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Vector Dominance with Threshold Searchable Encryption (VDTSE) for the Internet of Things
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作者 Jingjing Nie Zhenhua Chen 《Computers, Materials & Continua》 SCIE EI 2024年第6期4763-4779,共17页
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 ... 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. 展开更多
关键词 Internet of things(iot) Internet of Medical things(IoMT) vector dominance with threshold searchable encryption(VDTSE) threshold comparison electronic healthcare
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Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges
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作者 Qingsong Zhao Lei Shu +3 位作者 Kailiang Li Mohamed Amine Ferrag Ximeng Liu Yanbin Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第1期58-73,共16页
Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the... Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues.These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT. 展开更多
关键词 CHALLENGES Internet of things(iot) privacy and security security requirements solar insecticidal lamps(SIL)
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A Few-Shot Learning-Based Automatic Modulation Classification Method for Internet of Things
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作者 Aer Sileng Qi Chenhao 《China Communications》 SCIE CSCD 2024年第8期18-29,共12页
Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve it... Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve its reliability.A data enhancement module(DEM)is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC.Multimodal network is designed to have multiple residual blocks,where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction.Moreover,a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM.Since different model may output different results,cooperative classifier is designed to avoid the randomness of single model and improve the reliability.Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods. 展开更多
关键词 automatic modulation classification(AMC) deep learning(DL) few-shot learning Internet of things(iot)
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Potential Benefits and Obstacles of the Use of Internet of Things in Saudi Universities: Empirical Study
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作者 Najmah Adel Fallatah Fahad Mahmoud Ghabban +4 位作者 Omair Ameerbakhsh Ibrahim Alfadli Wael Ghazy Alheadary Salem Sulaiman Alatawi Ashwaq Hasen Al-Shehri 《Advances in Internet of Things》 2024年第1期1-20,共20页
Internet of Things (IoT) among of all the technology revolutions has been considered the next evolution of the internet. IoT has become a far more popular area in the computing world. IoT combined a huge number of thi... Internet of Things (IoT) among of all the technology revolutions has been considered the next evolution of the internet. IoT has become a far more popular area in the computing world. IoT combined a huge number of things (devices) that can be connected through the internet. The purpose: this paper aims to explore the concept of the Internet of Things (IoT) generally and outline the main definitions of IoT. The paper also aims to examine and discuss the obstacles and potential benefits of IoT in Saudi universities. Methodology: the researchers reviewed the previous literature and focused on several databases to use the recent studies and research related to the IoT. Then, the researchers also used quantitative methodology to examine the factors affecting the obstacles and potential benefits of IoT. The data were collected by using a questionnaire distributed online among academic staff and a total of 150 participants completed the survey. Finding: the result of this study reveals there are twelve factors that affect the potential benefits of using IoT such as reducing human errors, increasing business income and worker’s productivity. It also shows the eighteen factors which affect obstacles the IoT use, for example sensors’ cost, data privacy, and data security. These factors have the most influence on using IoT in Saudi universities. 展开更多
关键词 Internet of things (iot) M2M Factors Obstacles Potential Benefits UNIVERSITIES
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一种基于NB-IoT的智能井盖监测系统设计 被引量:1
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作者 彭勇 陈俞强 +2 位作者 王石 郑俊杰 胡文德 《微型电脑应用》 2024年第3期26-28,36,共4页
针对城市井盖保有量大、安全事故频发、人工巡检困难、管理组织混乱等问题,提出一种基于窄带物联网(NB-IoT)技术的窨井盖自动监测系统。该系统以STM32作为主控模块,包含倾角、水位等传感器,实现对井盖状态以及井内重点数据的采集,配合... 针对城市井盖保有量大、安全事故频发、人工巡检困难、管理组织混乱等问题,提出一种基于窄带物联网(NB-IoT)技术的窨井盖自动监测系统。该系统以STM32作为主控模块,包含倾角、水位等传感器,实现对井盖状态以及井内重点数据的采集,配合云端服务器和客户端完成井盖数据远程可视化显示。结果表明,所提系统可以实现故障井盖自动报警、精确定位,降低人工巡检的难度,让城市井盖管理更加智能化,让故障检修更加便捷化,提高城市管理的智能化水平。 展开更多
关键词 物联网 低功耗 窄带物联网 STM32单片机
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面向物联网的NB-IoT信号优化方法研究
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作者 吴坤芳 贾怡婧 《通信电源技术》 2024年第11期194-196,共3页
随着物联网技术的飞速发展,窄带物联网(Narrow Band Internet of Things,NB-IoT)作为一种低功耗、广覆盖、大连接的无线通信技术,逐渐成为连接物理世界与数字世界的桥梁。然而,在实际应用中,NB-IoT信号面临着诸如信号衰减、干扰、覆盖... 随着物联网技术的飞速发展,窄带物联网(Narrow Band Internet of Things,NB-IoT)作为一种低功耗、广覆盖、大连接的无线通信技术,逐渐成为连接物理世界与数字世界的桥梁。然而,在实际应用中,NB-IoT信号面临着诸如信号衰减、干扰、覆盖不均等挑战。这些挑战不仅影响用户体验,还限制了物联网应用的进一步发展。因此,研究面向物联网的NB-IoT信号优化方法具有重要意义。文章深入研究面向物联网的NB-IoT信号优化方法,提出多种有效的优化策略和技术手段。 展开更多
关键词 物联网 窄带物联网(NB-iot) 信号优化
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Internet of robotic things for mobile robots:Concepts,technologies,challenges,applications,and future directions 被引量:1
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作者 Homayun Kabir Mau-Luen Tham Yoong Choon Chang 《Digital Communications and Networks》 SCIE CSCD 2023年第6期1265-1290,共26页
Nowadays,Multi Robotic System(MRS)consisting of different robot shapes,sizes and capabilities has received significant attention from researchers and are being deployed in a variety of real-world applications.From sen... Nowadays,Multi Robotic System(MRS)consisting of different robot shapes,sizes and capabilities has received significant attention from researchers and are being deployed in a variety of real-world applications.From sensors and actuators improved by communication technologies to powerful computing systems utilizing advanced Artificial Intelligence(AI)algorithms have rapidly driven the development of MRS,so the Internet of Things(IoT)in MRS has become a new topic,namely the Internet of Robotic Things(IoRT).This paper summarizes a comprehensive survey of state-of-the-art technologies for mobile robots,including general architecture,benefits,challenges,practical applications,and future research directions.In addition,remarkable research of i)multirobot navigation,ii)network architecture,routing protocols and communications,and iii)coordination among robots as well as data analysis via external computing(cloud,fog,edge,edge-cloud)are merged with the IoRT architecture according to their applicability.Moreover,security is a long-term challenge for IoRT because of various attack vectors,security flaws,and vulnerabilities.Security threats,attacks,and existing solutions based on IoRT architectures are also under scrutiny.Moreover,the identification of environmental situations that are crucial for all types of IoRT applications,such as the detection of objects,human,and obstacles,is also critically reviewed.Finally,future research directions are given by analyzing the challenges of IoRT in mobile robots. 展开更多
关键词 Multi Robotic System(MRS) Internet of things(iot) Internet of Robotic things(IoRT) Cloud computing Artificial intelligence(AI) Machine learning(ML) Reinforcement learning(RL)
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Anomaly Detection for Industrial Internet of Things Cyberattacks 被引量:1
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作者 Rehab Alanazi Ahamed Aljuhani 《Computer Systems Science & Engineering》 SCIE EI 2023年第3期2361-2378,共18页
The evolution of the Internet of Things(IoT)has empowered modern industries with the capability to implement large-scale IoT ecosystems,such as the Industrial Internet of Things(IIoT).The IIoT is vulnerable to a diver... The evolution of the Internet of Things(IoT)has empowered modern industries with the capability to implement large-scale IoT ecosystems,such as the Industrial Internet of Things(IIoT).The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational andfinancial harm to organizations.To preserve the confidentiality,integrity,and availability of IIoT networks,an anomaly-based intrusion detection system(IDS)can be used to provide secure,reliable,and efficient IIoT ecosystems.In this paper,we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks.The proposed anomaly-based IDS is divided into three phases:pre-processing,feature selection,and classification.In the pre-processing phase,data cleaning and nor-malization are performed.In the feature selection phase,the candidates’feature vectors are computed using two feature reduction techniques,minimum redun-dancy maximum relevance and neighborhood components analysis.For thefinal step,the modeling phase,the following classifiers are used to perform the classi-fication:support vector machine,decision tree,k-nearest neighbors,and linear discriminant analysis.The proposed work uses a new data-driven IIoT data set called X-IIoTID.The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%,a sensitivity rate of 99.59%,a specificity rate of 99.58%,and a low false positive rate of 0.4%. 展开更多
关键词 Anomaly detection anomaly-based IDS Industrial Internet of things(Iiot) iot industrial control systems(ICSs) X-IiotID
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Internet of Things (IoT): A Literature Review 被引量:18
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作者 Somayya Madakam R. Ramaswamy Siddharth Tripathi 《Journal of Computer and Communications》 2015年第5期164-173,共10页
One of the buzzwords in the Information Technology is Internet of Things (IoT). The future is Internet of Things, which will transform the real world objects into intelligent virtual objects. The IoT aims to unify eve... One of the buzzwords in the Information Technology is Internet of Things (IoT). The future is Internet of Things, which will transform the real world objects into intelligent virtual objects. The IoT aims to unify everything in our world under a common infrastructure, giving us not only control of things around us, but also keeping us informed of the state of the things. In Light of this, present study addresses IoT concepts through systematic review of scholarly research papers, corporate white papers, professional discussions with experts and online databases. Moreover this research article focuses on definitions, geneses, basic requirements, characteristics and aliases of Internet of Things. The main objective of this paper is to provide an overview of Internet of Things, architectures, and vital technologies and their usages in our daily life. However, this manuscript will give good comprehension for the new researchers, who want to do research in this field of Internet of Things (Technological GOD) and facilitate knowledge accumulation in efficiently. 展开更多
关键词 Internet of things iot RFID IPv6 EPC BARCODE Wi-Fi BLUETOOTH NFC ZigBee Sensors Actuators
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Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology 被引量:1
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作者 Nazik Alturki Raed Alharthi +5 位作者 Muhammad Umer Oumaima Saidani Amal Alshardan Reemah M.Alhebshi Shtwai Alsubai Ali Kashif Bashir 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第6期3387-3415,共29页
The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the d... The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device itself.Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features.This paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in homes.We have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT devices.Our system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing devices.We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server.The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings.It is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation systems.Additionally,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber threats.The trial results support the proposed system and demonstrate its potential for use in everyday life. 展开更多
关键词 Blockchain Internet of things(iot) smart home automation CYBERSECURITY
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Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications
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作者 Abdulaziz Aldribi Aman Singh Jose Brensa 《Computer Systems Science & Engineering》 SCIE EI 2023年第9期3865-3881,共17页
Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications.Internet of Things(IoT),fog computing,edge computing,cloud computing,and the edge ... Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications.Internet of Things(IoT),fog computing,edge computing,cloud computing,and the edge of things are the spine of all real-time and scalable applications.Conspicuously,this study proposed a novel framework for a real-time and scalable application that changes dynamically with time.In this study,IoT deployment is recommended for data acquisition.The Pre-Processing of data with local edge and fog nodes is implemented in this study.The thresholdoriented data classification method is deployed to improve the intrusion detection mechanism’s performance.The employment of machine learningempowered intelligent algorithms in a distributed manner is implemented to enhance the overall response rate of the layered framework.The placement of respondent nodes near the framework’s IoT layer minimizes the network’s latency.For economic evaluation of the proposed framework with minimal efforts,EdgeCloudSim and FogNetSim++simulation environments are deployed in this study.The experimental results confirm the robustness of the proposed system by its improvised threshold-oriented data classification and intrusion detection approach,improved response rate,and prediction mechanism.Moreover,the proposed layered framework provides a robust solution for real-time and scalable applications that changes dynamically with time. 展开更多
关键词 Internet of things(iot) Edge of things(EoT) fog computing cloud computing SCALABLE DECENTRALIZED
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Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm
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作者 Ayman Khallel Al-Ani Shams Ul Arfeen Laghari +2 位作者 Hariprasath Manoharan Shitharth Selvarajan Mueen Uddin 《Computers, Materials & Continua》 SCIE EI 2023年第8期2261-2279,共19页
In this paper,the application of transportation systems in realtime traffic conditions is evaluated with data handling representations.The proposed method is designed in such a way as to detect the number of loads tha... In this paper,the application of transportation systems in realtime traffic conditions is evaluated with data handling representations.The proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the system.Compared to the existing approach,the design model in the proposed method is made by dividing the computing areas into several cluster regions,thereby reducing the complex monitoring system where control errors are minimized.Furthermore,a route management technique is combined with Artificial Intelligence(AI)algorithm to transmit the data to appropriate central servers.Therefore,the combined objective case studies are examined as minimization and maximization criteria,thus increasing the efficiency of the proposed method.Finally,four scenarios are chosen to investigate the projected design’s effectiveness.In all simulated metrics,the proposed approach provides better operational outcomes for an average percentage of 97,thereby reducing the amount of traffic in real-time conditions. 展开更多
关键词 TRANSPORTATION Artificial Intelligence(AI) DATA-DRIVEN Internet of things(iot)
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Network Learning-Enabled Sensor Association for Massive Internet of Things
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作者 Alaa Omran Almagrabi Rashid Ali +2 位作者 Daniyal Alghazzawi Bander A.Alzahrani Fahad M.Alotaibi 《Computer Systems Science & Engineering》 SCIE EI 2023年第10期843-853,共11页
The massive Internet of Things(IoT)comprises different gateways(GW)covering a given region of a massive number of connected devices with sensors.In IoT networks,transmission interference is observed when different sen... The massive Internet of Things(IoT)comprises different gateways(GW)covering a given region of a massive number of connected devices with sensors.In IoT networks,transmission interference is observed when different sensor devices(SD)try to send information to a single GW.This is mitigated by allotting various channels to adjoining GWs.Furthermore,SDs are permitted to associate with anyGWin a network,naturally choosing the one with a higher received signal strength indicator(RSSI),regardless of whether it is the ideal choice for network execution.Finding an appropriate GW to optimize the performance of IoT systems is a difficult task given the complicated conditions among GWs and SDs.Recently,in remote IoT networks,the utilization of machine learning(ML)strategies has arisen as a viable answer to determine the effect of various models in the system,and reinforcement learning(RL)is one of these ML techniques.Therefore,this paper proposes the use of an RL algorithm for GW determination and association in IoT networks.For this purpose,this study allows GWs and SDs with intelligence,through executing the multi-armed bandit(MAB)calculation,to investigate and determine the optimal GW with which to associate.In this paper,rigorous mathematical calculations are performed for this purpose and evaluate our proposed mechanism over randomly generated situations,which include different IoT network topologies.The evaluation results indicate that our intelligentMAB-based mechanism enhances the association as compared to state-of-the-art(RSSI-based)and related research approaches. 展开更多
关键词 Reinforcement learning ASSOCIATION internet of things massive iot sensors network
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Implementation of Machine-to-Machine Solutions Using MQTT Protocol in Internet of Things (IoT) Environment to Improve Automation Process for Electrical Distribution Substations in Colombia 被引量:2
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作者 Hermes Eslava Luis Alejandro Rojas Ramón Pereira 《Journal of Power and Energy Engineering》 2015年第4期92-96,共5页
In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost e... In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of MQTT protocol with an intensive use of Internet of things (IoT) environment which guarantees the following properties within the automation process: Advanced reports and statistics, remote command execution on one or more units (groups of units), detailed monitoring of remote units and custom alarm mechanism and firmware upgrade on one or more units (groups of units). This kind of implementation is the first one in Colombia and it is able to automatically recover from an N-1 fault. 展开更多
关键词 MACHINE to MACHINE Quality of Service Distribution Grids MQTT PROTOCOL Internet of things (iot) ENVIRONMENT ELECTRICAL Energy
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Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques
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作者 Okba Taouali Sawcen Bacha +4 位作者 Khaoula Ben Abdellafou Ahamed Aljuhani Kamel Zidi Rehab Alanazi Mohamed Faouzi Harkat 《Computer Systems Science & Engineering》 SCIE EI 2023年第11期1593-1609,共17页
Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’information and provide a proper diagnosis as needed,resulting in the Internet of Medical Things(IoMT).However,obtaining ... Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’information and provide a proper diagnosis as needed,resulting in the Internet of Medical Things(IoMT).However,obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge.However,due to the computational resources being limited,an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms.Therefore,designing and developing a lightweight detection mechanism is crucial.To address the aforementioned challenges,a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT networks.The proposed anomaly-based IDS is divided into three steps:pre-processing,feature selection,and decision.In the pre-processing phase,data cleaning and normalization are performed.In the feature selection step,the proposed approach uses two data-driven kernel techniques:kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection results.Therefore,in decision step,in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is used.To check the efficiency of the developed detection scheme,a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved results.The proposed method achieved 99.9%accuracy,99.8%specificity,100%Sensitivity,99.9 F-score. 展开更多
关键词 Machine learning data-driven technique KPCA KPLS intrusion detection iot Internet of Medical things(IoMT)
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基于双模式端址跳变SD-IoT主动防御方法
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作者 张兵 李辉 王欢 《吉林大学学报(信息科学版)》 CAS 2024年第3期421-429,共9页
由于地址跳变是物联网主动防御的一种有效手段,但因跳变资源匮乏、可预见性以及数据包混淆度低已经成为制约物联网地址跳变的主要问题。为此,提出一种基于双模式端址跳变的主动防御方法。该方法设计了双模式端址选择算法,通过动态确定... 由于地址跳变是物联网主动防御的一种有效手段,但因跳变资源匮乏、可预见性以及数据包混淆度低已经成为制约物联网地址跳变的主要问题。为此,提出一种基于双模式端址跳变的主动防御方法。该方法设计了双模式端址选择算法,通过动态确定虚拟端址生成策略,以通信时间为阈值,扩大端址跳变空间,从而解决地址池资源受限问题。同时,还构建了双虚拟端址跳变方法,通过动态分配和同步虚拟接收和发送地址,提升数据包混淆度,增强跳变的不可预见性。并且基于SDN(Software Defined Network)设计了流表双向同步机制,实现流表的动态下发和同步,以保证端址跳变的一致性。实验结果表明,该方法能有效提升地址跳变的多样性和不可预测性,显著增强抵御嗅探攻击的能力。 展开更多
关键词 物联网安全 主动防御 地址跳变 软件定义物联网
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Joint optimization of serving node selection and wireless resources allocation for transactions data in mobile blockchain enhanced Internet of Things
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作者 尹玉峰 WU Wenjun +3 位作者 GAO Yang JIN Kaiqi ZHANG Yanhua SUN Teng 《High Technology Letters》 EI CAS 2023年第2期181-193,共13页
With the increased emphasis on data security in the Internet of Things(IoT), blockchain has received more and more attention.Due to the computing consuming characteristics of blockchain, mobile edge computing(MEC) is ... With the increased emphasis on data security in the Internet of Things(IoT), blockchain has received more and more attention.Due to the computing consuming characteristics of blockchain, mobile edge computing(MEC) is integrated into IoT.However, how to efficiently use edge computing resources to process the computing tasks of blockchain from IoT devices has not been fully studied.In this paper, the MEC and blockchain-enhanced IoT is considered.The transactions recording the data or other application information are generated by the IoT devices, and they are offloaded to the MEC servers to join the blockchain.The practical Byzantine fault tolerance(PBFT) consensus mechanism is used among all the MEC servers which are also the blockchain nodes, and the latency of the consensus process is modeled with the consideration of characteristics of the wireless network.The joint optimization problem of serving base station(BS) selection and wireless transmission resources allocation is modeled as a Markov decision process(MDP), and the long-term system utility is defined based on task reward, credit value, the latency of infrastructure layer and blockchain layer, and computing cost.A double deep Q learning(DQN) based transactions offloading algorithm(DDQN-TOA) is proposed, and simulation results show the advantages of the proposed algorithm in comparison to other methods. 展开更多
关键词 Internet of things(iot) mobile edge computing(MEC) blockchain deep reinforcement learning(DRL)
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The Impact of Internet of Things in Manufacturing Management
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作者 Ukazu Noel Chinedu Eje Brendan 《Engineering(科研)》 2023年第9期533-560,共28页
The study investigated the impact of the Internet of Things in manufacturing management. Specifically, the study examined how IoT implementation and management affect organizational efficiency in Camanov Ltd.;and to w... The study investigated the impact of the Internet of Things in manufacturing management. Specifically, the study examined how IoT implementation and management affect organizational efficiency in Camanov Ltd.;and to what extent IoT implementation contributes to the saving of cost and time of the organization. The research design is a survey. The population of this study consisted of all 141 staff of Camanov Ltd. Port Harcourt. Since the population is not large, the researcher conducted a census of all, and 126 staff completed a structured questionnaire. The two research questions were analyzed using simple percentages and all two hypotheses were tested using sample proportion statistics (Z test) at a 0.05 level of significance. The result showed that the Internet of Things has a significant impact on organizational efficiency in Camanov Ltd. (Z = 4.73);and that the Internet of Things significantly contributes toward saving cost and time of the organization Camanov Ltd (Z = 4.95). It was recommended that organizations should encourage training of personnel in the improved limitless possibility of information gathered from the Internet of Things framework which supports planning, budgeting and monitoring approaches, providing more reliable information to support actions, in particular in the decision-making process, to enhance productivity. 展开更多
关键词 iot Internet of things AUTOMATION Network of Physical Objects
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Traffic-Aware Fuzzy Classification Model to Perform IoT Data Traffic Sourcing with the Edge Computing
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作者 Huixiang Xu 《Computers, Materials & Continua》 SCIE EI 2024年第2期2309-2335,共27页
The Internet of Things(IoT)has revolutionized how we interact with and gather data from our surrounding environment.IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to... The Internet of Things(IoT)has revolutionized how we interact with and gather data from our surrounding environment.IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable insights.The rapid proliferation of Internet of Things(IoT)devices has ushered in an era of unprecedented data generation and connectivity.These IoT devices,equipped with many sensors and actuators,continuously produce vast volumes of data.However,the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant challenges.However,transmitting all this data to a centralized cloud infrastructure for processing and analysis can be inefficient and impractical due to bandwidth limitations,network latency,and scalability issues.This paper proposed a Self-Learning Internet Traffic Fuzzy Classifier(SLItFC)for traffic data analysis.The proposed techniques effectively utilize clustering and classification procedures to improve classification accuracy in analyzing network traffic data.SLItFC addresses the intricate task of efficiently managing and analyzing IoT data traffic at the edge.It employs a sophisticated combination of fuzzy clustering and self-learning techniques,allowing it to adapt and improve its classification accuracy over time.This adaptability is a crucial feature,given the dynamic nature of IoT environments where data patterns and traffic characteristics can evolve rapidly.With the implementation of the fuzzy classifier,the accuracy of the clustering process is improvised with the reduction of the computational time.SLItFC can reduce computational time while maintaining high classification accuracy.This efficiency is paramount in edge computing,where resource constraints demand streamlined data processing.Additionally,SLItFC’s performance advantages make it a compelling choice for organizations seeking to harness the potential of IoT data for real-time insights and decision-making.With the Self-Learning process,the SLItFC model monitors the network traffic data acquired from the IoT Devices.The Sugeno fuzzy model is implemented within the edge computing environment for improved classification accuracy.Simulation analysis stated that the proposed SLItFC achieves 94.5%classification accuracy with reduced classification time. 展开更多
关键词 Internet of things(iot) edge computing traffic data SELF-LEARNING fuzzy-learning
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Delta Compression Correction Method for Covert Communication in IoT
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作者 Hu Zhijuan Liu Shuangyu +2 位作者 Xu Fei Liu Liqiang Li Guiping 《China Communications》 SCIE CSCD 2024年第9期60-74,共15页
Covert communication can conceal the existence of wireless transmission and thus has the ability to address information security transfer issue in many applications of the booming Internet of Things(IoT).However,the p... Covert communication can conceal the existence of wireless transmission and thus has the ability to address information security transfer issue in many applications of the booming Internet of Things(IoT).However,the proliferation of sensing devices has generated massive amounts of data,which has increased the burden of covert communication.Considering the spatiotemporal correlation of data collection causing redundancy between data,eliminating duplicate data before transmission is beneficial for shortening transmission time,reducing the average received signal power of warden,and ultimately realizing covert communication.In this paper,we propose to apply delta compression technology in the gateway to reduce the amount of data generated by IoT devices,and then sent it to the cloud server.To this end,a cost model and evaluation method that is closer to the actual storage mode of computer systems is been constructed.Based on which,the delta version sequence obtained by existing delta compression algorithms is no longer compact,manifested by the still high cost.In this situation,we designed the correction scheme based on instructions merging(CSIM)correction to save costs by merging instructions.Firstly,the delta version sequence is divided into five categories and corresponding merge rules were derived.Then,for any COPY/ADD class delta compression algorithm,merge according to strict to relaxed to selection rules while generating instructions.Finally,a more cost-effective delta version sequence can be gained.The experimental results on random data show that the delta version sequences output by the CSIM corrected 1.5-pass and greedy algorithms have better performance in cost reducing. 展开更多
关键词 correction method covert communica-tion delta compression Internet of things(iot)
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