A survey of edge computing-based designs for IoT security

Pervasive IoT applications enable us to perceive,analyze,control,and optimize the traditional physical systems.Recently,security breaches in many IoT applications have indicated that IoT applications may put the physical systems at risk.Severe resource constraints and insufficient security design are two major causes of many security problems in IoT applications.As an extension of the cloud,the emerging edge computing with rich resources provides us a new venue to design and deploy novel security solutions for IoT applications.Although there are some research efforts in this area,edge-based security designs for IoT applications are still in its infancy.This paper aims to present a comprehensive survey of existing IoT security solutions at the edge layer as well as to inspire more edge-based IoT security designs.We first present an edge-centric IoT architecture.Then,we extensively review the edge-based IoT security research efforts in the context of security architecture designs,firewalls,intrusion detection systems,authentication and authorization protocols,and privacy-preserving mechanisms.Finally,we propose our insight into future research directions and open research issues.

Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

In the Internet of Things(IoT)system,relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission efficiency.In Body Sensor Network(BSN)systems,biosensors communicate with receiving devices through relay nodes to improve their limited energy efficiency.When the relay node fails,the biosensor can communicate directly with the receiving device by releasing more transmitting power.However,if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device,the biosensor will be isolated by the system.Therefore,a new combinatorial analysis method is proposed to analyze the influence of random isolation time(RIT)on system reliability,and the competition relationship between biosensor isolation and propagation failure is considered.This approach inherits the advantages of common combinatorial algorithms and provides a new approach to effectively address the impact of RIT on system reliability in IoT systems,which are affected by competing failures.Finally,the method is applied to the BSN system,and the effect of RIT on the system reliability is analyzed in detail.

Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

The medical community has more concern on lung cancer analysis.Medical experts’physical segmentation of lung cancers is time-consuming and needs to be automated.The research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning techniques.Computer-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor detected.The application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation tasks.This research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold segmentation.The Honey hook process categorizes lung cancer based on characteristics retrieved using several classifiers.Considering this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using DNN.The proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)classifier.The study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal patients.In contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the network.It is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

Dietary and metabolomic determinants of relapse in ulcerative colitis patients: A pilot prospective cohort study

AIM To identify demographic, clinical, metabolomic, and lifestyle related predictors of relapse in adult ulcerative colitis(UC) patients.METHODS In this prospective pilot study, UC patients in clinical remission were recruited and followed-up at 12 mo to assess a clinical relapse, or not. At baseline information on demographic and clinical parameters was collected. Serum and urine samples were collected for analysis of metabolomic assays using a combined direct infusion/liquid chromatography tandem mass spectrometry and nuclear magnetic resolution spectroscopy. Stool samples were also collected to measure fecal calprotectin(FCP). Dietary assessment was performed using a validated self-administered food frequency questionnaire. RESULTS Twenty patients were included(mean age: 42.7 ± 14.8 years, females: 55%). Seven patients(35%) experienced a clinical relapse during the follow-up period. While 6 patients(66.7%) with normal body weight developed a clinical relapse, 1 UC patient(9.1%) who was overweight/obese relapsed during the follow-up(P = 0.02). At baseline, poultry intake was significantly higher in patients who were still in remission during follow-up(0.9 oz vs 0.2 oz, P = 0.002). Five patients(71.4%) with FCP > 150 μg/g and 2 patients(15.4%) with normal FCP(≤ 150 μg/g) at baseline relapsed during the follow-up(P = 0.02). Interestingly, baseline urinary and serum metabolomic profiling of UC patients with or without clinical relapse within 12 mo showed a significant difference. The most important metabolites that were responsible for this discrimination were trans-aconitate, cystine and acetamide in urine, and 3-hydroxybutyrate, acetoacetate and acetone in serum. CONCLUSION A combination of baseline dietary intake, fecal calprotectin, and metabolomic factors are associated with risk of UC clinical relapse within 12 mo.

BIFURCATIONS OF TRAVELLING WAVE SOLUTIONS FOR THE GENERALIZED DODD-BULLOUGH-MIKHAILOV EQUATION

在这份报纸，概括 Dodd-Bullough-Mikhailov 方程被学习。周期的波浪和无界的波浪解决方案的存在被使用动态系统的分叉理论的方法证明。在不同参量的条件下面，保证上述答案的存在的各种各样的足够的条件被给。上述旅行答案的一些准确明确的参量的代表被获得。

Identification of candidate genes for drought stress tolerance in rice by the integration of a genetic (QTL) map with the rice genome physical map

Genetic improvement for drought stress tolerance in rice involves the quantitative nature of the trait, which reflects the additive effects of several genetic loci throughout the genome. Yield components and related traits under stressed and well-water conditions were assayed in mapping populations derived from crosses of Azucena×IR64 and Azucena×Bala. To find the candidate rice genes underlying Quantitative Trait Loci (QTL) in these populations, we conducted in silico analysis of a candidate region flanked by the genetic markers RM212 and RM319 on chromosome 1, proximal to the semi-dwarf (sd1) locus. A total of 175 annotated genes were identified from this region. These included 48 genes annotated by functional homology to known genes, 23 pseudogenes, 24 ab initio predicted genes supported by an alignment match to an EST (Expressed sequence tag) of unknown function, and 80 hypothetical genes predicted solely by ab initio means. Among these, 16 candidate genes could potentially be involved in drought stress response.

An Analytical Model for Torus Networks in the Presence of Batch Message Arrivals with Hot-spot Destinations

Interconnection networks are hardware fabrics supporting communications between individual processors in multi-computers. The low-dimensional k-ary n-cubes (or torus) with adaptive wormhole switching have attracted significant research efforts to construct high-performance interconnection networks in contemporary multi-computers. The arrival process and destination dis- tribution of messages have great effects on network performance. With the aim of capturing the characteristics of the realistic traffic pattern and obtaining a deep understanding of the performance behaviour of interconnection networks, this paper presents an analytical model to investigate the message latency in adaptive-routed wormhole-switched torus networks where there exists hot-spot nodes and the message arrivals follow a batch arrival process. Each generated message has a given probability to be directed to the hot-spot node. The average degree of virtual channel multiplexing is computed by the GE/G/1/V queueing system with finite buffer capacity. We compare analytical results of message latency with those obtained through the simulation experiments in order to validate the accuracy of the derived model.

3-D distribution of tensile stress in rock specimens for the Brazilian test

It is claimed that the formula used for calculating the tensile strength of a disk-shaped rock specimen in the Brazilian test is not accurate, because the formula is based on the 2-dimensional elastic theory and only suitable for very long or very short cylin- ders. The Matlab software was used to obtain the 2-dimensional distribution of stress in the rock specimen for Brazilian test. Then the 2-dimensional stress distribution in Brazilian disk was analyzed by the Marc FEM software. It can be found that the results obtained by the two software packages can verify each other. Finally, the 3-dimensional elastic stress in the specimen was calculated. The re- sults demonstrate that the distribution of stress on the cross section of the specimen is similar to that in 2-dimension. However, the value of the stress on the cross section varies along the thickness of the specimen and the stress is bigger when getting closer to the end of the specimen. For the specimen with a height-to-diameter ratio of 1 and a Poisson’s ratio of 0.25, the tensile strength calculat- ed with the classical 2-D formula is 23.3% smaller than the real strength. Therefore, the classical 2-D formula is too conservative.

A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles

Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric vehicles.However,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust issues.In this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch center.Based on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social welfare.We use blockchain to record power trading data for trusted pricing and use smart contracts for transaction matching.The simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.

Note on Implementation of Three-Qubit SWAP Gate

在这份报纸， three-quhit 的合成和实现交换门被讨论。three-quhit 交换门能被分解成 2 two-qubit 的产品交换门，并且它能由 6 扇 CNOT 门被认识到。尽管结果很简单，研究说明了那，为 multi-qubit 门的矩阵分解的当前的方法不能得到那。然后 three-qubit 的实现与 Ising 在三个旋转系统交换门相互作用被调查，控制脉搏和飘移过程的顺序被给实现门。它需要 23 控制脉搏和 12 个飘移过程。因为相互作用不能随意断断续续地被交换， three-qubit 的实现也在特定的量系统交换门不能简单地下来到 2 two-qubit 交换门。

Lipschitz continuity of the optimal value function and KKT solution set in indefinite quadratic programs

When all the involved data in indefinite quadratic programs change simultaneously,we show the locally Lipschtiz continuity of the KKT set of the quadratic programming problem firstly, then we establish the locally Lipschtiz continuity of the KKT solution set. Finally, the similar conclusion for the corresponding optimal value function is obtained.

On the Performance of Traffic Locality Oriented Route Discovery Algorithm with Delay

In MANETs, traffic may follow certain pattern that is not necessarily spatial or temporal but rather to follow special needs as a part of group for collaboration purposes. The source node tends to communicate with a certain set of nodes more than others regardless of their location exhibiting traffic locality where this set changes over time. We introduce a traffic locality oriented route discovery algorithm with delay, TLRDA-D. It utilises traffic locality by establishing a neighbourhood that includes the most likely destinations for a particular source node. The source node broadcasts the route request according to the original routing used. However, each intermediate node broadcasts the route request with a delay beyond this boundary to give priority for route requests that are travelling within their own source node’s neighbourhood region. This ap-proach improves the end-to-end delay and packet loss, as it generates less contention throughout the network. TLRDA-D is analysed using simulation to study the effect of adding a delay to route request propagation and to decide on the amount of the added delay.

解决一维材料黏附行为的实验方法综述

一维(1D)材料(如纳米管和纳米线)的黏附行为在集成一维组件的新型器件以及基于一维阵列的仿生黏合剂的有效制造、功能和可靠性中起着决定性作用。本文汇编并批判性地评估了最近的实验技术,旨在表征由一维材料形成的界面的粘附行为(包括当这些材料与衬底或邻近的一维材料接触时)。讨论了一维材料与表面的构象以及与之相关的多粗糙度接触的发生,并探讨了界面附着和剥离过程中黏附和摩擦的耦合。考虑了一维材料作为增强剂在纳米复合材料中的应用以及相关的界面表征技术。研究了样品制备和黏附测试过程中存在的环境条件对一维界面相互作用的潜在影响,并最终改变了一维材料的粘附行为。最后,简要介绍了当前的挑战和未来的方向,包括测试环境的系统调查和通过表面改性改变附着力。

A low-carbon economic dispatch model for electricity market with wind power based on improved ant-lion optimisation algorithm

Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electricity market transactions.Therefore,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is developed.First,the economic dispatch goal of wind power is be considered.It is projected to save money and reduce the cost of power generation for the system.The model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment.The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two goals.Finally,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden sine.The proposed model and algorithm's rationality is proven through the use of cases.

An Intelligent Secure Adversarial Examples Detection Scheme in Heterogeneous Complex Environments

Image-denoising techniques are widely used to defend against Adversarial Examples(AEs).However,denoising alone cannot completely eliminate adversarial perturbations.The remaining perturbations tend to amplify as they propagate through deeper layers of the network,leading to misclassifications.Moreover,image denoising compromises the classification accuracy of original examples.To address these challenges in AE defense through image denoising,this paper proposes a novel AE detection technique.The proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network(CNN)network structures.The used detector model integrates the classification results of different models as the input to the detector and calculates the final output of the detector based on a machine-learning voting algorithm.By analyzing the discrepancy between predictions made by the model on original examples and denoised examples,AEs are detected effectively.This technique reduces computational overhead without modifying the model structure or parameters,effectively avoiding the error amplification caused by denoising.The proposed approach demonstrates excellent detection performance against mainstream AE attacks.Experimental results show outstanding detection performance in well-known AE attacks,including Fast Gradient Sign Method(FGSM),Basic Iteration Method(BIM),DeepFool,and Carlini&Wagner(C&W),achieving a 94%success rate in FGSM detection,while only reducing the accuracy of clean examples by 4%.

Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in cyberattacks.Though the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the victims.In this background,there is a drastic increase observed in the number of phishing emails sent to potential targets.This scenario necessitates the importance of designing an effective classification model.Though numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the literature.The current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)model.The aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing ones.At the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word elimination.Then,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature vectors.Moreover,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing emails.Furthermore,the CS algorithm is used to fine-tune the parameters involved in the GRU model.The performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several dimensions.Extensive comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing approaches.The proposed model achieved a maximum accuracy of 99.72%.

Timer Entrenched Baited Scheme to Locate and Remove Attacks in MANET

The Mobile Ad-hoc Network(MANET)is a dynamic topology that provides a variety of executions in various disciplines.The most sticky topic in organizationalfields was MANET protection.MANET is helpless against various threats that affect its usability and accessibility.The dark opening assault is considered one of the most far-reaching dynamic assaults that deteriorate the organi-zation's execution and reliability by dropping all approaching packages via the noxious node.The Dark Opening Node aims to deceive any node in the company that wishes to connect to another node by pretending to get the most delicate ability to support the target node.Ad-hoc On-demand Distance Vector(AODV)is a responsive steering convention with no corporate techniques to locate and destroy the dark opening center.We improved AODV by incorporating a novel compact method for detecting and isolating lonely and collaborative black-hole threats that utilize clocks and baits.The recommended method allows MANET nodes to discover and segregate black-hole network nodes over dynamic changes in the network topology.We implement the suggested method's performance with the help of Network Simulator(NS)-3 simulation models.Furthermore,the proposed approach comes exceptionally near to the original AODV,absent black holes in terms of bandwidth,end-to-end latency,error rate,and delivery ratio.

Fine-Grained Multivariate Time Series Anomaly Detection in IoT

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)systems.Multivariate time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and malfunctions.However,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is unknown.Therefore,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between indicators.Accordingly,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)framework.To avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained anomaly.Based on this framework,an algorithm based on Graph Attention Neural Network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal indicators.Extensive simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.

带学习的同步隐私保护频繁模式挖掘

为了提高挖掘结果的准确性,提出基于样例学习和项集同步随机化的隐私保护频繁模式挖掘方法(learning and synchronized privacy preserving frequent pattern mining,简称LS-PPFM).该方法充分利用不需要隐私保护的个体数据,首先对不需要保护的数据学习,得到样例数据中蕴涵的强关联项,然后在对数据随机化时,将强关联项绑定在一起作同步随机化变换,以保持项与项之间的潜在关联性.实验结果表明,相对于项独立随机化,LS-PPFM能够在略微牺牲一定的隐私保护性的情况下,显著提高频繁模式挖掘结果的准确性.

面向变电设备金属锈蚀检测的分层嵌套标注方法

为解决变电设备锈蚀数据集标注过程中经常遭遇的模糊和不确定问题,提出了一种基于分层嵌套的训练样本标注方法.首先,采用较大的矩形框对锈蚀区域进行大面积标注,将视觉上连续的、相邻的或不能清晰划分的锈蚀区域用一个矩形框标注;然后,在标注框内对特征明显并具有相对独立性的区域进行二次标注,形成第2层内部嵌套标注.为了验证分层嵌套方法的有效性,与常用标记方法进行对比实验.结果表明,采用分层嵌套标注方法后,YOLOv5模型的召回率由50.79%提升至59.41%,Faster R-CNN+VGG16模型的召回率由66.50%提升至78.94%,Faster R-CNN+Res101模型的召回率由78.32%提升至84.61%.由此可见,通过分层嵌套标注可以有效提升主流模型在金属锈蚀方面的检测能力.