Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace.Network intrusion detection is usually regarded as an efficient means of dealing with security attacks.Many ways have been...Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace.Network intrusion detection is usually regarded as an efficient means of dealing with security attacks.Many ways have been presented,utilizing various strategies and focusing on different types of visitors.Anomaly-based network intrusion monitoring is an essential area of intrusion detection investigation and development.Despite extensive research on anomaly-based network detection,there is still a lack of comprehensive literature reviews covering current methodologies and datasets.Despite the substantial research into anomaly-based network intrusion detection algorithms,there is a dearth of a research evaluation of new methodologies and datasets.We explore and evaluate 50 highest publications on anomaly-based intrusion detection using an in-depth review of related literature techniques.Our work thoroughly explores the technological environment of the subject in order to help future research in this sector.Our examination is carried out from the relevant angles:application areas,data preprocessing and threat detection approaches,assessment measures,and datasets.We select unresolved research difficulties and underexplored research areas from every viewpoint recommendation of the study.Finally,we outline five potentially increased research areas for the future.展开更多
Traditional fire safety management in the electric power industry has significant drawbacks,including a lack of data,difficulty of maintenance,lack of supervision,and lack of interaction.This type of management lags b...Traditional fire safety management in the electric power industry has significant drawbacks,including a lack of data,difficulty of maintenance,lack of supervision,and lack of interaction.This type of management lags behind current advanced safety management concepts such as“gate advancement”and“full process man-agement”,and it fails to meet the needs of future energy internet construction and development.In response to these problems,an internet of things system for smart firefighting in the electric power industry was constructed in this study.This system defines a centralized information window,trains a power intelligent firefighting brain,establishes a firefighting cloud management and control system,constructs a power firefighting interaction mech-anism,and performs multi-party coordination of firefighting mechanisms to realize concept of“a whole network on one screen and everything in one network”for managing fires.展开更多
The healthcare internet of things(IoT)system has dramatically reshaped this important industry sector.This system employs the latest technology of IoT and wireless medical sensor networks to support the reliable conne...The healthcare internet of things(IoT)system has dramatically reshaped this important industry sector.This system employs the latest technology of IoT and wireless medical sensor networks to support the reliable connection of patients and healthcare providers.The goal is the remote monitoring of a patient’s physiological data by physicians.Moreover,this system can reduce the number and expenses of healthcare centers,make up for the shortage of healthcare centers in remote areas,enable consultation with expert physicians around the world,and increase the health awareness of communities.The major challenges that affect the rapid deployment and widespread acceptance of such a system are the weaknesses in the authentication process,which should maintain the privacy of patients,and the integrity of remote medical instructions.Current research results indicate the need of a flexible authentication scheme.This study proposes a scheme with enhanced security for healthcare IoT systems,called an end-to-end authentication scheme for healthcare IoT systems,that is,an E2EA.The proposed scheme supports security services such as a strong and flexible authentication process,simultaneous anonymity of the patient and physician,and perfect forward secrecy services.A security analysis based on formal and informal methods demonstrates that the proposed scheme can resist numerous security-related attacks.A comparison with related authentication schemes shows that the proposed scheme is efficient in terms of communication,computation,and storage,and therefore cannot only offer attractive security services but can reasonably be applied to healthcare IoT systems.展开更多
As the agricultural internet of things(IoT)technology has evolved,smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments.In this paper,we propose the concep...As the agricultural internet of things(IoT)technology has evolved,smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments.In this paper,we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots,which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters.First,the speeded-up robust feature(SURF)extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular parallel vision system.Then the confidence density image is constructed by integrating the enhanced elevation image and the corresponding binarized crop row image,where the edge contour and the height information of crop row are fused to extract the navigation parameters(θ,d)based on the model of a smart agricultural robot.Finally,the five navigation network instruction sets are designed based on the navigation angleθand the lateral distance d,which represent the basic movements for a certain type of smart agricultural robot working in a field.Simulated experimental results in the laboratory show that the algorithm proposed in this study is effective with small turning errors and low standard deviations,and can provide a valuable reference for the further practical application of binocular vision navigation systems in smart agricultural robots in the agricultural IoT system.展开更多
Cloud computing has been exploited in managing large-scale IoT systems.IoT cloud servers usually handle a large number of requests from various IoT devices.Due to the fluctuant and heavy workload,the servers require t...Cloud computing has been exploited in managing large-scale IoT systems.IoT cloud servers usually handle a large number of requests from various IoT devices.Due to the fluctuant and heavy workload,the servers require the cloud to provide high scalability,stable performance,low price and necessary functionalities.However,traditional clouds usually offer computing service with the abstraction of virtual machine(VM),which can hardly meet these requirements.Meanwhile,different cloud vendors provide different performance stabilities and price models,which fluctuate according to the dynamic workload.A single cloud cannot satisfy all the requirements of the IoT scenario well.The JointCloud computing model empowers the cooperation among multiple public clouds.However,it is still difficult to dynamically schedule the workload on different clouds based on the VM abstraction.This paper introduces HCloud,a trusted JointCloud platform for IoT systems using serverless computing model.HCloud allows an IoT server to be implemented with multiple serverless functions and schedules these functions on different clouds based on a schedule policy.The policy is specified by the client and includes the required functionalities,execution resources,latency,price and so on.HCloud collects the status of each cloud and dispatches serverless functions to the most suitable cloud based on the schedule policy.By leveraging the blockchain technology,we further enforce that our system can neither fake the cloud status nor wrongly dispatch the target functions.We have implemented a prototype of HCloud and evaluated it by simulating multiple cloud providers.The evaluation results show that HCloud can greatly improve the performance of serverless workloads with negligible costs.展开更多
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 ...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.展开更多
Design and implementation of Internet of Things (IoT) systems require platforms with smart things and components. Two dominant architectural approaches for developing IoT systems are mashup-based and model-based appro...Design and implementation of Internet of Things (IoT) systems require platforms with smart things and components. Two dominant architectural approaches for developing IoT systems are mashup-based and model-based approaches. Mashup approaches use existing services and are mainly suitable for less critical, personalized applications. Web development tools are widely used in mashup approaches. Model-based techniques describe a system on a higher level of abstraction, resulting in very expressive modelling of systems. The article uses Cisco packet tracer 7.2 version, which consists of four subcategories of smart things—home, smart city, industrial and power grid, to design an IoT based control system for a fertilizer manufacturing plant. The packet tracer also consists of boards—microcontrollers (MCU-PT), and single boarded computers (SBC-PT), as well as actuators and sensors. The model facilitates flexible communication opportunities among things—machines, databases, and Human Machine Interfaces (HMIs). Implementation of the IoT system brings finer process control as the operating conditions are monitored online and are broadcasted to all stakeholders in real-time for quicker action on deviations. The model developed focuses on three process plants;steam raising, nitric acid, and ammonium nitrate plants. Key process parameters are saturated steam temperature, fuel flowrates, CO and SO<sub>x</sub> emissions, converter head temperature, NO<sub>x</sub> emissions, neutralisation temperature, solution temperature, and evaporator steam pressure. The parameters need to be monitored in order to ensure quality, safety, and efficiency. Through the Cisco packet tracer platform, a use case, physical layout, network layout, IoT layout, configuration, and simulation interface were developed.展开更多
Many Internet of Things(IoT)systems are based on the intercommunication among different devices and centralized systems.Nowadays,there are several commercial and research platforms available to simplify the creation o...Many Internet of Things(IoT)systems are based on the intercommunication among different devices and centralized systems.Nowadays,there are several commercial and research platforms available to simplify the creation of such IoT systems.However,developing these systems can often be a tedious task.To address this challenge,a proposed solution involves the implementation of a unified program or script that encompasses the entire system,including IoT devices functionality.This approach is based on an abstraction,integrating the control of the devices in a single program through a programmable object.Subsequently,the proposal processes the unified script to generate the centralized system code and a controller for each device.By adopting this approach,developers will be able to create IoT systems with significantly reduced implementation costs,surpassing current platforms by more than 10%.The results demonstrate that the single program approach can significantly accelerate the development of IoT systems relying on device communication.展开更多
Since the worldwide spread of internet-connected devices and rapid advances made in Internet of Things(IoT)systems,much research has been done in using machine learning methods to recognize IoT sensors data.This is pa...Since the worldwide spread of internet-connected devices and rapid advances made in Internet of Things(IoT)systems,much research has been done in using machine learning methods to recognize IoT sensors data.This is particularly the case for optical character recognition of handwritten scripts.Recognizing text in images has several useful applications,including content-based image retrieval,searching and document archiving.The Arabic language is one of the mostly used tongues in the world.However,Arabic text recognition in imagery is still very much in the nascent stage,especially handwritten text.This is mainly due to the language complexities,different writing styles,variations in the shape of characters,diacritics,and connected nature of Arabic text.In this paper,two deep learning models were proposed.The first model was based on a sequence-to-sequence recognition,while the second model was based on a fully convolution network.To measure the performance of these models,a new dataset,called QTID(Quran Text Image Dataset)was devised.This is the first Arabic dataset that includes Arabic diacritics.It consists of 309,720 different 192×64 annotated Arabic word images,which comprise 2,494,428 characters in total taken from the Holy Quran.The annotated images in the dataset were randomly divided into 90%,5%,and 5%sets for training,validation,and testing purposes,respectively.Both models were set up to recognize the Arabic Othmani font in the QTID.Experimental results show that the proposed methods achieve state-of-the-art outcomes.Furthermore,the proposed models surpass expectations in terms of character recognition rate,F1-score,average precision,and recall values.They are superior to the best Arabic text recognition engines like Tesseract and ABBYY FineReader.展开更多
Smart and interconnected devices can generate meaningful patient data and exchange it automatically without any human intervention in order to realize the Internet of Things(IoT)in healthcare(HIoT).Due to more and mor...Smart and interconnected devices can generate meaningful patient data and exchange it automatically without any human intervention in order to realize the Internet of Things(IoT)in healthcare(HIoT).Due to more and more online security and data hijacking attacks,the confidentiality,integrity and availability of data are considered serious issues in HIoT applications.In this regard,lightweight block ciphers(LBCs)are promising in resourceconstrained environment where security is the primary consideration.The prevalent challenge while designing an LBC for the HIoT environment is how to ascertain platform performance,cost,and security.Most of the existing LBCs primarily focus on text data or grayscale images.The main focus of this paper is about securing color images in a cost-effective way.We emphasis high confidentiality of color images captured by cameras in resource-constrained smartphones,and high confidentiality of sensitive images transmitted by low-power sensors in IoT systems.In order to reduce computational complexity and simulation time,the proposed Lightweight Symmetric Block Cipher(LSBC)exploits chaos-based confusion-diffusion operations at the inter-block level using a single round.The strength of LSBC is assessed by cryptanalysis,while it is ranked by comparing it to other privacy-preserving schemes.Our results show that the proposed cipher produces promising results in terms of key sensitivity and differential attacks,which proves that our LSBC is a good candidate for image security in HIoT.展开更多
The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed wo...The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.展开更多
In the era of the Internet of Things(IoT),the ever-increasing number of devices connected to the IoT networks also increases the energy consumption on the edge.This is prohibitive since the devices living on the edge ...In the era of the Internet of Things(IoT),the ever-increasing number of devices connected to the IoT networks also increases the energy consumption on the edge.This is prohibitive since the devices living on the edge are generally resource constrained devices in terms of energy consumption and computational power.Thus,trying to tackle this issue,in this paper,a fully automated end-to-end IoT system for real time monitoring of the status of a moving vehicle is proposed.The IoT system consists mainly of three components:(1)the ultra-lowpower consumptionWireless SensorNode(WSN),(2)the IoT gateway and(3)the IoT platform.In this scope,a selfpoweredWSN having ultra-low energy consumption(less than 10 mJ),which can be produced by environmental harvesting systems,is developed.WSN is used for collecting sensors’measurements from the vehicle and transmitting them to the IoT gateway,by exploiting a low energy communication protocol(i.e.,BLE).A powerful IoT gateway gathers the sensors’measurements,harmonizes,stores temporary and transmits them wirelessly,to a backend server(i.e.,LTE).And finally,the IoT platform,which in essence is a web application user interface(UI),used mainly for almost real time visualization of sensors’measurements,but also for sending alerts and control signals to enable actuators,installed in the vehicle near to the sensors field.The proposed system is scalable and it can be adopted for monitoring a large number of vehicles,thus providing a fully automatic IoT solution for vehicle fleet management.Moreover,it can be extended for simultaneous monitoring of additional parameters,supporting other low energy communication protocols and producing various kinds of alerts and control signals.展开更多
The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare I...The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.展开更多
Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e....Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.展开更多
With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Alth...With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.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
The Industrial Internet of Things(IIoT),combined with the Cyber-Physical Systems(CPS),is transforming industrial automation but also poses great cybersecurity threats because of the complexity and connectivity of the ...The Industrial Internet of Things(IIoT),combined with the Cyber-Physical Systems(CPS),is transforming industrial automation but also poses great cybersecurity threats because of the complexity and connectivity of the systems.There is a lack of explainability,challenges with imbalanced attack classes,and limited consideration of practical edge–cloud deployment strategies in prior works.In the proposed study,we suggest an Impact-Aware Taxonomy-Driven Machine Learning Framework with Edge Deployment and SHapley Additive exPlanations(SHAP)-based Explainable AI(XAI)to attack detection and classification in IIoT-CPS settings.It includes not only unsupervised clustering(K-Means and DBSCAN)to extract latent traffic patterns but also supervised classification based on taxonomy to classify 33 different kinds of attacks into seven high-level categories:Flood Attacks,Botnet/Mirai,Reconnaissance,Spoofing/Man-In-The-Middle(MITM),Injection Attacks,Backdoors/Exploits,and Benign.The three machine learning algorithms,Random Forest,XGBoost,and Multi-Layer Perceptron(MLP),were trained on a realworld dataset of more than 1 million network traffic records,with overall accuracy of 99.4%(RF),99.5%(XGBoost),and 99.1%(MLP).Rare types of attacks,such as injection attacks and backdoors,were examined even in the case of extreme imbalance between the classes.SHAP-based XAI was performed on every model to help gain transparency and trust in the model and identify important features that drive the classification decisions,such as inter-arrival time,TCP flags,and protocol type.A workable edge-computing implementation strategy is proposed,whereby lightweight computing is performed at the edge devices and heavy,computation-intensive analytics is performed at the cloud.This framework is highly accurate,interpretable,and has real-time application,hence a robust and scalable solution to securing IIoT-CPS infrastructure against dynamic cyber-attacks.展开更多
The purpose of this research is to design and develop a demonstration Set of a water cooling system using a Peltier with solar energy and technology,and IoT(Internet of Things),and test and measure the performance of ...The purpose of this research is to design and develop a demonstration Set of a water cooling system using a Peltier with solar energy and technology,and IoT(Internet of Things),and test and measure the performance of the Peltier Plate Water Cooling System Demonstration Set under different environmental conditions.To be used as a model for clean energy systems and experimental learning materials.The prototype system consists of a 100-W solar panel,a 12 V 20 Ah battery,a Peltier plate,a DS18B20 sensor,and a NodeMCU microcontroller.The system performance is determined by analyzing the energy drawn from the water(Q)compared to the electrical energy supplied to the plate(Q2)and calculating the coefficient of performance(COP)value to evaluate the performance of the system,as well as testing the operation under different light conditions(morning,noon,evening)with real-time temperature data and control behavior recorded via IoT(Internet of Things).The results showed that the system can reduce the water temperature by an average of 4°C-7.5°C within 60-90 min,with an average COP(COEFFICIENT OF PERFORMANCE)in the range of 0.3-0.4 during unstable solar energy periods.The system can respond to commands via the Blynk application in under 2 s and can also operate continuously on battery backup power during low-light hours.展开更多
文摘Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace.Network intrusion detection is usually regarded as an efficient means of dealing with security attacks.Many ways have been presented,utilizing various strategies and focusing on different types of visitors.Anomaly-based network intrusion monitoring is an essential area of intrusion detection investigation and development.Despite extensive research on anomaly-based network detection,there is still a lack of comprehensive literature reviews covering current methodologies and datasets.Despite the substantial research into anomaly-based network intrusion detection algorithms,there is a dearth of a research evaluation of new methodologies and datasets.We explore and evaluate 50 highest publications on anomaly-based intrusion detection using an in-depth review of related literature techniques.Our work thoroughly explores the technological environment of the subject in order to help future research in this sector.Our examination is carried out from the relevant angles:application areas,data preprocessing and threat detection approaches,assessment measures,and datasets.We select unresolved research difficulties and underexplored research areas from every viewpoint recommendation of the study.Finally,we outline five potentially increased research areas for the future.
文摘Traditional fire safety management in the electric power industry has significant drawbacks,including a lack of data,difficulty of maintenance,lack of supervision,and lack of interaction.This type of management lags behind current advanced safety management concepts such as“gate advancement”and“full process man-agement”,and it fails to meet the needs of future energy internet construction and development.In response to these problems,an internet of things system for smart firefighting in the electric power industry was constructed in this study.This system defines a centralized information window,trains a power intelligent firefighting brain,establishes a firefighting cloud management and control system,constructs a power firefighting interaction mech-anism,and performs multi-party coordination of firefighting mechanisms to realize concept of“a whole network on one screen and everything in one network”for managing fires.
文摘The healthcare internet of things(IoT)system has dramatically reshaped this important industry sector.This system employs the latest technology of IoT and wireless medical sensor networks to support the reliable connection of patients and healthcare providers.The goal is the remote monitoring of a patient’s physiological data by physicians.Moreover,this system can reduce the number and expenses of healthcare centers,make up for the shortage of healthcare centers in remote areas,enable consultation with expert physicians around the world,and increase the health awareness of communities.The major challenges that affect the rapid deployment and widespread acceptance of such a system are the weaknesses in the authentication process,which should maintain the privacy of patients,and the integrity of remote medical instructions.Current research results indicate the need of a flexible authentication scheme.This study proposes a scheme with enhanced security for healthcare IoT systems,called an end-to-end authentication scheme for healthcare IoT systems,that is,an E2EA.The proposed scheme supports security services such as a strong and flexible authentication process,simultaneous anonymity of the patient and physician,and perfect forward secrecy services.A security analysis based on formal and informal methods demonstrates that the proposed scheme can resist numerous security-related attacks.A comparison with related authentication schemes shows that the proposed scheme is efficient in terms of communication,computation,and storage,and therefore cannot only offer attractive security services but can reasonably be applied to healthcare IoT systems.
基金the National Natural Science Foundationof China(No.31760345).
文摘As the agricultural internet of things(IoT)technology has evolved,smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments.In this paper,we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots,which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters.First,the speeded-up robust feature(SURF)extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular parallel vision system.Then the confidence density image is constructed by integrating the enhanced elevation image and the corresponding binarized crop row image,where the edge contour and the height information of crop row are fused to extract the navigation parameters(θ,d)based on the model of a smart agricultural robot.Finally,the five navigation network instruction sets are designed based on the navigation angleθand the lateral distance d,which represent the basic movements for a certain type of smart agricultural robot working in a field.Simulated experimental results in the laboratory show that the algorithm proposed in this study is effective with small turning errors and low standard deviations,and can provide a valuable reference for the further practical application of binocular vision navigation systems in smart agricultural robots in the agricultural IoT system.
基金supported by the National Key Research&Development Program(No.2016YFB1000104).
文摘Cloud computing has been exploited in managing large-scale IoT systems.IoT cloud servers usually handle a large number of requests from various IoT devices.Due to the fluctuant and heavy workload,the servers require the cloud to provide high scalability,stable performance,low price and necessary functionalities.However,traditional clouds usually offer computing service with the abstraction of virtual machine(VM),which can hardly meet these requirements.Meanwhile,different cloud vendors provide different performance stabilities and price models,which fluctuate according to the dynamic workload.A single cloud cannot satisfy all the requirements of the IoT scenario well.The JointCloud computing model empowers the cooperation among multiple public clouds.However,it is still difficult to dynamically schedule the workload on different clouds based on the VM abstraction.This paper introduces HCloud,a trusted JointCloud platform for IoT systems using serverless computing model.HCloud allows an IoT server to be implemented with multiple serverless functions and schedules these functions on different clouds based on a schedule policy.The policy is specified by the client and includes the required functionalities,execution resources,latency,price and so on.HCloud collects the status of each cloud and dispatches serverless functions to the most suitable cloud based on the schedule policy.By leveraging the blockchain technology,we further enforce that our system can neither fake the cloud status nor wrongly dispatch the target functions.We have implemented a prototype of HCloud and evaluated it by simulating multiple cloud providers.The evaluation results show that HCloud can greatly improve the performance of serverless workloads with negligible costs.
基金supported by the National Key Research and Development Program of China(2020YFE0200600)the National Natural Science Foundation of China(U22B2026)。
文摘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.
文摘Design and implementation of Internet of Things (IoT) systems require platforms with smart things and components. Two dominant architectural approaches for developing IoT systems are mashup-based and model-based approaches. Mashup approaches use existing services and are mainly suitable for less critical, personalized applications. Web development tools are widely used in mashup approaches. Model-based techniques describe a system on a higher level of abstraction, resulting in very expressive modelling of systems. The article uses Cisco packet tracer 7.2 version, which consists of four subcategories of smart things—home, smart city, industrial and power grid, to design an IoT based control system for a fertilizer manufacturing plant. The packet tracer also consists of boards—microcontrollers (MCU-PT), and single boarded computers (SBC-PT), as well as actuators and sensors. The model facilitates flexible communication opportunities among things—machines, databases, and Human Machine Interfaces (HMIs). Implementation of the IoT system brings finer process control as the operating conditions are monitored online and are broadcasted to all stakeholders in real-time for quicker action on deviations. The model developed focuses on three process plants;steam raising, nitric acid, and ammonium nitrate plants. Key process parameters are saturated steam temperature, fuel flowrates, CO and SO<sub>x</sub> emissions, converter head temperature, NO<sub>x</sub> emissions, neutralisation temperature, solution temperature, and evaporator steam pressure. The parameters need to be monitored in order to ensure quality, safety, and efficiency. Through the Cisco packet tracer platform, a use case, physical layout, network layout, IoT layout, configuration, and simulation interface were developed.
文摘Many Internet of Things(IoT)systems are based on the intercommunication among different devices and centralized systems.Nowadays,there are several commercial and research platforms available to simplify the creation of such IoT systems.However,developing these systems can often be a tedious task.To address this challenge,a proposed solution involves the implementation of a unified program or script that encompasses the entire system,including IoT devices functionality.This approach is based on an abstraction,integrating the control of the devices in a single program through a programmable object.Subsequently,the proposal processes the unified script to generate the centralized system code and a controller for each device.By adopting this approach,developers will be able to create IoT systems with significantly reduced implementation costs,surpassing current platforms by more than 10%.The results demonstrate that the single program approach can significantly accelerate the development of IoT systems relying on device communication.
基金funded by the Australian Research Data Common(ARDC),project code—RG192500 that will be used for paying the APC of this manuscript.
文摘Since the worldwide spread of internet-connected devices and rapid advances made in Internet of Things(IoT)systems,much research has been done in using machine learning methods to recognize IoT sensors data.This is particularly the case for optical character recognition of handwritten scripts.Recognizing text in images has several useful applications,including content-based image retrieval,searching and document archiving.The Arabic language is one of the mostly used tongues in the world.However,Arabic text recognition in imagery is still very much in the nascent stage,especially handwritten text.This is mainly due to the language complexities,different writing styles,variations in the shape of characters,diacritics,and connected nature of Arabic text.In this paper,two deep learning models were proposed.The first model was based on a sequence-to-sequence recognition,while the second model was based on a fully convolution network.To measure the performance of these models,a new dataset,called QTID(Quran Text Image Dataset)was devised.This is the first Arabic dataset that includes Arabic diacritics.It consists of 309,720 different 192×64 annotated Arabic word images,which comprise 2,494,428 characters in total taken from the Holy Quran.The annotated images in the dataset were randomly divided into 90%,5%,and 5%sets for training,validation,and testing purposes,respectively.Both models were set up to recognize the Arabic Othmani font in the QTID.Experimental results show that the proposed methods achieve state-of-the-art outcomes.Furthermore,the proposed models surpass expectations in terms of character recognition rate,F1-score,average precision,and recall values.They are superior to the best Arabic text recognition engines like Tesseract and ABBYY FineReader.
基金This work was supported by the King Saud University (in Riyadh, SaudiArabia) through the Researcher Supporting Project Number (RSP–2021/387).
文摘Smart and interconnected devices can generate meaningful patient data and exchange it automatically without any human intervention in order to realize the Internet of Things(IoT)in healthcare(HIoT).Due to more and more online security and data hijacking attacks,the confidentiality,integrity and availability of data are considered serious issues in HIoT applications.In this regard,lightweight block ciphers(LBCs)are promising in resourceconstrained environment where security is the primary consideration.The prevalent challenge while designing an LBC for the HIoT environment is how to ascertain platform performance,cost,and security.Most of the existing LBCs primarily focus on text data or grayscale images.The main focus of this paper is about securing color images in a cost-effective way.We emphasis high confidentiality of color images captured by cameras in resource-constrained smartphones,and high confidentiality of sensitive images transmitted by low-power sensors in IoT systems.In order to reduce computational complexity and simulation time,the proposed Lightweight Symmetric Block Cipher(LSBC)exploits chaos-based confusion-diffusion operations at the inter-block level using a single round.The strength of LSBC is assessed by cryptanalysis,while it is ranked by comparing it to other privacy-preserving schemes.Our results show that the proposed cipher produces promising results in terms of key sensitivity and differential attacks,which proves that our LSBC is a good candidate for image security in HIoT.
文摘The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.
基金support from the European Union’s Horizon 2020 Research and Innovation Programme for project InComEss under Grant Agreement Number 862597.
文摘In the era of the Internet of Things(IoT),the ever-increasing number of devices connected to the IoT networks also increases the energy consumption on the edge.This is prohibitive since the devices living on the edge are generally resource constrained devices in terms of energy consumption and computational power.Thus,trying to tackle this issue,in this paper,a fully automated end-to-end IoT system for real time monitoring of the status of a moving vehicle is proposed.The IoT system consists mainly of three components:(1)the ultra-lowpower consumptionWireless SensorNode(WSN),(2)the IoT gateway and(3)the IoT platform.In this scope,a selfpoweredWSN having ultra-low energy consumption(less than 10 mJ),which can be produced by environmental harvesting systems,is developed.WSN is used for collecting sensors’measurements from the vehicle and transmitting them to the IoT gateway,by exploiting a low energy communication protocol(i.e.,BLE).A powerful IoT gateway gathers the sensors’measurements,harmonizes,stores temporary and transmits them wirelessly,to a backend server(i.e.,LTE).And finally,the IoT platform,which in essence is a web application user interface(UI),used mainly for almost real time visualization of sensors’measurements,but also for sending alerts and control signals to enable actuators,installed in the vehicle near to the sensors field.The proposed system is scalable and it can be adopted for monitoring a large number of vehicles,thus providing a fully automatic IoT solution for vehicle fleet management.Moreover,it can be extended for simultaneous monitoring of additional parameters,supporting other low energy communication protocols and producing various kinds of alerts and control signals.
文摘The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.
文摘Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.
基金funded by the Joint Project of Industry-University-Research of Jiangsu Province(Grant:BY20231146).
文摘With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.
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。
基金funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan(Grant No.AP23489127)。
文摘The Industrial Internet of Things(IIoT),combined with the Cyber-Physical Systems(CPS),is transforming industrial automation but also poses great cybersecurity threats because of the complexity and connectivity of the systems.There is a lack of explainability,challenges with imbalanced attack classes,and limited consideration of practical edge–cloud deployment strategies in prior works.In the proposed study,we suggest an Impact-Aware Taxonomy-Driven Machine Learning Framework with Edge Deployment and SHapley Additive exPlanations(SHAP)-based Explainable AI(XAI)to attack detection and classification in IIoT-CPS settings.It includes not only unsupervised clustering(K-Means and DBSCAN)to extract latent traffic patterns but also supervised classification based on taxonomy to classify 33 different kinds of attacks into seven high-level categories:Flood Attacks,Botnet/Mirai,Reconnaissance,Spoofing/Man-In-The-Middle(MITM),Injection Attacks,Backdoors/Exploits,and Benign.The three machine learning algorithms,Random Forest,XGBoost,and Multi-Layer Perceptron(MLP),were trained on a realworld dataset of more than 1 million network traffic records,with overall accuracy of 99.4%(RF),99.5%(XGBoost),and 99.1%(MLP).Rare types of attacks,such as injection attacks and backdoors,were examined even in the case of extreme imbalance between the classes.SHAP-based XAI was performed on every model to help gain transparency and trust in the model and identify important features that drive the classification decisions,such as inter-arrival time,TCP flags,and protocol type.A workable edge-computing implementation strategy is proposed,whereby lightweight computing is performed at the edge devices and heavy,computation-intensive analytics is performed at the cloud.This framework is highly accurate,interpretable,and has real-time application,hence a robust and scalable solution to securing IIoT-CPS infrastructure against dynamic cyber-attacks.
基金it was supported in kind through resources,technical consultation,and laboratory access generously provided by Chaiyapon Thongchaisuratkrul and relevant institutions.
文摘The purpose of this research is to design and develop a demonstration Set of a water cooling system using a Peltier with solar energy and technology,and IoT(Internet of Things),and test and measure the performance of the Peltier Plate Water Cooling System Demonstration Set under different environmental conditions.To be used as a model for clean energy systems and experimental learning materials.The prototype system consists of a 100-W solar panel,a 12 V 20 Ah battery,a Peltier plate,a DS18B20 sensor,and a NodeMCU microcontroller.The system performance is determined by analyzing the energy drawn from the water(Q)compared to the electrical energy supplied to the plate(Q2)and calculating the coefficient of performance(COP)value to evaluate the performance of the system,as well as testing the operation under different light conditions(morning,noon,evening)with real-time temperature data and control behavior recorded via IoT(Internet of Things).The results showed that the system can reduce the water temperature by an average of 4°C-7.5°C within 60-90 min,with an average COP(COEFFICIENT OF PERFORMANCE)in the range of 0.3-0.4 during unstable solar energy periods.The system can respond to commands via the Blynk application in under 2 s and can also operate continuously on battery backup power during low-light hours.
