IOT管在大功率分米波电视发射机中的应用

本文详细介绍最近几年在我国开始投入使用的先进的大功率感应输出管（ＩＯＴ管） 在我台的分米波电视发射机中的使用情况；阐述了其结构、特点和基本工作原理。

BIM+GIS+IOT管理平台主要应用功能研究

在大数据发展环境下,BIM、GIS、IOT技术不断发展,给工程建设管理工作带来极大便利,并且得到较为广泛的应用。通过梳理管理平台的主要应用功能,并以国内研究文献为基础,介绍典型应用及其技术原理,以期为开展BIM+GIS+IOT平台相关研究提供参考。

面向NVM的IoT时序数据多态协作压缩策略

压缩策略是影响IoT时序数据存储系统性能的重要因素,而现有压缩策略缺乏针对NVM与IoT时序数据特性的优化机制。因此,提出了面向NVM的IoT时序数据多态协作压缩策略。首先,给出了IoT时序数据的组织结构。然后,针对IoT时序数据在一段时间内较稳定以及在用户态与内核态读写NVM适合的粒度差异较大的情况,设计了分层压缩策略。在用户态接收数据时,采用轻量级的数据压缩算法减少需存储的数据量,也减小了对IoT时序数据的存储效率的影响;针对IoT系统以查询和分析异常时序数据为主的特性,设计了深度压缩算法,在内核态对历史IoT时序数据进行深度压缩。其次,针对深度压缩历史IoT时序数据与存储新接收的IoT时序数据之间对NVM带宽的竞争,提出了写带宽保证的动态调整算法。最后,构建了面向NVM的IoT时序数据多态协作压缩策略原型PCCTSMS,并使用YCSB-TS工具进行测试与分析。实验结果表明,与InfluxDB、OpenTSDB、KairosDB和TVStore相比,PCCTSMS最高能提升161.3%的写吞吐率以及减少14.6%的存储空间。

IoT安全课程的“实战化”教材建设

针对当前网络空间安全教学内容正向多逆向少、理论多实践少、案例旧复现难的现状,以IoT安全课程教材建设为例,提出采用理论知识加实践指导、传统纸质加视频教程、实践操作加硬件在环的方式打造新型立体化教材,以覆盖IoT安全中的固件安全、硬件安全以及无线电安全等全链条知识,并阐述物联网安全“实战化”教材规划和建设内容。

元宇宙IoT场景化阅读行为研究

文章基于元宇宙IoT场景化阅读行为具有的互动感知、体验沉浸和生态社群特征,构建元宇宙IoT场景化阅读行为理论模型并对其进行检验,研究元宇宙IoT场景化阅读行为形成机理,分析其与普通网络数字阅读行为的区别,进而提出元宇宙IoT场景化阅读行为促进策略,即以空间有用感知为中心建立用户感知到认知的意愿引导策略,以互动体验沉浸为核心构建信息偶遇的互动触发策略,基于社群生态环境设计社区阅读领袖培养策略。

IOT2000在高职院校现代电气控制实训中的应用

基于物联网应用的智能网关能够兼容自动控制系统中的不同通信协议及数据源,高效进行数据采集并实现通信互连的特点,研究它在高职自动化专业教育实践中的应用具有非常重要的指导意义。以亚龙YL158GA1为基础构建基于西门子IOT2000的数据采集与传输系统,开展物联网智能网关在高职院校技能训练中的应用研究,并对IOT2000的控制功能进行了探索。最终对IOT2000的优势与不足进行了总结,并结合目前国内高校“双创”教育对IOT2000的应用前景进行了分析。

Enhancing IoT Data Security with Lightweight Blockchain and Okamoto Uchiyama Homomorphic Encryption

Blockchain technology has garnered significant attention from global organizations and researchers due to its potential as a solution for centralized system challenges.Concurrently,the Internet of Things(IoT)has revolutionized the Fourth Industrial Revolution by enabling interconnected devices to offer innovative services,ultimately enhancing human lives.This paper presents a new approach utilizing lightweight blockchain technology,effectively reducing the computational burden typically associated with conventional blockchain systems.By integrating this lightweight blockchain with IoT systems,substantial reductions in implementation time and computational complexity can be achieved.Moreover,the paper proposes the utilization of the Okamoto Uchiyama encryption algorithm,renowned for its homomorphic characteristics,to reinforce the privacy and security of IoT-generated data.The integration of homomorphic encryption and blockchain technology establishes a secure and decentralized platformfor storing and analyzing sensitive data of the supply chain data.This platformfacilitates the development of some business models and empowers decentralized applications to perform computations on encrypted data while maintaining data privacy.The results validate the robust security of the proposed system,comparable to standard blockchain implementations,leveraging the distinctive homomorphic attributes of the Okamoto Uchiyama algorithm and the lightweight blockchain paradigm.

An Intelligent SDN-IoT Enabled Intrusion Detection System for Healthcare Systems Using a Hybrid Deep Learning and Machine Learning Approach

The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during these situations.Also,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures.Hence,services in the healthcare ecosystem need rapid,uninterrupted,and secure facilities.The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas.This research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare ecosystem.We propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare data.In addition,this system can efficiently monitor connected devices and suspicious behaviours.Finally,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios.the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.

IoT感知的业务微流程建模

基于物联大数据赋能的业务流程能够更快更准地感知物理世界并及时做出响应的需求突现,提出一种物联网(Internet of Things,IoT)感知的业务微流程建模方法。首先,以单个IoT对象为中心建模,融合MAPE-K(monitor,analysis,plan,execution and knowledge base,MAPE-K)模型思想,将IoT对象实例生命周期的行为状态与微流程实例状态一一映射,实现对单个IoT对象的环形自动监控和调节;其次,基于从IoT传感设备获取的数据,定义基于SASE+语言的业务规则,提取对业务流程有意义的业务事件,避免了无关事件对宏流程的干扰;最后,通过设计一个微流程建模工具原型系统,结合真实案例分析,验证了提出建模方法的有效性,实现了业务流程与IoT实时流式感知数据的结合,并显著减少了宏流程需要处理的业务事件数量。

基于BIM+GIS+IoT技术的水利工程数字孪生工地建设管理系统研究与应用

文章旨在探究基于BIM、GIS和IoT技术的水利工程数字孪生工地建设管理系统的实现与应用。通过综合分析这些技术在水利工程中的实际应用,并结合系统的开发需求,设计了系统框架。工程相关数据经过加工处理后接入系统,结合数据底板和可视化引擎实现工程各要素的全面数字化映射。经由实际案例应用,验证了该系统能够提升水利工程建设管理的效率和质量。研究在推动水利工程领域的数字化发展方面发挥着积极的推动作用。

面向IoT安全的MITM攻击防御机制研究

隐蔽性强及攻击方式灵活的中间人(Man-in-the-Middle Attack,MITM)攻击是当前物联网(Internet of Things,IoT)智能家居安全面临的最大安全隐患之一.从攻击与防御两个角度提出了基于上下文感知融合的物联网设备配对模型和基于熵的异构物联设备指纹生成机制,以对抗型思维并行开展监测与防御方案研究,实现了对隐藏安全威胁的感知与过滤,通过相似度阈值分析测试与评估模型的建立完成了设备安全性验证,具有较好的效果与优势.

基于开源鸿蒙的IOT网络隧道智能化改造方案实施

本文对在贵州省余安高速公路香炉山隧道实施的基于开源鸿蒙的IOT网络隧道智能化改造解决方案进行了较为详尽的介绍。该方案在实施过程中,在高速公路机电系统的软硬件产品实现自主研发、自主可控、国产化改造方面进行了大胆有益的探索。

基于事件关联的IoT服务即时推荐方法研究

由于用户需求及环境的不确定性,服务组合业务流程可能需要在运行时进行动态调整,因此为用户推荐更加满足即时需求的IoT服务成为当前亟待解决的关键问题之一。就如何提高业务流程中IoT服务推荐的即时性与准确率问题,提出了一种考虑时间延时性的IoT服务即时推荐方法,基于改进的CM-SPADE算法挖掘出带有延时属性的事件频繁关联模式,为用户更准确地推荐符合时间约束的IoT服务,以更好地适应业务流程的变化。实验结果表明,所提出的考虑时间约束的IoT服务推荐方法在提高服务推荐准确度方面表现出较好的性能。

Encrypted Cyberattack Detection System over Encrypted IoT Traffic Based onStatistical Intelligence

In the early days of IoT’s introduction, it was challenging to introduce encryption communication due to the lackof performance of each component, such as computing resources like CPUs and batteries, to encrypt and decryptdata. Because IoT is applied and utilized in many important fields, a cyberattack on IoT can result in astronomicalfinancial and human casualties. For this reason, the application of encrypted communication to IoT has beenrequired, and the application of encrypted communication to IoT has become possible due to improvements inthe computing performance of IoT devices and the development of lightweight cryptography. The applicationof encrypted communication in IoT has made it possible to use encrypted communication channels to launchcyberattacks. The approach of extracting evidence of an attack based on the primary information of a networkpacket is no longer valid because critical information, such as the payload in a network packet, is encrypted byencrypted communication. For this reason, technology that can detect cyberattacks over encrypted network trafficoccurring in IoT environments is required. Therefore, this research proposes an encrypted cyberattack detectionsystem for the IoT (ECDS-IoT) that derives valid features for cyberattack detection from the cryptographic networktraffic generated in the IoT environment and performs cyberattack detection based on the derived features. ECDSIoT identifies identifiable information from encrypted traffic collected in IoT environments and extracts statisticsbased features through statistical analysis of identifiable information. ECDS-IoT understands information aboutnormal data by learning only statistical features extracted from normal data. ECDS-IoT detects cyberattacks basedonly on the normal data information it has trained. To evaluate the cyberattack detection performance of theproposed ECDS-IoT in this research, ECDS-IoT used CICIoT2023, a dataset containing encrypted traffic generatedby normal and seven categories of cyberattacks in the IoT environment and experimented with cyberattackdetection on encrypted traffic using Autoencoder, RNN, GRU, LSTM, BiLSTM, and AE-LSTM algorithms. Asa result of evaluating the performance of cyberattack detection for encrypted traffic, ECDS-IoT achieved highperformance such as accuracy 0.99739, precision 0.99154, recall 1.0, F1 score 0.99575, and ROC_AUC 0.99822when using the AE-LSTM algorithm. As shown by the cyberattack detection results of ECDS-IoT, it is possibleto detect most cyberattacks through encrypted traffic. By applying ECDS-IoT to IoT, it can effectively detectcyberattacks concealed in encrypted traffic, promoting the efficient operation of IoT and preventing financial andhuman damage caused by cyberattacks.

A Framework for Enhancing Privacy and Anonymity in Blockchain-Enabled IoT Devices

With the increase in IoT(Internet of Things)devices comes an inherent challenge of security.In the world today,privacy is the prime concern of every individual.Preserving one’s privacy and keeping anonymity throughout the system is a desired functionality that does not come without inevitable trade-offs like scalability and increased complexity and is always exceedingly difficult to manage.The challenge is keeping confidentiality and continuing to make the person innominate throughout the system.To address this,we present our proposed architecture where we manage IoT devices using blockchain technology.Our proposed architecture works on and off blockchain integrated with the closed-circuit television(CCTV)security camera fixed at the rental property.In this framework,the CCTV security camera feed is redirected towards the owner and renter based on the smart contract conditions.One entity(owner or renter)can see the CCTV security camera feed at one time.There is no third-party dependence except for the CCTV security camera deployment phase.Our contributions include the proposition of framework architecture,a novel smart contract algorithm,and the modification to the ring signatures leveraging an existing cryptographic technique.Analyses are made based on different systems’security and key management areas.In an empirical study,our proposed algorithm performed better in key generation,proof generation,and verification times.By comparing similar existing schemes,we have shown the proposed architectures’advantages.Until now,we have developed this system for a specific area in the real world.However,this system is scalable and applicable to other areas like healthcare monitoring systems,which is part of our future work.

Ultrafast Response and Threshold Adjustable Intelligent Thermoelectric Systems for Next‑Generation Self‑Powered Remote IoT Fire Warning

Fire warning is vital to human life,economy and ecology.However,the development of effective warning systems faces great challenges of fast response,adjustable threshold and remote detecting.Here,we propose an intelligent self-powered remote IoT fire warning system,by employing single-walled carbon nanotube/titanium carbide thermoelectric composite films.The flexible films,prepared by a convenient solution mixing,display p-type characteristic with excellent high-temperature stability,flame retardancy and TE(power factor of 239.7±15.8μW m^(-1) K^(-2))performances.The comprehensive morphology and structural analyses shed light on the underlying mechanisms.And the assembled TE devices(TEDs)exhibit fast fire warning with adjustable warning threshold voltages(1–10 mV).Excitingly,an ultrafast fire warning response time of~0.1 s at 1 mV threshold voltage is achieved,rivaling many state-of-the-art systems.Furthermore,TE fire warning systems reveal outstanding stability after 50 repeated cycles and desired durability even undergoing 180 days of air exposure.Finally,a TED-based wireless intelligent fire warning system has been developed by coupling an amplifier,analogto-digital converter and Bluetooth module.By combining TE characteristics,high-temperature stability and flame retardancy with wireless IoT signal transmission,TE-based hybrid system developed here is promising for next-generation self-powered remote IoT fire warning applications.

Intelligent Dynamic Heterogeneous Redundancy Architecture for IoT Systems

The conventional dynamic heterogeneous redundancy(DHR)architecture suffers from the security threats caused by the stability differences and similar vulnerabilities among the executors.To overcome these challenges,we propose an intelligent DHR architecture,which is more feasible by intelligently combining the random distribution based dynamic scheduling algorithm(RD-DS)and information weight and heterogeneity based arbitrament(IWHA)algorithm.In the proposed architecture,the random distribution function and information weight are employed to achieve the optimal selection of executors in the process of RD-DS,which avoids the case that some executors fail to be selected due to their stability difference in the conventional DHR architecture.Then,through introducing the heterogeneity to restrict the information weights in the procedure of the IWHA,the proposed architecture solves the common mode escape issue caused by the existence of multiple identical error output results of similar vulnerabilities.The experimental results characterize that the proposed architecture outperforms in heterogeneity,scheduling times,security,and stability over the conventional DHR architecture under the same conditions.

Cloud-Edge Collaborative Federated GAN Based Data Processing for IoT-Empowered Multi-Flow Integrated Energy Aggregation Dispatch

The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as convergence speed,inferior optimality searching capability,and the inability to learn from failed decision making feedbacks.Therefore,a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges.The proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power control,data migration,and computing resource allocation.It employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy consumption constraint violations.Simulation results indicate that the proposed algorithm effectively reduces data processing latency,energy consumption,and convergence time.

A Novel Graph Structure Learning Based Semi-Supervised Framework for Anomaly Identification in Fluctuating IoT Environment

With the rapid development of Internet of Things(IoT)technology,IoT systems have been widely applied in health-care,transportation,home,and other fields.However,with the continuous expansion of the scale and increasing complexity of IoT systems,the stability and security issues of IoT systems have become increasingly prominent.Thus,it is crucial to detect anomalies in the collected IoT time series from various sensors.Recently,deep learning models have been leveraged for IoT anomaly detection.However,owing to the challenges associated with data labeling,most IoT anomaly detection methods resort to unsupervised learning techniques.Nevertheless,the absence of accurate abnormal information in unsupervised learning methods limits their performance.To address these problems,we propose AS-GCN-MTM,an adaptive structural Graph Convolutional Networks(GCN)-based framework using a mean-teacher mechanism(AS-GCN-MTM)for anomaly identification.It performs better than unsupervised methods using only a small amount of labeled data.Mean Teachers is an effective semi-supervised learning method that utilizes unlabeled data for training to improve the generalization ability and performance of the model.However,the dependencies between data are often unknown in time series data.To solve this problem,we designed a graph structure adaptive learning layer based on neural networks,which can automatically learn the graph structure from time series data.It not only better captures the relationships between nodes but also enhances the model’s performance by augmenting key data.Experiments have demonstrated that our method improves the baseline model with the highest F1 value by 10.4%,36.1%,and 5.6%,respectively,on three real datasets with a 10%data labeling rate.

A Cloud-Fog Enabled and Privacy-Preserving IoT Data Market Platform Based on Blockchain

The dynamic landscape of the Internet of Things(IoT)is set to revolutionize the pace of interaction among entities,ushering in a proliferation of applications characterized by heightened quality and diversity.Among the pivotal applications within the realm of IoT,as a significant example,the Smart Grid(SG)evolves into intricate networks of energy deployment marked by data integration.This evolution concurrently entails data interchange with other IoT entities.However,there are also several challenges including data-sharing overheads and the intricate establishment of trusted centers in the IoT ecosystem.In this paper,we introduce a hierarchical secure data-sharing platform empowered by cloud-fog integration.Furthermore,we propose a novel non-interactive zero-knowledge proof-based group authentication and key agreement protocol that supports one-to-many sharing sets of IoT data,especially SG data.The security formal verification tool shows that the proposed scheme can achieve mutual authentication and secure data sharing while protecting the privacy of data providers.Compared with previous IoT data sharing schemes,the proposed scheme has advantages in both computational and transmission efficiency,and has more superiority with the increasing volume of shared data or increasing number of participants.