By coordination and cooperation between multi-agents, this paper proposes the network of intelligent agents which can reduce the search time needed to finding a parking place. Based on multi-agent model, the fined sol...By coordination and cooperation between multi-agents, this paper proposes the network of intelligent agents which can reduce the search time needed to finding a parking place. Based on multi-agent model, the fined solution is designed to help drivers in finding a parking space at anytime and anywhere. Three services are offered: the search for a vacant place, directions to a parking space and booking a place for parking. The results of this study generated by the platform MATSim transport simulation, show that our approach optimizes the operation of vehicles in a parking need with the aim of reducing congestion, and improve traffic flow in urban area. A comparison between the first method where the vehicles are random and the second method where vehicles are steered to vacant parking spaces shows that the minimization of time looking for a parking space could improve circulation by reducing the number of cars in the morning of 2% and 0.7% of the evening. In addition, the traffic per hour per day was reduced by approximately 4.17%.展开更多
The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cann...The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cannot be ignored.To address this issue,we firstly construct the models of DT model training and model poisoning attacks.An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay.Then,the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm(MASTER)based on DT-assisted state information evaluation and attack detection.MASTER adopts multi-timescale deep Q-learning(DQN)networks to jointly schedule local training epochs and devices.It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness.Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.展开更多
In order to solve the problem that the drivers can't find the optimal parking lot timely,a reservation based optimal parking lot recommendation model in Internet of Vehicle(IoV) environment is designed.Based on th...In order to solve the problem that the drivers can't find the optimal parking lot timely,a reservation based optimal parking lot recommendation model in Internet of Vehicle(IoV) environment is designed.Based on the users oriented parking information recommendation system,the model considers subjective demands of drivers comprehensively,makes a deeply analysis of the evaluation indicators.This recommendation model uses a phased selection method to calculate the optimal objective parking lot.The first stage is screening which based on the users' subjective parking demands;the second stage is processing the candidate parking lots through multiple attribute decision making.Simulation experiments show that this model can effectively solve the problems encountered in the process of finding optimal parking lot,save the driver's parking time and parking costs and also improve the overall utilization of parking facilities to ease the traffic congestion caused by vehicles parked patrol.展开更多
文摘By coordination and cooperation between multi-agents, this paper proposes the network of intelligent agents which can reduce the search time needed to finding a parking place. Based on multi-agent model, the fined solution is designed to help drivers in finding a parking space at anytime and anywhere. Three services are offered: the search for a vacant place, directions to a parking space and booking a place for parking. The results of this study generated by the platform MATSim transport simulation, show that our approach optimizes the operation of vehicles in a parking need with the aim of reducing congestion, and improve traffic flow in urban area. A comparison between the first method where the vehicles are random and the second method where vehicles are steered to vacant parking spaces shows that the minimization of time looking for a parking space could improve circulation by reducing the number of cars in the morning of 2% and 0.7% of the evening. In addition, the traffic per hour per day was reduced by approximately 4.17%.
基金supported by the Science and Technology Project of State Grid Corporation of China under Grant Number 52094021N010 (5400-202199534A-05-ZN)。
文摘The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cannot be ignored.To address this issue,we firstly construct the models of DT model training and model poisoning attacks.An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay.Then,the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm(MASTER)based on DT-assisted state information evaluation and attack detection.MASTER adopts multi-timescale deep Q-learning(DQN)networks to jointly schedule local training epochs and devices.It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness.Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.
基金partially supported by the National Natural Science Foundation of China under Grants No.60903176the Provincial Natural Science Foundation of Shandong under Grants No.ZR2012FM010,No.ZR2010FQ028+1 种基金the Program for Youth science and technology starfund of Jinan No.TNK1108the Sub-Project of the National Key Technology R&D Program No.2012BAF12B07-3
文摘In order to solve the problem that the drivers can't find the optimal parking lot timely,a reservation based optimal parking lot recommendation model in Internet of Vehicle(IoV) environment is designed.Based on the users oriented parking information recommendation system,the model considers subjective demands of drivers comprehensively,makes a deeply analysis of the evaluation indicators.This recommendation model uses a phased selection method to calculate the optimal objective parking lot.The first stage is screening which based on the users' subjective parking demands;the second stage is processing the candidate parking lots through multiple attribute decision making.Simulation experiments show that this model can effectively solve the problems encountered in the process of finding optimal parking lot,save the driver's parking time and parking costs and also improve the overall utilization of parking facilities to ease the traffic congestion caused by vehicles parked patrol.
