With the popularity of cloud computing, more and more users store sensitive information in cloud servers. In order to protect the data over the cloud server, symmetric encryption with keyword search has been developed and the phrase search has been proposed subsequently to overcome the inefficiency produced by single/multi‐keyword search. However, most existing phrase search schemes fails to support fault‐tolerant search which is essential to users. Therefore, this paper proposes a fault‐tolerant dynamic phrase search scheme with forward privacy and backward privacy (FTPS). Piecewise Linear Chaotic Map and minhash function are used to blur information, and Bloom filter based index is constructed to realize efficient search and dynamic update simultaneously. Security analysis proves that FTPS can properly preserve the privacy of search user, and experimental results show that FTPS is practical.
{"title":"FTPS: Efficient fault‐tolerant dynamic phrase search over outsourced encrypted data with forward and backward privacy","authors":"You-sheng Zhou, Kexin Liu, P. Vijayakumar","doi":"10.1002/cpe.7360","DOIUrl":"https://doi.org/10.1002/cpe.7360","url":null,"abstract":"With the popularity of cloud computing, more and more users store sensitive information in cloud servers. In order to protect the data over the cloud server, symmetric encryption with keyword search has been developed and the phrase search has been proposed subsequently to overcome the inefficiency produced by single/multi‐keyword search. However, most existing phrase search schemes fails to support fault‐tolerant search which is essential to users. Therefore, this paper proposes a fault‐tolerant dynamic phrase search scheme with forward privacy and backward privacy (FTPS). Piecewise Linear Chaotic Map and minhash function are used to blur information, and Bloom filter based index is constructed to realize efficient search and dynamic update simultaneously. Security analysis proves that FTPS can properly preserve the privacy of search user, and experimental results show that FTPS is practical.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"204 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82710766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zichen Zhao, Xiliang Tong, Ying Sun, D. Bai, Xin Liu, Guojun Zhao, Hanwen Fan, Jun Li, Cejing Zou, Baojia Chen
Instance segmentation is a challenging task that requires both instance‐level and pixel‐level prediction and it has a wide range of applications in autonomous driving, video analysis, scene understandingand so on. The currently dominant instance segmentation methods have excellent accuracy, but they are slow, and the processing speed will be even less satisfactory if the input is a large‐scale image. In order to improve the efficiency and accuracy of instance segmentation of large‐scale images, this article modifies the backbone network based on YOLACT network, adds a multi‐information fusion module and provides an improved BiFPN method to achieve multi‐scale feature fusion, while adding two branches to the first level detector RetinaNet to achieve instance segmentation. The network model is tested on Cityscapes dataset and the results of the experiments show that the improved instance segmentation network in this article improves the accuracy while ensuring the speed of segmentation. The optimized network model size was reduced by 17% compared to YOLACT, and the mAP, mAP50, and mAP75 were improved by 18.3%, 32.1%, and 24.6%, respectively.
{"title":"Large scale instance segmentation of outdoor environment based on improved YOLACT","authors":"Zichen Zhao, Xiliang Tong, Ying Sun, D. Bai, Xin Liu, Guojun Zhao, Hanwen Fan, Jun Li, Cejing Zou, Baojia Chen","doi":"10.1002/cpe.7370","DOIUrl":"https://doi.org/10.1002/cpe.7370","url":null,"abstract":"Instance segmentation is a challenging task that requires both instance‐level and pixel‐level prediction and it has a wide range of applications in autonomous driving, video analysis, scene understandingand so on. The currently dominant instance segmentation methods have excellent accuracy, but they are slow, and the processing speed will be even less satisfactory if the input is a large‐scale image. In order to improve the efficiency and accuracy of instance segmentation of large‐scale images, this article modifies the backbone network based on YOLACT network, adds a multi‐information fusion module and provides an improved BiFPN method to achieve multi‐scale feature fusion, while adding two branches to the first level detector RetinaNet to achieve instance segmentation. The network model is tested on Cityscapes dataset and the results of the experiments show that the improved instance segmentation network in this article improves the accuracy while ensuring the speed of segmentation. The optimized network model size was reduced by 17% compared to YOLACT, and the mAP, mAP50, and mAP75 were improved by 18.3%, 32.1%, and 24.6%, respectively.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73998580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abid Hussain, Kalim Ullah, S. A. Cheema, Akbar Ali Khan, Z. Hussain
This research primarily aims at the development of a new estimation scheme exploiting the argument of dual use of auxiliary information. The objectives are obtained by materializing the new family of estimators, where the dual use of Supplementary Information is substantiated with the launch of the empirical distribution function of the auxiliary variable. The comparative performance evaluation of the newly devised formation is enumerated with respect to the most efficient method, to the best of our knowledge till to date, of Haq et al. along with other promising families of Hussain and Haq and Grover and Kaur. The elaborative account of contemporary advents of the newly proposed family are documented throughout the article.
{"title":"Empirical distribution function based dual use of auxiliary information for the improved estimation of finite population mean","authors":"Abid Hussain, Kalim Ullah, S. A. Cheema, Akbar Ali Khan, Z. Hussain","doi":"10.1002/cpe.7346","DOIUrl":"https://doi.org/10.1002/cpe.7346","url":null,"abstract":"This research primarily aims at the development of a new estimation scheme exploiting the argument of dual use of auxiliary information. The objectives are obtained by materializing the new family of estimators, where the dual use of Supplementary Information is substantiated with the launch of the empirical distribution function of the auxiliary variable. The comparative performance evaluation of the newly devised formation is enumerated with respect to the most efficient method, to the best of our knowledge till to date, of Haq et al. along with other promising families of Hussain and Haq and Grover and Kaur. The elaborative account of contemporary advents of the newly proposed family are documented throughout the article.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83262948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
John F. W. Zaki, A. Nayyar, Surjeet Dalal, Z. H. Ali
The problem with property valuation is that it is extremely complex. It is difficult to objectively model the pricing process or fairly estimate a property value. Many factors can contribute to this complexity such as spatial and time factors. Evaluators and researchers have been trying to model the process for centuries. Up until recently, when computer‐aided valuation systems provided better solutions in the data evaluation and real estate valuation. Nevertheless, they may suffer from low transparency, inaccuracy, and inefficiency. This work explores the ability of machine learning techniques (MLTs) in enhancing economic activities by increasing the accuracy of house price prediction. In this article, XGBoost algorithm has been integrated with outlier sum‐statistic (OS) approach. In the real estate industry, the price of property plays a crucial role in economic growth. The research attempts to predict the price of a house using MLTs. Here, the price of the property is predicted using Extreme Gradient (XG) boosting algorithm and hedonic regression pricing. Both XGBoost and hedonic pricing models use 13 variables as inputs to predict house prices. The contribution of this research lies in the practicality of using XGboost technique to predict house prices. Finally, the accuracy of the prediction algorithms is reported with XGBoosting showing the highest accuracy of 84.1% while the accuracy of the hedonic regression algorithm is 42%.
{"title":"House price prediction using hedonic pricing model and machine learning techniques","authors":"John F. W. Zaki, A. Nayyar, Surjeet Dalal, Z. H. Ali","doi":"10.1002/cpe.7342","DOIUrl":"https://doi.org/10.1002/cpe.7342","url":null,"abstract":"The problem with property valuation is that it is extremely complex. It is difficult to objectively model the pricing process or fairly estimate a property value. Many factors can contribute to this complexity such as spatial and time factors. Evaluators and researchers have been trying to model the process for centuries. Up until recently, when computer‐aided valuation systems provided better solutions in the data evaluation and real estate valuation. Nevertheless, they may suffer from low transparency, inaccuracy, and inefficiency. This work explores the ability of machine learning techniques (MLTs) in enhancing economic activities by increasing the accuracy of house price prediction. In this article, XGBoost algorithm has been integrated with outlier sum‐statistic (OS) approach. In the real estate industry, the price of property plays a crucial role in economic growth. The research attempts to predict the price of a house using MLTs. Here, the price of the property is predicted using Extreme Gradient (XG) boosting algorithm and hedonic regression pricing. Both XGBoost and hedonic pricing models use 13 variables as inputs to predict house prices. The contribution of this research lies in the practicality of using XGboost technique to predict house prices. Finally, the accuracy of the prediction algorithms is reported with XGBoosting showing the highest accuracy of 84.1% while the accuracy of the hedonic regression algorithm is 42%.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80850659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In linear regression model, ridge regression and two‐parameter Liu estimator (LE) are the most widely used methods in recent decade to overcome the problem of multicollinearity especially for ill conditioned cases. In this article, we propose new weighted ridge and Liu estimators which remain positive for each level of multicollinearity and also give smaller mean squared error (MSE) than the existing ridge regression and existing Liu estimators. In addition, a new adaptive LE for k which accounts for the error variance is also proposed to assess the ill condition cases. Furthermore, new weighted ridge estimator of Kibria arithmetic mean method and two parameter Liu estimator with Liu method are also proposed. Extensive Monte‐Carlo simulations are used to evaluate the performance of proposed estimators. Based on MSE criterion, the proposed estimators perform better than the existing estimators in many situations including severe multicollinearity and small signal‐to‐ noise ratio. Two real life applications are also provided to illustrate the usefulness of new estimators.
{"title":"Weighted ridge and Liu estimators for linear regression model","authors":"I. Babar, S. Chand","doi":"10.1002/cpe.7343","DOIUrl":"https://doi.org/10.1002/cpe.7343","url":null,"abstract":"In linear regression model, ridge regression and two‐parameter Liu estimator (LE) are the most widely used methods in recent decade to overcome the problem of multicollinearity especially for ill conditioned cases. In this article, we propose new weighted ridge and Liu estimators which remain positive for each level of multicollinearity and also give smaller mean squared error (MSE) than the existing ridge regression and existing Liu estimators. In addition, a new adaptive LE for k which accounts for the error variance is also proposed to assess the ill condition cases. Furthermore, new weighted ridge estimator of Kibria arithmetic mean method and two parameter Liu estimator with Liu method are also proposed. Extensive Monte‐Carlo simulations are used to evaluate the performance of proposed estimators. Based on MSE criterion, the proposed estimators perform better than the existing estimators in many situations including severe multicollinearity and small signal‐to‐ noise ratio. Two real life applications are also provided to illustrate the usefulness of new estimators.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89514882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shanthi Govindaraju, Wilson Vimala Rani Vinisha, Francis H. Shajin, D. A. Sivasakthi
Intrusion detection systems (IDSs) are the major component of safe network. Due to the high volume of network data, the false alarm report of intrusion to the network and intrusion detection accuracy is the problem of these security systems. The reliability of Internet of Things (IoT) connected devices based on security model is employed to protect user data and preventing devices from engaging in malicious activity. In this article, intrusion detection framework using auto‐metric graph neural network optimized with hybrid woodpecker mating and capuchin search optimization algorithm in IoT Network (IDF‐AGNN‐HYB‐WMA‐CSOA‐ IoT) is proposed. Initially the attacks affected in the IoT data is taken from the dataset such as CSIC 2010 dataset, ISCXIDS2012 dataset, then these data are preprocessed and the features are extracted to remove the redundant information using improved random forest with local least squares. Then the malicious attacks and the normal attacks are classified using the auto‐metric graph neural network. At last hybrid woodpecker mating and capuchin search optimization algorithm (Hyb‐WMA‐CSOA) is utilized to optimize the weight parameters of AGNN. The performance of ISCXIDS2012 dataset of the proposed method shows higher accuracy 25.37%, 29.57%, and 18.67%, compared with existing methods, such as IDF‐ANN‐IoT, IDF‐BMM‐IoT and IDF‐DNN‐IoT respectively.
{"title":"Intrusion detection framework using auto‐metric graph neural network optimized with hybrid woodpecker mating and capuchin search optimization algorithm in IoT network","authors":"Shanthi Govindaraju, Wilson Vimala Rani Vinisha, Francis H. Shajin, D. A. Sivasakthi","doi":"10.1002/cpe.7197","DOIUrl":"https://doi.org/10.1002/cpe.7197","url":null,"abstract":"Intrusion detection systems (IDSs) are the major component of safe network. Due to the high volume of network data, the false alarm report of intrusion to the network and intrusion detection accuracy is the problem of these security systems. The reliability of Internet of Things (IoT) connected devices based on security model is employed to protect user data and preventing devices from engaging in malicious activity. In this article, intrusion detection framework using auto‐metric graph neural network optimized with hybrid woodpecker mating and capuchin search optimization algorithm in IoT Network (IDF‐AGNN‐HYB‐WMA‐CSOA‐ IoT) is proposed. Initially the attacks affected in the IoT data is taken from the dataset such as CSIC 2010 dataset, ISCXIDS2012 dataset, then these data are preprocessed and the features are extracted to remove the redundant information using improved random forest with local least squares. Then the malicious attacks and the normal attacks are classified using the auto‐metric graph neural network. At last hybrid woodpecker mating and capuchin search optimization algorithm (Hyb‐WMA‐CSOA) is utilized to optimize the weight parameters of AGNN. The performance of ISCXIDS2012 dataset of the proposed method shows higher accuracy 25.37%, 29.57%, and 18.67%, compared with existing methods, such as IDF‐ANN‐IoT, IDF‐BMM‐IoT and IDF‐DNN‐IoT respectively.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"154 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73456625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cloud computing environment contains important, essential, or confidential information; therefore, a security solution is needed to prevent this environment from potential attacks. In short, cloud computing has become one of the most sought after technologies in the field of information technology, and among the most dangerous threats. In this article, we propose a hybrid soft computing technique for intrusion detection in web and cloud environment (ST‐IDS). In ST‐IDS, we illustrate whale integrated slap swarm optimization algorithm for pre‐processing which remove the unwanted/repeated data's in dataset. We introduce new clustering technique based on modified tug‐of‐war optimization algorithm which groups the data in different segments. Then, we develop hybrid machine learning technique that is, capsule learning based neural network which categorize the attack in cloud environment. Finally, the proposed ST‐IDS technique can evaluate through standard open source datasets are KDD cup'99 and NSL‐KDD. The performance comparison of the proposed ST‐IDS technique using existing innovative technologies in terms of accuracy, precession, recall, specificity, F measure, false positive rate, and false negative rate.
{"title":"A hybrid soft computing technique for intrusion detection in web and cloud environment","authors":"K. Maheswari, C. Siva, G. Nalinipriya","doi":"10.1002/cpe.7046","DOIUrl":"https://doi.org/10.1002/cpe.7046","url":null,"abstract":"Cloud computing environment contains important, essential, or confidential information; therefore, a security solution is needed to prevent this environment from potential attacks. In short, cloud computing has become one of the most sought after technologies in the field of information technology, and among the most dangerous threats. In this article, we propose a hybrid soft computing technique for intrusion detection in web and cloud environment (ST‐IDS). In ST‐IDS, we illustrate whale integrated slap swarm optimization algorithm for pre‐processing which remove the unwanted/repeated data's in dataset. We introduce new clustering technique based on modified tug‐of‐war optimization algorithm which groups the data in different segments. Then, we develop hybrid machine learning technique that is, capsule learning based neural network which categorize the attack in cloud environment. Finally, the proposed ST‐IDS technique can evaluate through standard open source datasets are KDD cup'99 and NSL‐KDD. The performance comparison of the proposed ST‐IDS technique using existing innovative technologies in terms of accuracy, precession, recall, specificity, F measure, false positive rate, and false negative rate.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"88 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84569995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gang Xian, Xiaorong Zhang, Jie Yu, Guijuan Wang, Wenxiang Yang, Longfang Zhou, Yadong Wu, Xuejun Li, Xin He
Large numbers of jobs are executed on supercomputers almost every day. Unfortunately, many jobs would fail for various reasons, resulting in the waste of resources and the prolonged waiting time for queuing jobs. Job failure prediction can guide adjustment measures in advance, which is vital to the system's overall execution efficiency and reliability. Aiming at the problem that the existing job failure prediction methods are single, the collection of job features is complex and challenging to apply. This article strives to study whether these failed jobs can be predicted with known and synthetic features. We perform a comprehensive analysis of large amounts of historical data and various features and find that two novel features (running path and retry count) can predict job failure well. The running path indicates the application type a job belongs to, and the retry count reflects the user's behavior when the job fails. We propose a job failure prediction framework called PreF on supercomputers using machine learning based on the novel features. The experimental results show that PreF can correctly identify over 89% of jobs, outperforming the latest related methods on the comprehensive evaluation indicator (S_score) by around 4%.
{"title":"PreF: Predicting job failure on supercomputers with job path and user behavior","authors":"Gang Xian, Xiaorong Zhang, Jie Yu, Guijuan Wang, Wenxiang Yang, Longfang Zhou, Yadong Wu, Xuejun Li, Xin He","doi":"10.1002/cpe.7202","DOIUrl":"https://doi.org/10.1002/cpe.7202","url":null,"abstract":"Large numbers of jobs are executed on supercomputers almost every day. Unfortunately, many jobs would fail for various reasons, resulting in the waste of resources and the prolonged waiting time for queuing jobs. Job failure prediction can guide adjustment measures in advance, which is vital to the system's overall execution efficiency and reliability. Aiming at the problem that the existing job failure prediction methods are single, the collection of job features is complex and challenging to apply. This article strives to study whether these failed jobs can be predicted with known and synthetic features. We perform a comprehensive analysis of large amounts of historical data and various features and find that two novel features (running path and retry count) can predict job failure well. The running path indicates the application type a job belongs to, and the retry count reflects the user's behavior when the job fails. We propose a job failure prediction framework called PreF on supercomputers using machine learning based on the novel features. The experimental results show that PreF can correctly identify over 89% of jobs, outperforming the latest related methods on the comprehensive evaluation indicator (S_score) by around 4%.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81069682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Reform mode of sports training and competition organization based on data mining","authors":"Li Wan","doi":"10.1002/cpe.7291","DOIUrl":"https://doi.org/10.1002/cpe.7291","url":null,"abstract":"","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90292062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Behbod Kheradmand, A. Ghaffari, F. S. Gharehchopogh, Mohammad Masdari
In a vehicular ad‐hoc network (VANET), each vehicle is equipped with an on‐board unit to communicate vehicle to vehicle or vehicle to fixed infrastructure. VANET technology is offered to provide many facilities to passengers and drivers, including safety, entertainment, mobile commerce, driver assistance, and emergency alarms. VANET has unique features such as high‐speed node mobility and network topology dynamics. These special features cause many problems such as increased transmission delays and packet loss. On the other hand, providing a good routing plan for VANET is a critical issue. Therefore, this article proposes a cluster‐based routing using in‐vehicle meta‐heuristic algorithms (CRMHA‐VANET) which has two phases. In the first stage, the vehicles are clustered and the most suitable cluster head (CH) is selected using the gray wolf optimization algorithm (GWO). In the next step, the next suitable CH is selected for data transmission in direct paths using the technique for order of preference by similarity to ideal solution (TOPSIS). The performance of the proposed method is analyzed through several criteria such as package delivery rate, end‐to‐end delay and throughput. CRMHA‐VANET results in a 10% to 25% improvement over all performance metrics, that is, packet delivery rate, latency, and throughput, over CRBP (clustering routing based on PSO [particle swarm optimization]), WCV (weight based clustering for VANET), and AODV‐CD methods.
{"title":"Clustering‐based routing protocol using gray wolf optimization and technique for order of preference by similarity to ideal solution algorithms in the vehicular ad hoc networks","authors":"Behbod Kheradmand, A. Ghaffari, F. S. Gharehchopogh, Mohammad Masdari","doi":"10.1002/cpe.7209","DOIUrl":"https://doi.org/10.1002/cpe.7209","url":null,"abstract":"In a vehicular ad‐hoc network (VANET), each vehicle is equipped with an on‐board unit to communicate vehicle to vehicle or vehicle to fixed infrastructure. VANET technology is offered to provide many facilities to passengers and drivers, including safety, entertainment, mobile commerce, driver assistance, and emergency alarms. VANET has unique features such as high‐speed node mobility and network topology dynamics. These special features cause many problems such as increased transmission delays and packet loss. On the other hand, providing a good routing plan for VANET is a critical issue. Therefore, this article proposes a cluster‐based routing using in‐vehicle meta‐heuristic algorithms (CRMHA‐VANET) which has two phases. In the first stage, the vehicles are clustered and the most suitable cluster head (CH) is selected using the gray wolf optimization algorithm (GWO). In the next step, the next suitable CH is selected for data transmission in direct paths using the technique for order of preference by similarity to ideal solution (TOPSIS). The performance of the proposed method is analyzed through several criteria such as package delivery rate, end‐to‐end delay and throughput. CRMHA‐VANET results in a 10% to 25% improvement over all performance metrics, that is, packet delivery rate, latency, and throughput, over CRBP (clustering routing based on PSO [particle swarm optimization]), WCV (weight based clustering for VANET), and AODV‐CD methods.","PeriodicalId":10584,"journal":{"name":"Concurrency and Computation: Practice and Experience","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85334372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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