As big data,Artificial Intelligence,and Vehicle-to-Everything(V2X)communication have advanced,Intelligent Transportation Systems(ITS)are being developed to enable efficient and safe transportation systems.Electronic T...As big data,Artificial Intelligence,and Vehicle-to-Everything(V2X)communication have advanced,Intelligent Transportation Systems(ITS)are being developed to enable efficient and safe transportation systems.Electronic Toll Collection(ETC),which is one of the services included in ITS systems,is an automated system that allows vehicles to pass through toll plazas without stopping for manual payment.The ETC system is widely deployed on highways due to its contribution to stabilizing the overall traffic system flow.To ensure secure and efficient toll payments,designing a distributed model for sharing toll payment information among untrusted toll service providers is necessary.However,the current ETC system operates under a centralized model.Additionally,both toll service providers and toll plazas know the toll usage history of vehicles.It raises concerns about revealing the entire driving routes and patterns of vehicles.To address these issues,blockchain technology,suitable for secure data management and data sharing in distributed systems,is being applied to the ETC system.Blockchain enables efficient and transparent management of ETC information.Nevertheless,the public nature of blockchain poses a challenge where users’usage records are exposed to all participants.To tackle this,we propose a blockchain-based toll ticket model named AnonymousTollPass that considers the privacy of vehicles.The proposed model utilizes traceable ring signatures to provide unlinkability between tickets used by a vehicle and prevent the identity of the vehicle using the ticket from being identified among the ring members for the ticket.Furthermore,malicious vehicles’identities can be traced when they attempt to reuse tickets.By conducting simulations,we show the effectiveness of the proposed model and demonstrate that gas fees required for executing the proposed smart contracts are only 10%(when the ring size is 50)of the fees required in previous studies.展开更多
The software engineering field has long focused on creating high-quality software despite limited resources.Detecting defects before the testing stage of software development can enable quality assurance engineers to ...The software engineering field has long focused on creating high-quality software despite limited resources.Detecting defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the modules.This approach can enhance the quality of the final product while lowering development costs.Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team.This process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and maintenance.This study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high accuracy.In the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty modules.The second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and Stacking.Finally,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning techniques.The experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse datasets.Five NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and CM1.According to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1A2C1095591).
文摘As big data,Artificial Intelligence,and Vehicle-to-Everything(V2X)communication have advanced,Intelligent Transportation Systems(ITS)are being developed to enable efficient and safe transportation systems.Electronic Toll Collection(ETC),which is one of the services included in ITS systems,is an automated system that allows vehicles to pass through toll plazas without stopping for manual payment.The ETC system is widely deployed on highways due to its contribution to stabilizing the overall traffic system flow.To ensure secure and efficient toll payments,designing a distributed model for sharing toll payment information among untrusted toll service providers is necessary.However,the current ETC system operates under a centralized model.Additionally,both toll service providers and toll plazas know the toll usage history of vehicles.It raises concerns about revealing the entire driving routes and patterns of vehicles.To address these issues,blockchain technology,suitable for secure data management and data sharing in distributed systems,is being applied to the ETC system.Blockchain enables efficient and transparent management of ETC information.Nevertheless,the public nature of blockchain poses a challenge where users’usage records are exposed to all participants.To tackle this,we propose a blockchain-based toll ticket model named AnonymousTollPass that considers the privacy of vehicles.The proposed model utilizes traceable ring signatures to provide unlinkability between tickets used by a vehicle and prevent the identity of the vehicle using the ticket from being identified among the ring members for the ticket.Furthermore,malicious vehicles’identities can be traced when they attempt to reuse tickets.By conducting simulations,we show the effectiveness of the proposed model and demonstrate that gas fees required for executing the proposed smart contracts are only 10%(when the ring size is 50)of the fees required in previous studies.
基金supported by the Center for Cyber-Physical Systems,Khalifa University,under Grant 8474000137-RC1-C2PS-T5.
文摘The software engineering field has long focused on creating high-quality software despite limited resources.Detecting defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the modules.This approach can enhance the quality of the final product while lowering development costs.Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team.This process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and maintenance.This study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high accuracy.In the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty modules.The second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and Stacking.Finally,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning techniques.The experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse datasets.Five NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and CM1.According to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.
