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metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

Computer Systems Science & Engineering 2023年第1期44卷 1-17页

作者： R.Bhaskaran P.S.Sujith Kumar G.Shanthi L.Raja Gyanendra Prasad Joshi Woong Cho Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Computer Science and Engineering MEA Engineering CollegeMallapuram679325KeralaIndia Department of Electronics and Communication Engineering Rajalakshmi Institute of TechnologyKuthambakkamChennai600124India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore641202India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,etc.At the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks.This study presents an Improved metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless networks.The proposed IM-EECNL technique involves two major processes namely node localization and clustering.Firstly,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the nodes.Secondly,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the network.Besides,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and load.The performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.

关键词： Wireless networks real time applications clustering node localization energy efficiency metaheuristics

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metaheuristics-based Clustering with Routing Technique for Lifetime Maximization in Vehicular Networks

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Computers, Materials & Continua 2023年第1期74卷 1107-1122页

作者： P.Muthukrishnan P.Muthu Kannan Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha UniversitySaveetha NagarThandalamChennai602105TamilnaduIndia

Recently,vehicular ad hoc networks(VANETs)finds applicability in different domains such as security,rescue operations,intelligent transportation systems(ITS),*** has unique features like high mobility,limited mobility patterns,adequate topologymodifications,and wireless communication.Despite the benefits of VANET,scalability is a challenging issue which could be addressed by the use of cluster-based routing techniques.It enables the vehicles to perform intercluster communication via chosen CHs and optimal routes.The main drawback of VANET network is the network unsteadiness that results in minimum lifetime.In order to avoid reduced network lifetime in VANET,this paper presents an enhanced metaheuristics based clustering with multihop routing technique for lifetime maximization(EMCMHR-LM)in VANET.The presented EMCMHR-LM model involves the procedure of arranging clusters,cluster head(CH)selection,and route selection appropriate for VANETs.The presentedEMCMHR-LMmodel uses slime mold optimization based clustering(SMO-C)technique to group the vehicles into clusters.Besides,an enhanced wild horse optimization based multihop routing(EWHO-MHR)protocol by the optimization of network parameters.The presented EMCMHR-LMmodel is simulated usingNetwork Simulator(NS3)tool and the simulation outcomes reported the enhanced performance of the proposed EMCMHR-LM technique over the other models.

关键词： Scalability VANET clustering multihop routing metaheuristics route selection fitness function

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metaheuristics Enabled Clustering with Routing Scheme for Wireless Sensor Networks

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Computers, Materials & Continua 2022年第12期73卷 5491-5507页

作者： Mashael M.Asiri Saud S.Alotaibi Dalia H.Elkamchouchi Amira Sayed A.Aziz Manar Ahmed Hamza Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Wireless Sensor Network(WSN)is a vital element in Internet of Things(IoT)as the former enables the collection of huge quantities of data in energy-constrained environment.WSN offers independent access to the target region and performs data collection in an effective manner.But energy constraints remain a challenging issue in WSN since it operates on in-built battery.The studies conducted earlier recommended that the energy spent on communication processmust be considerably reduced to improve the efficiency of WSN.Cluster organization and optimal selection of the routes are considered as NP hard optimization problems which can be resolved with the help of metaheuristic algorithms.Clustering and routing are considered as effective approaches in enhancing the energy effectiveness and lifespan of WSN.In this background,the current study develops an Improved Duck and Traveller Optimization(IDTO)-enabled cluster-based Multi-Hop Routing(IDTOMHR)technique for WSN.Primarily,IDTO algorithm is exploited for the selection of Cluster Head(CH)and construction of clusters.Besides,Artificial Gorilla Troops Optimization(ATGO)technique is also used to derive an optimal set of routes to the destination.Both clustering and routing approaches derive a fitness function with the inclusion of multiple input parameters.The proposed IDTOMHR model was experimentally validated for its performance under different aspects.The extensive experimental results confirmed the better performance of IDTOMHR model over other recent approaches.

关键词： Wireless sensor network clustering routing metaheuristics energy efficiency lifetime

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metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

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Intelligent Automation & Soft Computing 2023年第6期36卷 3607-3620页

作者： Adepu Shravan Kumar S.Srinivasan Department of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversitySaveetha NagarThandalamChennai602105India

At the present time,the Industrial Internet of Things(IIoT)has swiftly evolved and emerged,and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data.The use of image sensors as an automa-tion tool for the IIoT is increasingly becoming more common.Due to the fact that this organisation transfers an enormous number of photographs at any one time,one of the most significant issues that it has is reducing the total quantity of data that is sent and,as a result,the available bandwidth,without compromising the image quality.Image compression in the sensor,on the other hand,expedites the transfer of data while simultaneously reducing bandwidth use.The traditional method of protecting sensitive data is rendered less effective in an environment dominated by IoT owing to the involvement of third parties.The image encryp-tion model provides a safe and adaptable method to protect the confidentiality of picture transformation and storage inside an IIoT system.This helps to ensure that image datasets are kept safe.The Linde–Buzo–Gray(LBG)methodology is an example of a vector quantization algorithm that is extensively used and a rela-tively new form of picture reduction known as vector quantization(VQ).As a result,the purpose of this research is to create an artificial humming bird optimi-zation approach that combines LBG-enabled codebook creation and encryption(AHBO-LBGCCE)for use in an IIoT setting.In the beginning,the AHBO-LBGCCE method used the LBG model in conjunction with the AHBO algorithm in order to construct the VQ.The Burrows-Wheeler Transform(BWT)model is used in order to accomplish codebook compression.In addition,the Blowfish algorithm is used in order to carry out the encryption procedure so that security may be attained.A comprehensive experimental investigation is carried out in order to verify the effectiveness of the proposed algorithm in comparison to other algorithms.The experimental values ensure that the suggested approach and the outcomes are examined in a va

关键词： Codebook compression industrial internet of things lbg model metaheuristics vector quantization

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Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for metaheuristics

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Chinese Journal of Electronics 2023年第1期32卷 111-129页

作者： TIAN Ye ZHANG Xingyi HE Cheng TAN Kay Chen JIN Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University School of Artificial Intelligence Anhui University School of Electrical and Electronic Engineering Huazhong University of Science and Technology Department of Computing The Hong Kong Polytechnic University Faculty of Technology Bielefeld University

A large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed,new operators are automatically designed in this work,which are expected to be search space independent and thus exhibit robust performance on different problems.This work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. This work then deduces the generic form of translation, scale, and rotation invariant operators, and proposes a principled approach for the automated design of operators, which searches for high-performance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-the-art ones on a variety of problems with complex landscapes and up to 1000decision variables.

关键词： metaheuristics Evolutionary computation Swarm intelligence Variation operator Transformation invariance Automated design

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Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization metaheuristics

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Computers, Materials & Continua 2022年第2期70卷 4199-4215页

作者： Nebojsa Bacanin Khaled Alhazmi Miodrag Zivkovic K.Venkatachalam Timea Bezdan Jamel Nebhen Singidunum University Danijelova11000BelgradeSerbia National Center for Robotics and IoT Communication and Information Technology Research InstituteKing Abdulaziz City for Science and Technology(KACST)Riyadh12371Saudi Arabia Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Prince Sattam Bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia

In the domain of artificial neural networks,the learning process represents one of the most challenging tasks.Since the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or suboptimal values for the problem at hand.However,to a very large search space,it is very difficult to find the proper values of connection weights and biases.Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima.Most commonly,back-propagation is used formulti-layer-perceptron training and it can lead to vanishing gradient issue.As an alternative approach,stochastic optimization algorithms,such as nature-inspired metaheuristics are more reliable for complex optimization tax,such as finding the proper values of weights and biases for neural network training.In thiswork,we propose an enhanced brain storm optimization-based algorithm for training neural networks.In the simulations,ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization algorithm.The results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed,due to the capability of balancing the intensification and diversification and avoiding the local minima.The proposed approach obtained the best accuracy on eight out of ten observed dataset,outperforming all other algorithms by 1-2%on average.When mean accuracy is observed,the proposed algorithm dominated on nine out of ten datasets.

关键词： Artificial neural network optimization metaheuristics algorithm hybridization brain storm optimization

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Novel Optimized Feature Selection Using metaheuristics Applied to Physical Benchmark Datasets

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Computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target data.There are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard approaches.Consequently,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been proposed.When using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to instability.Because of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization process.For the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed before.According to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance vs.eleven other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and bGAmethods.Experimental results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Enhanced metaheuristics-Based Clustering Scheme for Wireless Multimedia Sensor Networks

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Computers, Materials & Continua 2022年第11期73卷 4179-4192页

作者： R.Uma Mageswari Sara A.Althubiti Fayadh Alenezi E.Laxmi Lydia Gyanendra Prasad Joshi Woong Cho Department of Computer Science and Engineering Vardhaman College of Engineering(Autonomous)HyderabadTelangana501218India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

Traditional Wireless Sensor Networks(WSNs)comprise of costeffective sensors that can send physical parameters of the target environment to an intended user.With the evolution of technology,multimedia sensor nodes have become the hot research topic since it can continue gathering multimedia content and scalar from the target domain.The existence of multimedia sensors,integrated with effective signal processing and multimedia source coding approaches,has led to the increased application of Wireless Multimedia Sensor Network(WMSN).This sort of network has the potential to capture,transmit,and receive multimedia content.Since energy is a major source in WMSN,novel clustering approaches are essential to deal with adaptive topologies of WMSN and prolonged network lifetime.With this motivation,the current study develops an Enhanced Spider Monkey Optimization-based Energy-Aware Clustering Scheme(ESMO-EACS)for WMSN.The proposed ESMO-EACS model derives ESMO algorithm by incorporating the concepts of SMO algorithm and quantum computing.The proposed ESMO-EACS model involves the design of fitness functions using distinct input parameters for effective construction of clusters.A comprehensive experimental analysis was conducted to validate the effectiveness of the proposed ESMO-EACS technique in terms of different performance measures.The simulation outcome established the superiority of the proposed ESMO-EACS technique to other methods under various measures.

关键词： Wireless multimedia sensor networks clustering spider monkey optimization algorithm energy efficiency metaheuristics quantum computing

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Modified metaheuristics with Weighted Majority Voting Ensemble Deep Learning Model for Intrusion Detection System

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Computer Systems Science & Engineering 2023年第11期47卷 2497-2512页

作者： Mahmoud Ragab Sultanah M.Alshammari Abdullah S.Al-Malaise Al-Ghamdi Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

The Internet of Things(IoT)system has confronted dramatic growth in high dimensionality and data traffic.The system named intrusion detection systems(IDS)is broadly utilized for the enhancement of security posture in an IT infrastructure.An IDS is a practical and suitable method for assuring network security and identifying attacks by protecting it from intrusive hackers.Nowadays,machine learning(ML)-related techniques were used for detecting intrusion in IoTs IDSs.But,the IoT IDS mechanism faces significant challenges because of physical and functional diversity.Such IoT features use every attribute and feature for IDS self-protection unrealistic and difficult.This study develops a Modified metaheuristics with Weighted Majority Voting Ensemble Deep Learning(MM-WMVEDL)model for IDS.The proposed MM-WMVEDL technique aims to discriminate distinct kinds of attacks in the IoT environment.To attain this,the presented MM-WMVEDL technique implements min-max normalization to scale the input dataset.For feature selection purposes,the MM-WMVEDL technique exploits the Harris hawk optimization-based elite fractional derivative mutation(HHO-EFDM)technique.In the presented MM-WMVEDL technique,a Bi-directional long short-term memory(BiLSTM),extreme learning machine(ELM)and an ensemble of gated recurrent unit(GRU)models take place.A wide range of simulation analyses was performed on CICIDS-2017 dataset to exhibit the promising performance of the MM-WMVEDL technique.The comparison study pointed out the supremacy of the MM-WMVEDL method over other recent methods with accuracy of 99.67%.

关键词： Internet of Things intrusion detection system machine learning ensemble deep learning metaheuristics

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Enhanced metaheuristics with Machine Learning Enabled Cyberattack Detection Model

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Intelligent Automation & Soft Computing 2023年第9期37卷 2849-2863页

作者： Ahmed S.Almasoud Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia

The Internet of Things(IoT)is considered the next-gen connection network and is ubiquitous since it is based on the Internet.Intrusion Detection System(IDS)determines the intrusion performance of terminal equipment and IoT communication procedures from IoT environments after taking equivalent defence measures based on the identified behaviour.In this back-ground,the current study develops an Enhanced metaheuristics with Machine Learning enabled Cyberattack Detection and Classification(EMML-CADC)model in an IoT environment.The aim of the presented EMML-CADC model is to detect cyberattacks in IoT environments with enhanced efficiency.To attain this,the EMML-CADC model primarily employs a data preprocessing stage to normalize the data into a uniform format.In addition,Enhanced Cat Swarm Optimization based Feature Selection(ECSO-FS)approach is followed to choose the optimal feature subsets.Besides,Mayfly Optimization(MFO)with Twin Support Vector Machine(TSVM),called the MFO-TSVM model,is utilized for the detection and classification of cyberattacks.Here,the MFO model has been exploited to fine-tune the TSVM variables for enhanced results.The performance of the proposed EMML-CADC model was validated using a benchmark dataset,and the results were inspected under several measures.The comparative study concluded that the EMML-CADC model is superior to other models under different measures.

关键词： metaheuristics cyberattack detection machine learning cat swarm optimization security

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