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英语角 2024年第2期 38-41页

A.A Sunny Day-个晴天The visitor to L ondon is very angry.He says to his guide,"Rain,rain,rain.fog,fog,fog.When do you have sunny days in England?""I say,"replies the guide,"That is a difficult question.Last year|belie... 详细信息

关键词：快乐阅读 sunny fog

SIMAD:Secure Intelligent Method for IoT-fog Environments Attacks Detection

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Computers, Materials & Continua 2022年第2期70卷 2727-2742页

作者： Wided Ben Daoud Sami Mahfoudhi NTS’Com Research Unit SfaxTunisia Department of MIS CBEQassim UniversityBuraidahAl QassimKSA

The Internet of Thing IoT paradigm has emerged in numerous domains and it has achieved an exponential progress.Nevertheless,alongside this advancement,IoT networks are facing an ever-increasing rate of security risks because of the continuous and rapid changes in network environments.In order to overcome these security challenges,the fog system has delivered a powerful environment that provides additional resources for a more improved data security.However,because of the emerging of various breaches,several attacks are ceaselessly emerging in IoT and fog environment.Consequently,the new emerging applications in IoT-fog environment still require novel,distributed,and intelligent security models,controls,and decisions.In addition,the ever-evolving hacking techniques and methods and the expanded risks surfaces have demonstrated the importance of attacks detection systems.This proves that even advanced solutions face difficulties in discovering and recognizing these small variations of attacks.In fact,to address the above problems,Artificial Intelligence(AI)methods could be applied on the millions of terabytes of collected information to enhance and optimize the processes of IoT and fog systems.In this respect,this research is designed to adopt a new security scheme supported by an advanced machine learning algorithm to ensure an intelligent distributed attacks detection and a monitoring process that detects malicious attacks and updates threats signature databases in IoTfog environments.We evaluated the performance of our distributed approach with the application of certain machine learningmechanisms.The experiments show that the proposed scheme,applied with the Random Forest(RF)is more efficient and provides better accuracy(99.50%),better scalability,and lower false alert rates.In this regard,the distribution character of our method brings about faster detection and better learning.

关键词： Attack detection fog IoT network machine learning distributed mechanism

From capture to transport:A review of engineered surfaces for fog collection

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液滴(英文) 2023年第2期2卷 46-68页

作者： Youhua Jiang Christian Machado Kyoo-Chul K.Park Department of Mechanical Engineering Guangdong Technion—Israel Institute of TechnologyShantouChina Department of Mechanical Engineering Northwestern UniversityEvanstonIllinoisUSA

Collecting microscale water droplets suspended in the wind,that is,fog,using permeable surfaces is a promising solution to the worldwide problem of water scarcity and is of great interest to industries,such as mist elimination and recapturing water in cooling towers.In the past few decades,this topic has attracted a drastically increasing number of researchers across a wide range of subjects.However,many aspects remain unclear,such as the definition and process of fog collection,fog collection determined from the perspectives of both the fog capture process and the liquid transport process,and how surface characteristics affect fog collection performance.In this review,we introduce and discuss fog collection from the perspectives of aerodynamics-governed fog-capturing processes and interfacial-phenomena-determined liquid transport processes.Then,an emphasis is given to the design and engineering of permeable surfaces at different length scales to optimize the fog collection performance,including the dimension,morphology,and arrangement of wires at the millimetric scale,unidirectional spreading,and Laplace pressure gradient induced by asymmetric surface geometry and nano-/microstructures.At last,a brief outlook of future research directions is provided.

关键词： fog transport unidirectional

Paillier Cryptography Based Message Authentication Code for IoMT Security

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Computer Systems Science & Engineering 2023年第3期44卷 2209-2223页

作者： S.Siamala Devi Chandrakala Kuruba Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Sri Krishna College of TechnologyCoimbatore641042India Department of Computer Science and Engineering Vignan Nirula Institute of Technology and ScienceGuntur522009India Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Health care visualization through Internet of Things(IoT)over wireless sensor network(WSN)becomes a current research attention due to medical sensor evolution of devices.The digital technology-based communication system is widely used in all *** of medical thing(IoMT)assisted health-care application ensures the continuous health monitoring of a patient and provides the early awareness of the one who is suffered without human participation.These smart medical devices may consume with limited resources and also the data generated by these devices are large in size.These IoMT based applications suffer from the issues such as security,anonymity,privacy,and interoper-ability.To overcome these issues,data aggregation methods are the solution that can concatenate the data generated by the sensors and forward it into the base station through fog node with efficient encryption and decryption.This article proposed a well-organized data aggregation and secured transmission approach.The data generated by the sensor are collected and compressed.Aggregator nodes(AN)received the compressed data and concatenate it.The concatenated and encrypted data is forward to fog node using the enhanced Paillier cryptogra-phy-based encryption with Message Authentication code(MAC).fog node extracts the forwarded data from AN using fog message extractor method(FME)with decryption.The proposed system ensures data integrity,security and also protects from security threats.This proposed model is simulated in Net-work Simulator 2.35 and the evaluated simulation results proves that the aggregation with MAC code will ensures the security,privacy and also reduces the communication cost.fog node usages in between Aggregator and base station,will reduce the cloud server/base station computational overhead and storage cost.The proposed ideology is compared with existing data aggregation schemes in terms of computational cost,storage cost,communication cost and energy cost.Cost of communication takes 18.7 ms which is much lesser than existing schemes.

关键词： fog IoMT wireless sensor network cloud aggregation encryption decryption energy

Health Data Deduplication Using Window Chunking-Signature Encryption in Cloud

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Intelligent Automation & Soft Computing 2023年第4期36卷 1079-1093页

作者： G.Neelamegam P.Marikkannu Department of Computer Science and Engineering Anna University Regional Campus CoimbatoreCoimbatore641046India

Due to the development of technology in medicine,millions of health-related data such as scanning the images are generated.It is a great challenge to store the data and handle a massive volume of data.Healthcare data is stored in the cloud-fog storage environments.This cloud-fog based health model allows the users to get health-related data from different sources,and duplicated informa-tion is also available in the background.Therefore,it requires an additional sto-rage area,increase in data acquisition time,and insecure data replication in the environment.This paper is proposed to eliminate the de-duplication data using a window size chunking algorithm with a biased sampling-based bloomﬁlter and provide the health data security using the Advanced Signature-Based Encryp-tion(ASE)algorithm in the fog-Cloud Environment(WCA-BF+ASE).This WCA-BF+ASE eliminates the duplicate copy of the data and minimizes its sto-rage space and maintenance cost.The data is also stored in an efﬁcient and in a highly secured manner.The security level in the cloud storage environment Win-dows Chunking Algorithm(WSCA)has got 86.5%,two thresholds two divisors(TTTD)80%,Ordinal in Python(ORD)84.4%,Boom Filter(BF)82%,and the proposed work has got better security storage of 97%.And also,after applying the de-duplication process,the proposed method WCA-BF+ASE has required only less storage space for variousﬁle sizes of 10 KB for 200,400 MB has taken only 22 KB,and 600 MB has required 35 KB,800 MB has consumed only 38 KB,1000 MB has taken 40 KB of storage spaces.

关键词： Health data encryption chunks cloud fog deduplication bloomﬁlter Algorithm 3:Generation of Key

Research on Freezing of Gait Recognition Method Based on Variational Mode Decomposition

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Intelligent Automation & Soft Computing 2023年第9期37卷 2809-2823页

作者： Shoutao Li Ruyi Qu Yu Zhang Dingli Yu School of Communication Engineering Jilin UniversityChangchun130012China Academy of Electric Information Changchun University of Architecture and Civil EngineeringChangchun130607China School of Engineering and Technology Liverpool John Moores UniversityLiverpoolL33AFUK

Freezing of Gait(fog)is the most common and disabling gait disorder in patients with Parkinson’s Disease(PD),which seriously affects the life quality and social function of patients.This paper proposes a fog recognition method based on the Variational Mode Decomposition(VMD).Firstly,VMD instead of the traditional time-frequency analysis method to complete adaptive decomposition to the fog signal.Secondly,to improve the accuracy and speed of the recognition algorithm,use the CART model as the base classifier and perform the feature dimension reduction.Then use the RUSBoost ensemble algorithm to solve the problem of unbalanced sample size and considerable limitations of a single classifier.Finally,the hyperparam-eters of the ensemble classifier are optimized by Bayesian optimization,and the experiment proves that the RUSBoost algorithm can complete the gait recognition task well.Compared with the Adaboost,Tomeklinks-Adaboost and ROS-Adaboost ensemble algorithms,the RUSBoost ensemble algorithm can complete the fog recognition task more efficiently.When the maximum number of splits is 1023,and the number of base classifiers is 100,the performance of the RUSBoost ensemble algorithm can reach the best.The accuracy of the time recognition algorithm was 87.8%,the sensitivity was 89.7%,and the specificity was 87.5%.

关键词： fog VMD RUSBoost Bayesian optimization method

Urban climate in the Tokyo metropolitan area in Japan

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Journal of Environmental Sciences 2017年第9期29卷 54-62页

作者： Jun Matsumoto Fumiaki Fujibe Hideo Takahashi Research Center for Climatology Department of Geography Tokyo Metropolitan University Department of Coupled Ocean-Atmosphere-Land Processes Research JAMSTEC

Long-term climate changes related with urbanization in Tokyo, Japan, and recent temperature and heavy rainfall distribution in the Tokyo metropolitan area are reviewed. A relatively high temperature increase in annual mean temperature at the rate of 3.0°C/century was detected in Tokyo for the period 1901–2015. Some observational evidence showed the existence of both thermal and mechanical effects of urbanization on recent heavy rainfall occurrences, and modeling studies also support precipitation enhancement. Urban influences were recognized in other climatological elements, such as number of fog days, relative humidity, and wind circulation.

关键词： Heat island Cool island Heavy rainfall fog Humidity Sea breeze

Enhanced fat degradation following the addition of a Pseudomonas species to a bioaugmentation product used in grease traps

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Journal of Environmental Sciences 2019年第3期31卷 174-188页

作者： Markella Tzirita Seraphim Papanikolaou Bríd Quilty School of Biotechnology Dublin City University (DCU) Department of Food Science and Human Nutrition Agricultural University of Athens National Institute for Cellular Biotechnology (NICB) DCU

The presence of fats, oils and greases(fogs) in wastewater can lead to many problems including blockages. Investigation of a bioaugmentation product, consisting of Bacillus spp., to degrade butter(1%, W/V) and olive oil(1%, V/V) was performed in aerobic batch cultures for 13-day incubation. Gravimetric analysis of the remaining substrates showed slowly degradation of the oil after a 2-day lag, but no degradation of the butter. Addition of a Pseudomonas putida strain CP1 to the Bacillus spp. population promoted rapidly degradation of both fats after7 days of incubation. High fat accumulation revealed the potential use of the new bacterial mixture for production of added-value compounds. Lipase production only by the Bacillus spp.along with the analysis of the remaining lipids with thin layer chromatography and gas chromatography, suggested that the Bacillus spp. mainly only hydrolyzed the fat. The breakdown products were metabolized by the Pseudomonas putida CP1 performing preferential utilization of unsaturated fatty acids. Investigation of population dynamics using selective plating and a labeled Pseudomonas putida CP1::Tn7-gfp showed domination of the latter. The new mixture performed a successful cooperation with good potential for fog treatment and an aggregative response desirable to fat degradation in grease traps.

关键词： Bacillus Bioaugmentation Biodegradation fog Lipase Pseudomonas

fog-IBDIS:Industrial Big Data Integration and Sharing with fog Computing for Manufacturing Systems

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Engineering 2019年第4期5卷 662-670页

作者： Junliang Wang Peng Zheng Youlong Lv Jingsong Bao Jie Zhang College of Mechanical Engineering Donghua UniversityShanghai 201620China School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Industrial big data integration and sharing(IBDIS)is of great significance in managing and providing data for big data analysis in manufacturing systems.A novel fog-computing-based IBDIS approach called fog-IBDIS is proposed in order to integrate and share industrial big data with high raw data security and low network traffic loads by moving the integration task from the cloud to the edge of networks.First,a task flow graph(TFG)is designed to model the data analysis process.The TFG is composed of several tasks,which are executed by the data owners through the fog-IBDIS platform in order to protect raw data privacy.Second,the function of fog-IBDIS to enable data integration and sharing is presented in five modules:TFG management,compilation and running control,the data integration model,the basic algorithm library,and the management component.Finally,a case study is presented to illustrate the implementation of fog-IBDIS,which ensures raw data security by deploying the analysis tasks executed by the data generators,and eases the network traffic load by greatly reducing the volume of transmitted data.

关键词： fog computing Industrial big data Integration Manufacturing system