systems(IDS)is an actual security solution,whose performance can be enhanced by integrating various algorithms,including Machine Learning(ML)and Deep Learning(DL),enabling proactive and accurate detection of threats.This study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification algorithm.By combining the strengths of three powerful algorithms,Random Forest(RF),K-Nearest Neighbors(KNN),and Support Vector Machine(SVM)to detect both normal behavior and different categories of attack.Our analysis focuses primarily on the NSL-KDD dataset,while also integrating the recent Edge-IIoT dataset,tailored to industrial IoT environments.Experimental results show significant enhancements on the Edge-IIoT and NSL-KDD datasets,reaching accuracy levels between 72%to 99%,with precision between 87%and 99%,while recall values and F1-scores are also between 72%and 99%,for both normal and attack detection.Despite the promising results of this study,it suffers from certain limitations,notably the use of specific datasets and the lack of evaluations in a variety of environments.Future work could include applying this model to various datasets and evaluating more advanced ensemble strategies,with the aim of further enhancing the effectiveness of IDS.
文摘The rapid advancement of the Internet of Things(IoT)has led to the proliferation of connected devices across various domains,including smart cities,industrial automation,and healthcare.However,interoperability challenges arising from heterogeneous communication protocols,diverse data formats,and fragmented standardization efforts hinder the seamless integration of IoT systems.This paper explores the current state of IoT interoperability,analyzing key challenges,existing standardization initiatives,and emerging technological solutions.We examine the role of middleware,gateway solutions,artificial intelligence(AI),blockchain,and edge computing in facilitating interoperability.Furthermore,we provide a comparative analysis of major IoT standards and discuss the potential for greater convergence among standardization efforts.The findings highlight that while significant progress has been made,a unified and widely accepted interoperability framework remains elusive.Addressing these challenges requires collaborative efforts among industry stakeholders,researchers,and policymakers to establish robust and scalable interoperability solutions,ensuring the continued growth and efficiency of IoT ecosystems.
文摘Due to their resource constraints,Internet of Things(IoT)devices require authentication mechanisms that are both secure and efficient.Elliptic curve cryptography(ECC)meets these needs by providing strong security with shorter key lengths,which significantly reduces the computational overhead required for authentication algorithms.This paper introduces a novel ECC-based IoT authentication system utilizing our previously proposed efficient mapping and reverse mapping operations on elliptic curves over prime fields.By reducing reliance on costly point multiplication,the proposed algorithm significantly improves execution time,storage requirements,and communication cost across varying security levels.The proposed authentication protocol demonstrates superior performance when benchmarked against relevant ECC-based schemes,achieving reductions of up to 35.83%in communication overhead,62.51%in device-side storage consumption,and 71.96%in computational cost.The security robustness of the scheme is substantiated through formal analysis using the Automated Validation of Internet Security Protocols and Applications(AVISPA)tool and Burrows-Abadir-Needham(BAN)logic,complemented by a comprehensive informal analysis that confirms its resilience against various attack models,including impersonation,replay,and man-in-the-middle attacks.Empirical evaluation under simulated conditions demonstrates notable gains in efficiency and security.While these results indicate the protocol’s strong potential for scalable IoT deployments,further validation on real-world embedded platforms is required to confirm its applicability and robustness at scale.
基金funded by the Ongoing Research Funding Program(ORF-2025-890)King Saud University,Riyadh,Saudi Arabia and was supported by the Competitive Research Fund of theUniversity of Aizu,Japan.
文摘The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
文摘针对当前换流站一次设备温度监测中非接触式红外测温存在成本高、准确率低、时效性差等问题,提出一种面向高压场景的温度监控方案。该方案结合5G无源物联网(Passive Internet of Things,P-IoT)技术与Transformer模型。通过在高压设备关键部位部署无源温度传感器,利用反向散射通信技术实现低功耗数据传输,并借助5G网络将数据传输至边缘服务器处理。随后,采用基于Transformer的异常检测模型,通过多头注意力机制有效捕捉温度数据中的时序特征,结合最大池化操作实现对异常温度的准确识别与预警。实验结果表明,该方案在高电磁干扰环境下的传输成功率达到99.0%,在温度异常检测任务中的精度、召回率和F1值分别为98.7%、97.5%和96.9%,显著优于LSTM和GRU等传统时序模型。研究成果验证了所提方法在复杂高压场景下的适用性和稳定性,可为后续在更高电压等级的特高压设备中推广应用奠定技术基础。
文摘为了有效地实时监测肉鸡生产过程中的环境因子,提高肉鸡健康养殖水平,本文将传感器技术与窄带物联网(Narrow Band Internet of Things,NB-IoT)技术结合,设计并实现一种肉鸡养殖环境监测系统,使用无线传感器实时监测肉鸡养殖环境中的温度、湿度、光照、NH 3等环境因子,采用B/S(浏览器/服务器)模式,运用NB-IoT技术将信息传输至云端服务器。结果表明:通过该方法可实现环境数据查询、绘制环境变化曲线、远程向终端设备下发控制命令等功能。该系统具有低功耗、低成本的优势,操作简便、性能稳定,为养殖户的决策分析提供了有效信息和科学依据。
文摘针对大规模分布式蓄电池管理在实时监控、数据融合及多厂商兼容等方面的挑战,引入基于窄带物联网(Narrow Band Internet of Things,NB-IoT)技术的解决方案。通过构建低功耗NB-IoT监控网络、边缘智能管控机制及标准化接入平台,实现对大规模异构蓄电池的高效接入、状态感知与统一管理。在数据中心和智能电网等场景的应用结果表明,NB-IoT方案显著提升了数据传输效率、监控响应速度以及故障预警能力,为分布式储能设施的智能化运维提供了有力支撑。
