There are numerous internet-connected devices attached to the industrial process through recent communication technologies,which enable machine-to-machine communication and the sharing of sensitive data through a new ...There are numerous internet-connected devices attached to the industrial process through recent communication technologies,which enable machine-to-machine communication and the sharing of sensitive data through a new technology called the industrial internet of things(IIoTs).Most of the suggested security mechanisms are vulnerable to several cybersecurity threats due to their reliance on cloud-based services,external trusted authorities,and centralized architectures;they have high computation and communication costs,low performance,and are exposed to a single authority of failure and bottleneck.Blockchain technology(BC)is widely adopted in the industrial sector for its valuable features in terms of decentralization,security,and scalability.In our work,we propose a decentralized,scalable,lightweight,trusted and secure private network based on blockchain technology/smart contracts for the overhead circuit breaker of the electrical power grid of the Al-Kufa/Iraq power plant as an industrial application.The proposed scheme offers a double layer of data encryption,device authentication,scalability,high performance,low power consumption,and improves the industry’s operations;provides efficient access control to the sensitive data generated by circuit breaker sensors and helps reduce power wastage.We also address data aggregation operations,which are considered challenging in electric power smart grids.We utilize a multi-chain proof of rapid authentication(McPoRA)as a consensus mechanism,which helps to enhance the computational performance and effectively improve the latency.The advanced reduced instruction set computer(RISC)machinesARMCortex-M33 microcontroller adopted in our work,is characterized by ultra-low power consumption and high performance,as well as efficiency in terms of real-time cryptographic algorithms such as the elliptic curve digital signature algorithm(ECDSA).This improves the computational execution,increases the implementation speed of the asymmetric cryptographic algorithm and provides data integrity and device authenticity at the perceptual layer.Our experimental results show that the proposed scheme achieves excellent performance,data security,real-time data processing,low power consumption(70.880 mW),and very low memory utilization(2.03%read-only memory(RAM)and 0.9%flash memory)and execution time(0.7424 s)for the cryptographic algorithm.This enables autonomous network reconfiguration on-demand and real-time data processing.展开更多
This paper introduces a novel methodology to address the issue of destination carrying capacity assessment and monitoring,while,to develop a new tool to enable effective and optimized policy making in tourism sector.T...This paper introduces a novel methodology to address the issue of destination carrying capacity assessment and monitoring,while,to develop a new tool to enable effective and optimized policy making in tourism sector.Tourism fits together a chain value,where stakeholders are working as cogwheels targeting the sustainability.An inherent requirement towards the objectives of sustainability is the capacity of policy makers to sustain the available social,economic,and environmental resources.Given the facts that(i)carrying capacity is a complex,dynamic,multidimensional concept,apparently vague,as such it is quite difficult to assess and monitor and(ii)each destination has unique characteristics,varying the extent and intensity of tourism development,the research proposal refines and implements measures and establishes a state-of-the-art data collection network in order to monitor tourism development in terms of sustainability and to establish an integrated carrying capacity estimation framework.Since validity and reliability of input data is a critical issue,different kind of sensors for environmental data(IoTs)are used,as well as web mining methods and field surveys,statistical information and secondary research results.Also,specialized analysis methods are applied and integration of all sources is achieved,defining the characteristics of the tourism destination ecosystem from the perspective of entropy change,in order to provide planning,policy making,and decision support,as well as a set of intelligent tools.展开更多
IPv6 over Low PowerWireless Personal Area Network(6LoWPAN)provides IP connectivity to the highly constrained nodes in the Internet of Things(IoTs).6LoWPANallows nodeswith limited battery power and storage capacity to ...IPv6 over Low PowerWireless Personal Area Network(6LoWPAN)provides IP connectivity to the highly constrained nodes in the Internet of Things(IoTs).6LoWPANallows nodeswith limited battery power and storage capacity to carry IPv6 datagrams over the lossy and error-prone radio links offered by the IEEE 802.15.4 standard,thus acting as an adoption layer between the IPv6 protocol and IEEE 802.15.4 network.The data link layer of IEEE 802.15.4 in 6LoWPAN is based on AES(Advanced Encryption Standard),but the 6LoWPANstandard lacks and has omitted the security and privacy requirements at higher layers.The sensor nodes in 6LoWPANcan join the network without requiring the authentication procedure.Therefore,from security perspectives,6LoWPAN is vulnerable to many attacks such as replay attack,Man-in-the-Middle attack,Impersonation attack,and Modification attack.This paper proposes a secure and efficient cluster-based authentication scheme(CBAS)for highly constrained sensor nodes in 6LoWPAN.In this approach,sensor nodes are organized into a cluster and communicate with the central network through a dedicated sensor node.The main objective of CBAS is to provide efficient and authentic communication among the 6LoWPAN nodes.To ensure the low signaling overhead during the registration,authentication,and handover procedures,we also introduce lightweight and efficient registration,de-registration,initial authentication,and handover procedures,when a sensor node or group of sensor nodes join or leave a cluster.Our security analysis shows that the proposed CBAS approach protects against various security attacks,including Identity Confidentiality attack,Modification attack,Replay attack,Man-in-the-middle attack,and Impersonation attack.Our simulation experiments show that CBAS has reduced the registration delay by 11%,handoff authentication delay by 32%,and signaling cost by 37%compared to the SGMS(Secure GroupMobility Scheme)and LAMS(Light-Wight Authentication&Mobility Scheme).展开更多
Automated attendance management system will reduce complexity by eliminating plenty of manual processes involved in attendance system and calculating hours attended. This paper presents a simple technique of taking st...Automated attendance management system will reduce complexity by eliminating plenty of manual processes involved in attendance system and calculating hours attended. This paper presents a simple technique of taking student attendance in the form of an Internet of Things (IoT) based system that records the attendance using fingerprint-based system and stores them securely in a database. We use NodeMCUV3, RFID Module and Fingerprint sensor module in our system. The fingerprint module is responsible for authentication of the students. RFID Module is used to scan the RFID tag and sends data to the central server. By using this information, the system will generate an attendance report which can be accessed for further use.展开更多
为解决传统电平交叉模数转换器(LC ADC)精度较低和噪声整形逐次逼近寄存器(NS SAR)ADC功耗较大的问题,提出了一种应用于移动物联网(IoT)随机稀疏信号采集的LC-NS SAR ADC。在NS SAR ADC前端插入8 bit的LC ADC作为输入信号活跃度的预检...为解决传统电平交叉模数转换器(LC ADC)精度较低和噪声整形逐次逼近寄存器(NS SAR)ADC功耗较大的问题,提出了一种应用于移动物联网(IoT)随机稀疏信号采集的LC-NS SAR ADC。在NS SAR ADC前端插入8 bit的LC ADC作为输入信号活跃度的预检测电路,在电平交叉发生后开启NS SAR ADC的转换。二阶无源噪声整形电路积分过程只在事件触发后发生,从而能够根据输入信号的活跃度动态调节整体功耗。在1.8 V 180 nm CMOS工艺、采样率为40 kS/s、过采样率(OSR)为20、带宽为1 kHz下对该ADC进行仿真验证,结果表明信噪失真比(SNDR)达到87 dB,电路功耗为2.70μW,心电图信号输入时功耗仅为0.79μW,相较于传统等间隔奈奎斯特采样ADC,采样点减少了73%,在处理生物医学信号时实现了约5∶1的数据压缩比,Schreier品质因数(FoMs)和Walden品质因数(FoMw)分别为172.6 dB和67.0 fJ/conv.step。展开更多
Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compro...Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compromises both system reliability and operational continuity.Existing hybrid IDS solutions often struggle to balance accurate classification of known attacks with reliable anomaly detection,particularly under the computational constraints of IoT environments.To address this gap,we introduce ZeroDefense,an adaptive fusion-based IDS designed for simultaneous detection of known intrusions and emerging zero-day threats.The framework employs a four-layer architecture consisting of i)feature standardization and class balancing,ii)anomaly detection using isolation forest,autoencoder,and local outlier factor,iii)fine-grained attack classification via random forest,extreme gradient boosting(XGBoost),light gradient boosting machine(LightGBM),and attentive interpretable tabular learning(TabNet),and iv)a confidence-aware fusion engine that adaptively selects the most reliable decision path.Suspicious or previously unseen traffic is isolated early through fused anomaly scoring,while benign and known-malicious flows are processed through supervised classification for precise attack labeling.With an anomaly cascaded decision pipeline,a dynamic confidence-driven fusion mechanism,and a deploymentconscious design,ZeroDefense enables real-time inference on IoT edge gateways.Evaluation on the CICIoT2023 benchmark demonstrates 99.94% overall accuracy and 95.64%macro-average F1-score for known attacks,while 5.76% of traffic is successfully flagged as potential zero-day activity,with inference latency maintained below 100 ms/flow.These results indicate that ZeroDefense offers a scalable,resilient,and practically deployable defense capability for modern IoT infrastructures.展开更多
The modern world remains vulnerable to natural disasters,including floods,earthquakes,wildfires,and others.These events remain unpredictable and inevitable,and recovering quickly and effectively requires significant e...The modern world remains vulnerable to natural disasters,including floods,earthquakes,wildfires,and others.These events remain unpredictable and inevitable,and recovering quickly and effectively requires significant effort and expense.Monitoring is becoming more efficient thanks to technologies such as Unmanned Aerial Vehicles(UAVs),which can access hard-to-reach areas and provide real-time data.However,in disaster-affected areas,these monitoring systems may encounter many obstacles when communicating with servers or transmitting monitored data.This paper proposes an adaptive communication model to overcome the challenges faced in disaster-affected areas.A base station is responsible for collecting data(such as images and videos)captured by UAVs performing surveillance within its communication range.This station is typically a tower providing fixed cellular network service.However,in the absence of such a tower,a selected UAV may serve as the station,depending on the situation.If surveillance needs to be performed outside the coverage area,it can continue to communicate via nearby UAVs through cooperative communication.UAVs with internet support,known as the Internet of Flying Things(IoFT),will also be utilized to enhance communication capacity and efficiency.The proposed communication model is validated through experiments,showing superior data transmission performance and higher throughput.Analysis indicates it outperforms traditional systems,even in rural areas,with or without internet access.展开更多
Location-Based Services(LBS)have greatly improved efficiency and functionality in various domains,but privacy and security concerns remain due to the centralized nature of many existing systems.To address these issues...Location-Based Services(LBS)have greatly improved efficiency and functionality in various domains,but privacy and security concerns remain due to the centralized nature of many existing systems.To address these issues,this paper introduces the V-Track system,a decentralized architecture using blockchain technology for reliable vehicle location verification.By integrating GPS devices(Spark Fun GPS NEO-M9),IoT-enabled sensors,and a Cosmos blockchain-based ledger(network of interconnected blockchains),V-Track aims to solve centralized LBS problems.Through rigorous simulation experiments,this paper evaluates the performance and security of the V-Track system and demonstrates its potential to provide reliable location verification while preserving user privacy.This paper makes significant contributions by presenting V-Track as a decentralized solution to centralized LBS privacy and security problems,enhancing reliability and trustworthiness through blockchain integration,improving tracking mechanisms with GPS devices and IoT sensors for improved accuracy,and providing a privacy-preserving alternative to centralized LBS through its decentralized design and use of blockchain technology.These advancements hold promise for applications across multiple sectors,including logistics,supply chain management,urban planning,and emerging fields such as autonomous vehicles and augmented reality.展开更多
Spectrum sensing is an indispensable core part of cognitive radio dynamic spectrum access(DSA)and a key approach to alleviating spectrum scarcity in the Internet of Things(IoT).The key issue in practical IoT networks ...Spectrum sensing is an indispensable core part of cognitive radio dynamic spectrum access(DSA)and a key approach to alleviating spectrum scarcity in the Internet of Things(IoT).The key issue in practical IoT networks is robust sensing under the coexistence of low signal-to-noise ratios(SNRs)and non-Gaussian impulsive noise,where observations may be distorted differently across feature modalities,making conventional fusion unstable and degrading detection reliability.To address this challenge,the generalized Gaussian distribution(GGD)is adopted as the noise model,and a multimodal fusion framework termed BCAM-Net(bidirectional cross-attention multimodal network)is proposed.BCAM-Net adopts a parallel dual-branch architecture:a time-frequency branch that leverages the continuous wavelet transform(CWT)to extract time-frequency representations,and a temporal branch that learns long-range dependencies from raw signals.BCAM-Net utilizes a bidirectional cross-attention mechanism to achieve deep alignment and mutual calibration of temporal and time-frequency features,generating a fused representation that is highly robust to complex noise.Simulation results show that,under GGD noise with shape parameterβ=0.5,BCAM-Net achieves high detection probabilities in the low-SNR regime and outperforms representative baselines.At a false alarm probability Pf=0.1 and SNR of−14 dB,it attains a detection probability of 0.9020,exceeding the CNN-Transformer,WT-ResNet,TFCFN,and conventional CNN benchmarks by 5.75%,6.98%,33.3%,and 21.1%,respectively.These results indicate that BCAM-Net can effectively improve spectrum sensing performance in low-SNR impulsive-noise scenarios,and provides a lightweight,high-performance solution for practical cognitive radio spectrum sensing.展开更多
建筑信息模型(Building Information Modeling,BIM)、地理信息系统(Geographic Information System,GIS)与物联网(Internet of Things,IoT)技术的发展,为构建面向智慧城市的一体化运维管理平台提供了技术基础。文章围绕智慧城市治理需求...建筑信息模型(Building Information Modeling,BIM)、地理信息系统(Geographic Information System,GIS)与物联网(Internet of Things,IoT)技术的发展,为构建面向智慧城市的一体化运维管理平台提供了技术基础。文章围绕智慧城市治理需求,提出BIM-GIS-IoT三维融合的运维管理平台架构,重点研究多源异构数据集成与模型融合、IoT实时监测、三维可视化展示、设施健康诊断与预测性运维等关键技术,以提升城市基础设施的运维效率、风险预警能力与决策科学性,为智慧城市建设提供新的技术路径与管理模式。展开更多
The Internet of Things(IoT)and cloud computing have significantly contributed to the development of smart cities,enabling real-time monitoring,intelligent decision-making,and efficient resource management.These system...The Internet of Things(IoT)and cloud computing have significantly contributed to the development of smart cities,enabling real-time monitoring,intelligent decision-making,and efficient resource management.These systems,particularly in IoT networks,rely on numerous interconnected devices that handle time-sensitive data for critical applications.In related approaches,trusted communication and reliable device interaction have been overlooked,thereby lowering security when sharing sensitive IoT data.Moreover,it incurs additional energy consumption and overhead while addressing potential threats in the dynamic environment.In this research,an Artificial Intelligence(AI)recommended fault-tolerant framework is proposed that leverages blockchain technology,aiming to enhance device trustworthiness and ensure data privacy.In addition,the intelligence of the proposed framework enables more authentic and authorized device involvement in data routing,thereby enabling seamless transmission in smart cities integrated with lightweight computing.To evaluate dynamic network conditions,the proposed framework offers a timely decision-making system to ensure robust delivery of IoT-assisted services.Using simulations,the efficacy of the proposed framework is validated by comparing it with existing approaches across various network metrics,demonstrating remarkable performance while achieving energy efficiency and optimizing network resources.展开更多
The large-scale deployment of Internet of Things(IoT)technology across various aspects of daily life has significantly propelled the intelligent development of society.Among them,the integration of IoT and named data ...The large-scale deployment of Internet of Things(IoT)technology across various aspects of daily life has significantly propelled the intelligent development of society.Among them,the integration of IoT and named data networks(NDNs)reduces network complexity and provides practical directions for content-oriented network design.However,ensuring data integrity in NDN-IoT applications remains a challenging issue.Very recently,Wang et al.(Entropy,27(5),471(2025))designed a certificateless aggregate signature(CLAS)scheme for NDN-IoT environments.Wang et al.stated that their construction was provably secure under various types of security attacks.Using theoretical analysis methods,in this work,we reveal that their CLAS design fails to meet unforgeability,a core security requirement for CLAS schemes.In particular,we demonstrate that their scheme is vulnerable to amalicious public-key replacement attack,enabling an adversary to produce authentic signatures for arbitrary fraudulent messages.Therefore,Wang et al.’s design cannot achieve its goal.To address the issue,we systematically examine the root causes behind the vulnerability and propose a security-enhanced CLAS construction for NDN-IoT environments.We prove the security ofour improveddesignunder the standard security assumptionandalsoanalyze its practicalperformanceby comparing the computational and communication costs with several related works.The comparison results show the practicality of our design.展开更多
随着建筑行业的快速发展和智能技术的广泛应用,建筑机电安装工程中管道施工的质量管理问题日益受到重视。传统质量控制方式存在依赖人工、信息化水平低、反馈滞后等问题,难以满足智能建筑对施工精度、安全性和运行可靠性的高要求。文章...随着建筑行业的快速发展和智能技术的广泛应用,建筑机电安装工程中管道施工的质量管理问题日益受到重视。传统质量控制方式存在依赖人工、信息化水平低、反馈滞后等问题,难以满足智能建筑对施工精度、安全性和运行可靠性的高要求。文章提出一种基于信息化与智能技术的管道施工质量控制体系,结合物联网(Internet of Things,IoT)、BIM、智能传感与大数据分析手段,构建全过程质量管控框架,并在实际工程中进行应用验证。研究表明,该体系能够有效提升施工过程的可视化程度,减少质量缺陷,提高管道系统的整体施工水平和运行可靠性。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
Malware poses a significant threat to the Internet of Things(IoT).It enables unauthorized access to devices in the IoT environment.The lack of unique architectural standards causes challenges in developing robust malw...Malware poses a significant threat to the Internet of Things(IoT).It enables unauthorized access to devices in the IoT environment.The lack of unique architectural standards causes challenges in developing robust malware detection(MD)models.The existing models demand substantial computational resources.This study intends to build a lightweight MD model to detect anomalies in IoT networks.The authors develop a transformation technique,converting the malware binaries into images.MobileNet V2 is fine-tuned using improved grey wolf optimization(IGWO)to extract crucial features of malicious and benign samples.The ResNeXt model is combined with the Linformer’s attention mechanism to identify Malware features.A fully connected layer is integrated with gradientweighted class activation mapping(Grad-CAM)in order to facilitate an interpretable classification model.The proposed model is evaluated using the IoT malware and the IoT-23 datasets.The model performs well on the two datasets with an accuracy of 98.94%,precision of 98.46%,recall of 98.11%,and F1-score of 98.28%on the IoT malware dataset,and an accuracy of 98.23%,precision of 96.80%,recall of 96.64%,and F1-score of 96.71%on the IoT-23 dataset,respectively.The findings indicate that the model has a high standard of classification.The lightweight architecture enables efficient deployment with an inference time of 1.42 s.Inference time has no direct impact on accuracy,precision,recall,or F1-score.However,the inference speed would warrant timely detection in latency-sensitive IoT applications.By achieving a remarkable result,the proposed study offers a comprehensive solution:a scalable,interpretable,and computationally efficient MD model for the evolving IoT landscape.展开更多
With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comp...With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.展开更多
基金This work is supported by the National Key R&D Program of China under Grand No.2021YFB2012202the Key Research Development Plan of Hubei Province of China under Grant No.2021BAA171,2021BAA038the project of Science Technology and Innovation Commission of Shenzhen Municipality of China under Grant No.JCYJ20210324120002006 and JSGG20210802153009028.
文摘There are numerous internet-connected devices attached to the industrial process through recent communication technologies,which enable machine-to-machine communication and the sharing of sensitive data through a new technology called the industrial internet of things(IIoTs).Most of the suggested security mechanisms are vulnerable to several cybersecurity threats due to their reliance on cloud-based services,external trusted authorities,and centralized architectures;they have high computation and communication costs,low performance,and are exposed to a single authority of failure and bottleneck.Blockchain technology(BC)is widely adopted in the industrial sector for its valuable features in terms of decentralization,security,and scalability.In our work,we propose a decentralized,scalable,lightweight,trusted and secure private network based on blockchain technology/smart contracts for the overhead circuit breaker of the electrical power grid of the Al-Kufa/Iraq power plant as an industrial application.The proposed scheme offers a double layer of data encryption,device authentication,scalability,high performance,low power consumption,and improves the industry’s operations;provides efficient access control to the sensitive data generated by circuit breaker sensors and helps reduce power wastage.We also address data aggregation operations,which are considered challenging in electric power smart grids.We utilize a multi-chain proof of rapid authentication(McPoRA)as a consensus mechanism,which helps to enhance the computational performance and effectively improve the latency.The advanced reduced instruction set computer(RISC)machinesARMCortex-M33 microcontroller adopted in our work,is characterized by ultra-low power consumption and high performance,as well as efficiency in terms of real-time cryptographic algorithms such as the elliptic curve digital signature algorithm(ECDSA).This improves the computational execution,increases the implementation speed of the asymmetric cryptographic algorithm and provides data integrity and device authenticity at the perceptual layer.Our experimental results show that the proposed scheme achieves excellent performance,data security,real-time data processing,low power consumption(70.880 mW),and very low memory utilization(2.03%read-only memory(RAM)and 0.9%flash memory)and execution time(0.7424 s)for the cryptographic algorithm.This enables autonomous network reconfiguration on-demand and real-time data processing.
文摘This paper introduces a novel methodology to address the issue of destination carrying capacity assessment and monitoring,while,to develop a new tool to enable effective and optimized policy making in tourism sector.Tourism fits together a chain value,where stakeholders are working as cogwheels targeting the sustainability.An inherent requirement towards the objectives of sustainability is the capacity of policy makers to sustain the available social,economic,and environmental resources.Given the facts that(i)carrying capacity is a complex,dynamic,multidimensional concept,apparently vague,as such it is quite difficult to assess and monitor and(ii)each destination has unique characteristics,varying the extent and intensity of tourism development,the research proposal refines and implements measures and establishes a state-of-the-art data collection network in order to monitor tourism development in terms of sustainability and to establish an integrated carrying capacity estimation framework.Since validity and reliability of input data is a critical issue,different kind of sensors for environmental data(IoTs)are used,as well as web mining methods and field surveys,statistical information and secondary research results.Also,specialized analysis methods are applied and integration of all sources is achieved,defining the characteristics of the tourism destination ecosystem from the perspective of entropy change,in order to provide planning,policy making,and decision support,as well as a set of intelligent tools.
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation,Ministry of Education,Kingdom of Saudi Arabia for this research through a Grant(NU/IFC/INT/01/008)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘IPv6 over Low PowerWireless Personal Area Network(6LoWPAN)provides IP connectivity to the highly constrained nodes in the Internet of Things(IoTs).6LoWPANallows nodeswith limited battery power and storage capacity to carry IPv6 datagrams over the lossy and error-prone radio links offered by the IEEE 802.15.4 standard,thus acting as an adoption layer between the IPv6 protocol and IEEE 802.15.4 network.The data link layer of IEEE 802.15.4 in 6LoWPAN is based on AES(Advanced Encryption Standard),but the 6LoWPANstandard lacks and has omitted the security and privacy requirements at higher layers.The sensor nodes in 6LoWPANcan join the network without requiring the authentication procedure.Therefore,from security perspectives,6LoWPAN is vulnerable to many attacks such as replay attack,Man-in-the-Middle attack,Impersonation attack,and Modification attack.This paper proposes a secure and efficient cluster-based authentication scheme(CBAS)for highly constrained sensor nodes in 6LoWPAN.In this approach,sensor nodes are organized into a cluster and communicate with the central network through a dedicated sensor node.The main objective of CBAS is to provide efficient and authentic communication among the 6LoWPAN nodes.To ensure the low signaling overhead during the registration,authentication,and handover procedures,we also introduce lightweight and efficient registration,de-registration,initial authentication,and handover procedures,when a sensor node or group of sensor nodes join or leave a cluster.Our security analysis shows that the proposed CBAS approach protects against various security attacks,including Identity Confidentiality attack,Modification attack,Replay attack,Man-in-the-middle attack,and Impersonation attack.Our simulation experiments show that CBAS has reduced the registration delay by 11%,handoff authentication delay by 32%,and signaling cost by 37%compared to the SGMS(Secure GroupMobility Scheme)and LAMS(Light-Wight Authentication&Mobility Scheme).
文摘Automated attendance management system will reduce complexity by eliminating plenty of manual processes involved in attendance system and calculating hours attended. This paper presents a simple technique of taking student attendance in the form of an Internet of Things (IoT) based system that records the attendance using fingerprint-based system and stores them securely in a database. We use NodeMCUV3, RFID Module and Fingerprint sensor module in our system. The fingerprint module is responsible for authentication of the students. RFID Module is used to scan the RFID tag and sends data to the central server. By using this information, the system will generate an attendance report which can be accessed for further use.
文摘为解决传统电平交叉模数转换器(LC ADC)精度较低和噪声整形逐次逼近寄存器(NS SAR)ADC功耗较大的问题,提出了一种应用于移动物联网(IoT)随机稀疏信号采集的LC-NS SAR ADC。在NS SAR ADC前端插入8 bit的LC ADC作为输入信号活跃度的预检测电路,在电平交叉发生后开启NS SAR ADC的转换。二阶无源噪声整形电路积分过程只在事件触发后发生,从而能够根据输入信号的活跃度动态调节整体功耗。在1.8 V 180 nm CMOS工艺、采样率为40 kS/s、过采样率(OSR)为20、带宽为1 kHz下对该ADC进行仿真验证,结果表明信噪失真比(SNDR)达到87 dB,电路功耗为2.70μW,心电图信号输入时功耗仅为0.79μW,相较于传统等间隔奈奎斯特采样ADC,采样点减少了73%,在处理生物医学信号时实现了约5∶1的数据压缩比,Schreier品质因数(FoMs)和Walden品质因数(FoMw)分别为172.6 dB和67.0 fJ/conv.step。
文摘Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compromises both system reliability and operational continuity.Existing hybrid IDS solutions often struggle to balance accurate classification of known attacks with reliable anomaly detection,particularly under the computational constraints of IoT environments.To address this gap,we introduce ZeroDefense,an adaptive fusion-based IDS designed for simultaneous detection of known intrusions and emerging zero-day threats.The framework employs a four-layer architecture consisting of i)feature standardization and class balancing,ii)anomaly detection using isolation forest,autoencoder,and local outlier factor,iii)fine-grained attack classification via random forest,extreme gradient boosting(XGBoost),light gradient boosting machine(LightGBM),and attentive interpretable tabular learning(TabNet),and iv)a confidence-aware fusion engine that adaptively selects the most reliable decision path.Suspicious or previously unseen traffic is isolated early through fused anomaly scoring,while benign and known-malicious flows are processed through supervised classification for precise attack labeling.With an anomaly cascaded decision pipeline,a dynamic confidence-driven fusion mechanism,and a deploymentconscious design,ZeroDefense enables real-time inference on IoT edge gateways.Evaluation on the CICIoT2023 benchmark demonstrates 99.94% overall accuracy and 95.64%macro-average F1-score for known attacks,while 5.76% of traffic is successfully flagged as potential zero-day activity,with inference latency maintained below 100 ms/flow.These results indicate that ZeroDefense offers a scalable,resilient,and practically deployable defense capability for modern IoT infrastructures.
文摘The modern world remains vulnerable to natural disasters,including floods,earthquakes,wildfires,and others.These events remain unpredictable and inevitable,and recovering quickly and effectively requires significant effort and expense.Monitoring is becoming more efficient thanks to technologies such as Unmanned Aerial Vehicles(UAVs),which can access hard-to-reach areas and provide real-time data.However,in disaster-affected areas,these monitoring systems may encounter many obstacles when communicating with servers or transmitting monitored data.This paper proposes an adaptive communication model to overcome the challenges faced in disaster-affected areas.A base station is responsible for collecting data(such as images and videos)captured by UAVs performing surveillance within its communication range.This station is typically a tower providing fixed cellular network service.However,in the absence of such a tower,a selected UAV may serve as the station,depending on the situation.If surveillance needs to be performed outside the coverage area,it can continue to communicate via nearby UAVs through cooperative communication.UAVs with internet support,known as the Internet of Flying Things(IoFT),will also be utilized to enhance communication capacity and efficiency.The proposed communication model is validated through experiments,showing superior data transmission performance and higher throughput.Analysis indicates it outperforms traditional systems,even in rural areas,with or without internet access.
文摘Location-Based Services(LBS)have greatly improved efficiency and functionality in various domains,but privacy and security concerns remain due to the centralized nature of many existing systems.To address these issues,this paper introduces the V-Track system,a decentralized architecture using blockchain technology for reliable vehicle location verification.By integrating GPS devices(Spark Fun GPS NEO-M9),IoT-enabled sensors,and a Cosmos blockchain-based ledger(network of interconnected blockchains),V-Track aims to solve centralized LBS problems.Through rigorous simulation experiments,this paper evaluates the performance and security of the V-Track system and demonstrates its potential to provide reliable location verification while preserving user privacy.This paper makes significant contributions by presenting V-Track as a decentralized solution to centralized LBS privacy and security problems,enhancing reliability and trustworthiness through blockchain integration,improving tracking mechanisms with GPS devices and IoT sensors for improved accuracy,and providing a privacy-preserving alternative to centralized LBS through its decentralized design and use of blockchain technology.These advancements hold promise for applications across multiple sectors,including logistics,supply chain management,urban planning,and emerging fields such as autonomous vehicles and augmented reality.
基金supported in part by JSPS Grants-in-Aid for Scientific Research 25K07742 and 25K23457.
文摘Spectrum sensing is an indispensable core part of cognitive radio dynamic spectrum access(DSA)and a key approach to alleviating spectrum scarcity in the Internet of Things(IoT).The key issue in practical IoT networks is robust sensing under the coexistence of low signal-to-noise ratios(SNRs)and non-Gaussian impulsive noise,where observations may be distorted differently across feature modalities,making conventional fusion unstable and degrading detection reliability.To address this challenge,the generalized Gaussian distribution(GGD)is adopted as the noise model,and a multimodal fusion framework termed BCAM-Net(bidirectional cross-attention multimodal network)is proposed.BCAM-Net adopts a parallel dual-branch architecture:a time-frequency branch that leverages the continuous wavelet transform(CWT)to extract time-frequency representations,and a temporal branch that learns long-range dependencies from raw signals.BCAM-Net utilizes a bidirectional cross-attention mechanism to achieve deep alignment and mutual calibration of temporal and time-frequency features,generating a fused representation that is highly robust to complex noise.Simulation results show that,under GGD noise with shape parameterβ=0.5,BCAM-Net achieves high detection probabilities in the low-SNR regime and outperforms representative baselines.At a false alarm probability Pf=0.1 and SNR of−14 dB,it attains a detection probability of 0.9020,exceeding the CNN-Transformer,WT-ResNet,TFCFN,and conventional CNN benchmarks by 5.75%,6.98%,33.3%,and 21.1%,respectively.These results indicate that BCAM-Net can effectively improve spectrum sensing performance in low-SNR impulsive-noise scenarios,and provides a lightweight,high-performance solution for practical cognitive radio spectrum sensing.
文摘建筑信息模型(Building Information Modeling,BIM)、地理信息系统(Geographic Information System,GIS)与物联网(Internet of Things,IoT)技术的发展,为构建面向智慧城市的一体化运维管理平台提供了技术基础。文章围绕智慧城市治理需求,提出BIM-GIS-IoT三维融合的运维管理平台架构,重点研究多源异构数据集成与模型融合、IoT实时监测、三维可视化展示、设施健康诊断与预测性运维等关键技术,以提升城市基础设施的运维效率、风险预警能力与决策科学性,为智慧城市建设提供新的技术路径与管理模式。
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-02152).
文摘The Internet of Things(IoT)and cloud computing have significantly contributed to the development of smart cities,enabling real-time monitoring,intelligent decision-making,and efficient resource management.These systems,particularly in IoT networks,rely on numerous interconnected devices that handle time-sensitive data for critical applications.In related approaches,trusted communication and reliable device interaction have been overlooked,thereby lowering security when sharing sensitive IoT data.Moreover,it incurs additional energy consumption and overhead while addressing potential threats in the dynamic environment.In this research,an Artificial Intelligence(AI)recommended fault-tolerant framework is proposed that leverages blockchain technology,aiming to enhance device trustworthiness and ensure data privacy.In addition,the intelligence of the proposed framework enables more authentic and authorized device involvement in data routing,thereby enabling seamless transmission in smart cities integrated with lightweight computing.To evaluate dynamic network conditions,the proposed framework offers a timely decision-making system to ensure robust delivery of IoT-assisted services.Using simulations,the efficacy of the proposed framework is validated by comparing it with existing approaches across various network metrics,demonstrating remarkable performance while achieving energy efficiency and optimizing network resources.
基金supported in part by theHubei Engineering Research Center for BDS-CloudHigh-Precision Deformation Monitoring Open Funding(No.HBBDGJ202507Y)the National Natural Science Foundation of China(No.62377037).
文摘The large-scale deployment of Internet of Things(IoT)technology across various aspects of daily life has significantly propelled the intelligent development of society.Among them,the integration of IoT and named data networks(NDNs)reduces network complexity and provides practical directions for content-oriented network design.However,ensuring data integrity in NDN-IoT applications remains a challenging issue.Very recently,Wang et al.(Entropy,27(5),471(2025))designed a certificateless aggregate signature(CLAS)scheme for NDN-IoT environments.Wang et al.stated that their construction was provably secure under various types of security attacks.Using theoretical analysis methods,in this work,we reveal that their CLAS design fails to meet unforgeability,a core security requirement for CLAS schemes.In particular,we demonstrate that their scheme is vulnerable to amalicious public-key replacement attack,enabling an adversary to produce authentic signatures for arbitrary fraudulent messages.Therefore,Wang et al.’s design cannot achieve its goal.To address the issue,we systematically examine the root causes behind the vulnerability and propose a security-enhanced CLAS construction for NDN-IoT environments.We prove the security ofour improveddesignunder the standard security assumptionandalsoanalyze its practicalperformanceby comparing the computational and communication costs with several related works.The comparison results show the practicality of our design.
文摘随着建筑行业的快速发展和智能技术的广泛应用,建筑机电安装工程中管道施工的质量管理问题日益受到重视。传统质量控制方式存在依赖人工、信息化水平低、反馈滞后等问题,难以满足智能建筑对施工精度、安全性和运行可靠性的高要求。文章提出一种基于信息化与智能技术的管道施工质量控制体系,结合物联网(Internet of Things,IoT)、BIM、智能传感与大数据分析手段,构建全过程质量管控框架,并在实际工程中进行应用验证。研究表明,该体系能够有效提升施工过程的可视化程度,减少质量缺陷,提高管道系统的整体施工水平和运行可靠性。
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.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Grant No.KFU253774].
文摘Malware poses a significant threat to the Internet of Things(IoT).It enables unauthorized access to devices in the IoT environment.The lack of unique architectural standards causes challenges in developing robust malware detection(MD)models.The existing models demand substantial computational resources.This study intends to build a lightweight MD model to detect anomalies in IoT networks.The authors develop a transformation technique,converting the malware binaries into images.MobileNet V2 is fine-tuned using improved grey wolf optimization(IGWO)to extract crucial features of malicious and benign samples.The ResNeXt model is combined with the Linformer’s attention mechanism to identify Malware features.A fully connected layer is integrated with gradientweighted class activation mapping(Grad-CAM)in order to facilitate an interpretable classification model.The proposed model is evaluated using the IoT malware and the IoT-23 datasets.The model performs well on the two datasets with an accuracy of 98.94%,precision of 98.46%,recall of 98.11%,and F1-score of 98.28%on the IoT malware dataset,and an accuracy of 98.23%,precision of 96.80%,recall of 96.64%,and F1-score of 96.71%on the IoT-23 dataset,respectively.The findings indicate that the model has a high standard of classification.The lightweight architecture enables efficient deployment with an inference time of 1.42 s.Inference time has no direct impact on accuracy,precision,recall,or F1-score.However,the inference speed would warrant timely detection in latency-sensitive IoT applications.By achieving a remarkable result,the proposed study offers a comprehensive solution:a scalable,interpretable,and computationally efficient MD model for the evolving IoT landscape.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2023-00235509Development of security monitoring technology based network behavior against encrypted cyber threats in ICT convergence environment).
文摘With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.
