The development of the Internet of Things(IoT)has brought great convenience to people.However,some information security problems such as privacy leakage are caused by communicating with risky users.It is a challenge t...The development of the Internet of Things(IoT)has brought great convenience to people.However,some information security problems such as privacy leakage are caused by communicating with risky users.It is a challenge to choose reliable users with which to interact in the IoT.Therefore,trust plays a crucial role in the IoT because trust may avoid some risks.Agents usually choose reliable users with high trust to maximize their own interests based on reinforcement learning.However,trust propagation is time-consuming,and trust changes with the interaction process in social networks.To track the dynamic changes in trust values,a dynamic trust inference algorithm named Dynamic Double DQN Trust(Dy-DDQNTrust)is proposed to predict the indirect trust values of two users without direct contact with each other.The proposed algorithm simulates the interactions among users by double DQN.Firstly,CurrentNet and TargetNet networks are used to select users for interaction.The users with high trust are chosen to interact in future iterations.Secondly,the trust value is updated dynamically until a reliable trust path is found according to the result of the interaction.Finally,the trust value between indirect users is inferred by aggregating the opinions from multiple users through a Modified Collaborative Filtering Averagebased Similarity(SMCFAvg)aggregation strategy.Experiments are carried out on the FilmTrust and the Epinions datasets.Compared with TidalTrust,MoleTrust,DDQNTrust,DyTrust and Dynamic Weighted Heuristic trust path Search algorithm(DWHS),our dynamic trust inference algorithm has higher prediction accuracy and better scalability.展开更多
The autonomous driving aims at ensuring the vehicle to effectively sense the environment and use proper strategies to navigate the vehicle without the interventions of humans.Hence,there exist a prediction of the back...The autonomous driving aims at ensuring the vehicle to effectively sense the environment and use proper strategies to navigate the vehicle without the interventions of humans.Hence,there exist a prediction of the background scenes and that leads to discontinuity between the predicted and planned outputs.An optimal prediction engine is required that suitably reads the background objects and make optimal decisions.In this paper,the author(s)develop an autonomous model for vehicle driving using ensemble model for large Sport Utility Vehicles(SUVs)that uses three different modules involving(a)recognition model,(b)planning model and(c)prediction model.The study develops a direct realization method for an autonomous vehicle driving.The direct realization method is designed as a behavioral model that incorporates three different modules to ensure optimal autonomous driving.The behavioral model includes recognition,planning and prediction modules that regulates the input trajectory processing of input video datasets.A deep learning algorithm is used in the proposed approach that helps in the classification of known or unknown objects along the line of sight.This model is compared with conventional deep learning classifiers in terms of recall rate and root mean square error(RMSE)to estimate its efficacy.Simulation results on different traffic environment shows that the Ensemble Convolutional Network Reinforcement Learning(E-CNN-RL)offers increased accuracy of 95.45%,reduced RMSE and increased recall rate than existing Ensemble Convolutional Neural Networks(CNN)and Ensemble Stacked CNN.展开更多
基金supported by the National Natural Science Foundation of China(62072392)the National Natural Science Foundation of China(61972360)the Major Scientific and Technological Innovation Projects of Shandong Province(2019522Y020131).
文摘The development of the Internet of Things(IoT)has brought great convenience to people.However,some information security problems such as privacy leakage are caused by communicating with risky users.It is a challenge to choose reliable users with which to interact in the IoT.Therefore,trust plays a crucial role in the IoT because trust may avoid some risks.Agents usually choose reliable users with high trust to maximize their own interests based on reinforcement learning.However,trust propagation is time-consuming,and trust changes with the interaction process in social networks.To track the dynamic changes in trust values,a dynamic trust inference algorithm named Dynamic Double DQN Trust(Dy-DDQNTrust)is proposed to predict the indirect trust values of two users without direct contact with each other.The proposed algorithm simulates the interactions among users by double DQN.Firstly,CurrentNet and TargetNet networks are used to select users for interaction.The users with high trust are chosen to interact in future iterations.Secondly,the trust value is updated dynamically until a reliable trust path is found according to the result of the interaction.Finally,the trust value between indirect users is inferred by aggregating the opinions from multiple users through a Modified Collaborative Filtering Averagebased Similarity(SMCFAvg)aggregation strategy.Experiments are carried out on the FilmTrust and the Epinions datasets.Compared with TidalTrust,MoleTrust,DDQNTrust,DyTrust and Dynamic Weighted Heuristic trust path Search algorithm(DWHS),our dynamic trust inference algorithm has higher prediction accuracy and better scalability.
文摘The autonomous driving aims at ensuring the vehicle to effectively sense the environment and use proper strategies to navigate the vehicle without the interventions of humans.Hence,there exist a prediction of the background scenes and that leads to discontinuity between the predicted and planned outputs.An optimal prediction engine is required that suitably reads the background objects and make optimal decisions.In this paper,the author(s)develop an autonomous model for vehicle driving using ensemble model for large Sport Utility Vehicles(SUVs)that uses three different modules involving(a)recognition model,(b)planning model and(c)prediction model.The study develops a direct realization method for an autonomous vehicle driving.The direct realization method is designed as a behavioral model that incorporates three different modules to ensure optimal autonomous driving.The behavioral model includes recognition,planning and prediction modules that regulates the input trajectory processing of input video datasets.A deep learning algorithm is used in the proposed approach that helps in the classification of known or unknown objects along the line of sight.This model is compared with conventional deep learning classifiers in terms of recall rate and root mean square error(RMSE)to estimate its efficacy.Simulation results on different traffic environment shows that the Ensemble Convolutional Network Reinforcement Learning(E-CNN-RL)offers increased accuracy of 95.45%,reduced RMSE and increased recall rate than existing Ensemble Convolutional Neural Networks(CNN)and Ensemble Stacked CNN.
