基于BIM+AIoT的市政工程智慧工地平台开发与应用

在国家数字化转型战略的大背景下,市政工程专业众多,施工现场环境复杂,管理难度大,亟需通过数字化、智能化手段提升管理水平。通过BIM、AI、IoT技术的集成应用开发适应于市政工程特点的智慧工地管理平台,实现了人员实时定位、关键设备在线监控、基坑安全监测、隐患自动识别等功能,提升了现场各要素的管控能力。通过试点项目的应用验证了平台的应用效果,为其他同类项目的智慧工地建设提供了有益的经验。

基于AIoT的海水水产养殖环境智能监控系统的设计与开发

文章介绍了一种基于人工智能物联网(AIoT)技术的海水水产养殖环境智能监控系统的设计和开发。该系统旨在借助物联网信息化技术手段,提高海水养殖的生产效率和环境可持续性,实现实时监测养殖水环境中的溶解氧、温度、水位、叶绿素、pH、氨氮等参数。通过此实验表明,基于AIoT的智能监控系统可以有效提高养殖效益,减少资源浪费,保护生态环境,并实现对养殖环境的优化、高效、精细管理。

Securing the Internet of Health Things with Certificateless Anonymous Authentication Scheme

Internet of Health Things(IoHT)is a subset of Internet of Things(IoT)technology that includes interconnected medical devices and sensors used in medical and healthcare information systems.However,IoHT is susceptible to cybersecurity threats due to its reliance on low-power biomedical devices and the use of open wireless channels for communication.In this article,we intend to address this shortcoming,and as a result,we propose a new scheme called,the certificateless anonymous authentication(CAA)scheme.The proposed scheme is based on hyperelliptic curve cryptography(HECC),an enhanced variant of elliptic curve cryptography(ECC)that employs a smaller key size of 80 bits as compared to 160 bits.The proposed scheme is secure against various attacks in both formal and informal security analyses.The formal study makes use of the Real-or-Random(ROR)model.A thorough comparative study of the proposed scheme is conducted for the security and efficiency of the proposed scheme with the relevant existing schemes.The results demonstrate that the proposed scheme not only ensures high security for health-related data but also increases efficiency.The proposed scheme’s computation cost is 2.88 ms,and the communication cost is 1440 bits,which shows its better efficiency compared to its counterpart schemes.

农业生产智能化发展中的AIoT创新综合体项目构建策略研究

科技的飞速发展为农业生产带来了新的可能性。特别是信息技术革命,如人工智能(AI)和物联网(IoT)的出现与普及,为农业生产的智能化提供了新的技术手段和路径。AIoT(人工智能物联网)作为人工智能和物联网技术的融合,具有巨大的发展潜力,能够更加有效地实现农业生产的自动化、智能化和精准化管理,为农业生产提供更高效、更系统的解决方案。在这样的背景下,农业生产智能化发展中的AIoT创新综合体项目成为当前研究的热点之一。基于此,本研究探讨了农业生产智能化发展中的AIoT创新综合体项目的特点、构建难点,以及针对性的构建策略,以期为农业智能化发展提供新的思路和方向。

基于AIOT 技术的深基坑一体化作业安全视觉监测方法

针对深基坑一体化作业安全风险较高的问题,研究了基于AIOT技术的深基坑一体化作业安全视觉监测方法。利用双目视觉相机采集深基坑一体化作业监测区域图像,利用物联网节点将所采集图像传送至远程监测中心。远程监测中心接收图像后,利用卡尔曼滤波算法优化深基坑一体化作业监测图像。利用完成训练的YOLOv3算法,识别是否存在未佩戴安全帽以及误入危险区域的作业人员,存在未佩戴安全帽以及误入危险区域的作业人员时,利用物联网节点定位方法定位危险作业人员位置,实现深基坑一体化作业安全监测。实验结果表明,该方法有效监测深基坑一体化作业中,未佩戴安全帽以及进入危险区域的作业人员,提升深基坑一体化作业的管控水平。

基于AIoT平台的智能环境监测系统研究

当前,环境污染、气候变化对环境监测提出了更高要求。鉴于此,基于AIoT(人工智能物联网)平台,整合环境科学、物联网、数据分析与算法等多领域,开发智能环境监测系统,以提升环境监测质量、效率及精确度,共同解决环境监测面临的技术难题。通过调研和分析国内外学术论文、专利和成功案例,团队汲取、借鉴先进环境监测设计理念、技术方案、应用效果,用于项目的系统设计、传感器网络优化、数据处理和智能算法等方面,实现关键技术创新,增强竞争优势。最后,以人工智能与大数据产业学院为建设平台,通过学术研讨、行业展览等活动推广研究成果,挖掘应用潜力,实现社会效益最大化。

基于AIoT驱动的智慧物流全过程管理系统设计

针对现代物流管理中需要提高效率、信息公开等需求,提出了一种基于人工智能和物联网技术的智慧物流全过程管理系统。该系统以智能输送设备、自动化存储设备和智慧物流全过程管理算法为核心组成部分,通过实现自动化、智能化的物流运输、存储和管理,提升了物流的效率、准确性和可控性。

基于AIoT的高铁集约化混凝土生产管理系统研究与应用

混凝土在高速铁路建设过程中用量占比非常大,这对混凝土的质量也提出了更高要求。目前所有高速铁路拌和站都应用铁路工程管理平台实现拌和站生产过程的数据采集,监控实际混凝土配合比是否超标等预警功能,但还是存在拌和站的信息化建设分散、集成度不高、数据互联互通性低、数据溯源难、统计分析不够全面、协同管理程度低等问题。混凝土生产过程包含原材料管理、原材料试验、拌和机控制、运输等环节,每个环节都会对混凝土质量产生影响。对高铁集约化混凝土生产管理系统进行研究,以达到生产管理过程全程化、业务系统集成化、控制流程自动化、统计分析多元化的目标。

A Hybrid and Lightweight Device-to-Server Authentication Technique for the Internet of Things

The Internet of Things(IoT)is a smart networking infrastructure of physical devices,i.e.,things,that are embedded with sensors,actuators,software,and other technologies,to connect and share data with the respective server module.Although IoTs are cornerstones in different application domains,the device’s authenticity,i.e.,of server(s)and ordinary devices,is the most crucial issue and must be resolved on a priority basis.Therefore,various field-proven methodologies were presented to streamline the verification process of the communicating devices;however,location-aware authentication has not been reported as per our knowledge,which is a crucial metric,especially in scenarios where devices are mobile.This paper presents a lightweight and location-aware device-to-server authentication technique where the device’s membership with the nearest server is subjected to its location information along with other measures.Initially,Media Access Control(MAC)address and Advance Encryption Scheme(AES)along with a secret shared key,i.e.,λ_(i) of 128 bits,have been utilized by Trusted Authority(TA)to generate MaskIDs,which are used instead of the original ID,for every device,i.e.,server and member,and are shared in the offline phase.Secondly,TA shares a list of authentic devices,i.e.,server S_(j) and members C_(i),with every device in the IoT for the onward verification process,which is required to be executed before the initialization of the actual communication process.Additionally,every device should be located such that it lies within the coverage area of a server,and this location information is used in the authentication process.A thorough analytical analysis was carried out to check the susceptibility of the proposed and existing authentication approaches against well-known intruder attacks,i.e.,man-in-the-middle,masquerading,device,and server impersonations,etc.,especially in the IoT domain.Moreover,proposed authentication and existing state-of-the-art approaches have been simulated in the real environment of IoT to verify their performance,particularly in terms of various evaluation metrics,i.e.,processing,communication,and storage overheads.These results have verified the superiority of the proposed scheme against existing state-of-the-art approaches,preferably in terms of communication,storage,and processing costs.

Internet of Things Authentication Protocols: Comparative Study

Nowadays, devices are connected across all areas, from intelligent buildings and smart cities to Industry 4.0 andsmart healthcare. With the exponential growth of Internet of Things usage in our world, IoT security is still thebiggest challenge for its deployment. The main goal of IoT security is to ensure the accessibility of services providedby an IoT environment, protect privacy, and confidentiality, and guarantee the safety of IoT users, infrastructures,data, and devices. Authentication, as the first line of defense against security threats, becomes the priority ofeveryone. It can either grant or deny users access to resources according to their legitimacy. As a result, studyingand researching authentication issues within IoT is extremely important. As a result, studying and researchingauthentication issues within IoT is extremely important. This article presents a comparative study of recent researchin IoT security;it provides an analysis of recent authentication protocols from2019 to 2023 that cover several areaswithin IoT (such as smart cities, healthcare, and industry). This survey sought to provide an IoT security researchsummary, the biggest susceptibilities, and attacks, the appropriate technologies, and the most used simulators. Itillustrates that the resistance of protocols against attacks, and their computational and communication cost arelinked directly to the cryptography technique used to build it. Furthermore, it discusses the gaps in recent schemesand provides some future research directions.

Enhancing Internet of Things Intrusion Detection Using Artificial Intelligence

Escalating cyber security threats and the increased use of Internet of Things(IoT)devices require utilisation of the latest technologies available to supply adequate protection.The aim of Intrusion Detection Systems(IDS)is to prevent malicious attacks that corrupt operations and interrupt data flow,which might have significant impact on critical industries and infrastructure.This research examines existing IDS,based on Artificial Intelligence(AI)for IoT devices,methods,and techniques.The contribution of this study consists of identification of the most effective IDS systems in terms of accuracy,precision,recall and F1-score;this research also considers training time.Results demonstrate that Graph Neural Networks(GNN)have several benefits over other traditional AI frameworks through their ability to achieve in excess of 99%accuracy in a relatively short training time,while also capable of learning from network traffic the inherent characteristics of different cyber-attacks.These findings identify the GNN(a Deep Learning AI method)as the most efficient IDS system.The novelty of this research lies also in the linking between high yielding AI-based IDS algorithms and the AI-based learning approach for data privacy protection.This research recommends Federated Learning(FL)as the AI training model,which increases data privacy protection and reduces network data flow,resulting in a more secure and efficient IDS solution.

ResNeSt-biGRU: An Intrusion Detection Model Based on Internet of Things

The rapid expansion of Internet of Things (IoT) devices across various sectors is driven by steadily increasingdemands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device hascaught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. Thishas resulted in a myriad of security challenges, including information leakage, malware propagation, and financialloss, among others. Consequently, developing an intrusion detection system to identify both active and potentialintrusion traffic in IoT networks is of paramount importance. In this paper, we propose ResNeSt-biGRU, a practicalintrusion detection model that combines the strengths of ResNeSt, a variant of Residual Neural Network, andbidirectionalGated RecurrentUnitNetwork (biGRU).Our ResNeSt-biGRUframework diverges fromconventionalintrusion detection systems (IDS) by employing this dual-layeredmechanism that exploits the temporal continuityand spatial feature within network data streams, a methodological innovation that enhances detection accuracy.In conjunction with this, we introduce the PreIoT dataset, a compilation of prevalent IoT network behaviors, totrain and evaluate IDSmodels with a focus on identifying potential intrusion traffics. The effectiveness of proposedscheme is demonstrated through testing, wherein it achieved an average accuracy of 99.90% on theN-BaIoT datasetas well as on the PreIoT dataset and 94.45% on UNSW-NB15 dataset. The outcomes of this research reveal thepotential of ResNeSt-biGRU to bolster security measures, diminish intrusion-related vulnerabilities, and preservethe overall security of IoT ecosystems.

Interworking between Modbus and internet of things platform for industrial services

In the era of rapid development of Internet of Things(IoT),numerous machine-to-machine technologies have been applied to the industrial domain.Due to the divergence of IoT solutions,the industry is faced with a need to apply various technologies for automation and control.This fact leads to a demand for an establishing interworking mechanism which would allow smooth interoperability between heterogeneous devices.One of the major protocols widely used today in industrial electronic devices is Modbus.However,data generated by Modbus devices cannot be understood by IoT applications using different protocols,so it should be applied in a couple with an IoT service layer platform.oneM2M,a global IoT standard,can play the role of interconnecting various protocols,as it provides flexible tools suitable for building an interworking framework for industrial services.Therefore,in this paper,we propose an interworking architecture between devices working on the Modbus protocol and an IoT platform implemented based on oneM2M standards.In the proposed architecture,we introduce the way to model Modbus data as oneM2M resources,rules to map them to each other,procedures required to establish interoperable communication,and optimization methods for this architecture.We analyze our solution and provide an evaluation by implementing it based on a solar power management use case.The results demonstrate that our model is feasible and can be applied to real case scenarios.

Deep Learning-Based Secure Transmission Strategy with Sensor-Transmission-Computing Linkage for Power Internet of Things

The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power grid data. While wireless communication offers a convenient channel for grid terminal access and data transmission, it is important to note that the bandwidth of wireless communication is limited. Additionally, the broadcast nature of wireless transmission raises concerns about the potential for unauthorized eavesdropping during data transmission. To address these challenges and achieve reliable, secure, and real-time transmission of power grid data, an intelligent security transmission strategy with sensor-transmission-computing linkage is proposed in this paper. The primary objective of this strategy is to maximize the confidentiality capacity of the system. To tackle this, an optimization problem is formulated, taking into consideration interruption probability and interception probability as constraints. To efficiently solve this optimization problem, a low-complexity algorithm rooted in deep reinforcement learning is designed, which aims to derive a suboptimal solution for the problem at hand. Ultimately, through simulation results, the validity of the proposed strategy in guaranteed communication security, stability, and timeliness is substantiated. The results confirm that the proposed intelligent security transmission strategy significantly contributes to the safeguarding of communication integrity, system stability, and timely data delivery.

Behaviour recognition based on the integration of multigranular motion features in the Internet of Things

With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices,crowdsensing systems in the Internet of Things(IoT)are now conducting complicated video analysis tasks such as behaviour recognition.These applications have dramatically increased the diversity of IoT systems.Specifically,behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal dimension.Behaviour recognition may even rely more on the modeling of temporal information containing short-range and long-range motions,in contrast to computer vision tasks involving images that focus on understanding spatial information.However,current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in videos.In this paper,we propose a novel behaviour recognition method based on the integration of multigranular(IMG)motion features,which can provide support for deploying video analysis in multimedia IoT crowdsensing systems.In particular,we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module(CSEM)and a cascaded long-term motion feature integration module(CLIM).We evaluate our model on several action recognition benchmarks,such as HMDB51,Something-Something and UCF101.The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods,which confirms its effective-ness and efficiency.

Vector Dominance with Threshold Searchable Encryption (VDTSE) for the Internet of Things

The Internet of Medical Things(IoMT)is an application of the Internet of Things(IoT)in the medical field.It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems,which is essential in smart healthcare.However,Personal Health Records(PHRs)are normally kept in public cloud servers controlled by IoMT service providers,so privacy and security incidents may be frequent.Fortunately,Searchable Encryption(SE),which can be used to execute queries on encrypted data,can address the issue above.Nevertheless,most existing SE schemes cannot solve the vector dominance threshold problem.In response to this,we present a SE scheme called Vector Dominance with Threshold Searchable Encryption(VDTSE)in this study.We use a Lagrangian polynomial technique and convert the vector dominance threshold problem into a constraint that the number of two equal-length vectors’corresponding bits excluding wildcards is not less than a threshold t.Then,we solve the problem using the proposed technique modified in Hidden Vector Encryption(HVE).This technique makes the trapdoor size linear to the number of attributes and thus much smaller than that of other similar SE schemes.A rigorous experimental analysis of a specific application for privacy-preserving diabetes demonstrates the feasibility of the proposed VDTSE scheme.

Artificial intelligence in physiological characteristics recognition for internet of things authentication

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)systems.However,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral characteristics.Behavioral characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in practice.However,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate authentication.Thus,we review the literature on the use of AI in physiological characteristics recognition pub-lished after 2015.We use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their limitations.We also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

基于AIoT的智慧港口数字散货堆场系统设计

如何有效管理和追踪港口物流链是智慧港口建设急需解决的问题。以物联网、人工智能等为代表的数字技术为智慧港口建设数字散货堆场提供技术支撑。本文基于国内智慧港口形势,建立了一套基于智能物联网(AIoT)的数字散货堆场系统,将散货堆场中的物料与传感器连接,实现对物料进出场、移位堆放、存储信息、物流状态等进行实时监测和追踪的全程管理,为港口管理人员提供实时的物流状态和库存信息,提高港口的运输效率和服务质量,为打造世界级港口群注入新动力。

Power-Domain Collision-Tolerant Random Access Method with Auxiliary Beam for Satellite Internet of Things:A New Solution

There are numerous terminals in the satellite Internet of Things(IoT).To save cost and reduce power consumption,the system needs terminals to catch the characteristics of low power consumption and light control.The regular random access(RA)protocols may generate large amounts of collisions,which degrade the system throughout severally.The near-far effect and power control technologies are not applicable in capture effect to obtain power difference,resulting in the collisions that cannot be separated.In fact,the optimal design at the receiving end can also realize the condition of packet power domain separation,but there are few relevant researches.In this paper,an auxiliary beamforming scheme is proposed for power domain signal separation.It adds an auxiliary reception beam based on the conventional beam,utilizing the correlation of packets in time-frequency domain between the main and auxiliary beam to complete signal separation.The roll-off belt of auxiliary beam is used to create the carrier-to-noise ratio(CNR)difference.This paper uses the genetic algorithm to optimize the auxiliary beam direction.Simulation results show that the proposed scheme outperforms slotted ALOHA(SA)in terms of system throughput per-formance and without bringing terminals additional control burden.

The Machine Learning Ensemble for Analyzing Internet of Things Networks:Botnet Detection and Device Identification

The rapid proliferation of Internet of Things(IoT)technology has facilitated automation across various sectors.Nevertheless,this advancement has also resulted in a notable surge in cyberattacks,notably botnets.As a result,research on network analysis has become vital.Machine learning-based techniques for network analysis provide a more extensive and adaptable approach in comparison to traditional rule-based methods.In this paper,we propose a framework for analyzing communications between IoT devices using supervised learning and ensemble techniques and present experimental results that validate the efficacy of the proposed framework.The results indicate that using the proposed ensemble techniques improves accuracy by up to 1.7%compared to singlealgorithm approaches.These results also suggest that the proposed framework can flexibly adapt to general IoT network analysis scenarios.Unlike existing frameworks,which only exhibit high performance in specific situations,the proposed framework can serve as a fundamental approach for addressing a wide range of issues.