Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be dep...Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.展开更多
Multinational power grid interconnections play a critical part in supporting the vision of global energy internet.During the early stages of the Global Energy Internet,the value proposition of multinational interconne...Multinational power grid interconnections play a critical part in supporting the vision of global energy internet.During the early stages of the Global Energy Internet,the value proposition of multinational interconnections should be carefully investigated in order to stimulate the activities for associated countries in such potential interconnections.This paper proposes a new e conomic benefit evaluation model which is quantified by using a chronological production cost simulation approach.The economic benefit model comprehensively considered investment costs and the benefits of the decrease of load payments and the increase of net generation revenue due to a transmission project interconnected with different countries.This economic benefit model can assist to quantitatively determine the optimal transmission capacity for multinational interconnections to achieve maximum economic benefits as a whole.In the case study,the economic benefit of an interconnected system of western China and the Gulf States is assessed by using the method proposed in this paper.And the optimal interconnection capacity with maximum benefit is achieved.The case study shows that the proposed method can be used for economic benefit assessment and is of great significance to the multinational and intercontinental transmission interconnections.展开更多
Smart grids are increasingly dependent on data with the rapid development of communication and measurement.As one of the important data sources of smart grids,phasor measurement unit(PMU)is facing the high risk from a...Smart grids are increasingly dependent on data with the rapid development of communication and measurement.As one of the important data sources of smart grids,phasor measurement unit(PMU)is facing the high risk from attacks.Compared with cyber attacks,global position system(GPS)spoofing attacks(GSAs)are easier to implement because they can be exploited by portable devices,without the need to access the physical system.Therefore,this paper proposes a novel method for pattern recognition of GSA and an additional function of the proposed method is the data correction to the phase angle difference(PAD)deviation.Specifically,this paper analyzes the effect of GSA on PMU measurement and gives two common patterns of GSA,i.e.,the step attack and the ramp attack.Then,the method of estimating the PAD deviation across a transmission line introduced by GSA is proposed,which does not require the line parameters.After obtaining the estimated PAD deviations,the pattern of GSA can be recognized by hypothesis tests and correlation coefficients according to the statistical characteristics of the estimated PAD deviations.Finally,with the case studies,the effectiveness of the proposed method is demonstrated,and the success rate of the pattern recognition and the online performance of the proposed method are analyzed.展开更多
基金supported by the Innovation Fund Project of Jiangxi Normal University(YJS2022065)the Domestic Visiting Program of Jiangxi Normal University.
文摘Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.
基金This work was supported by the Science and Technology Project of State Grid Cooperation of China(No.0501000082).
文摘Multinational power grid interconnections play a critical part in supporting the vision of global energy internet.During the early stages of the Global Energy Internet,the value proposition of multinational interconnections should be carefully investigated in order to stimulate the activities for associated countries in such potential interconnections.This paper proposes a new e conomic benefit evaluation model which is quantified by using a chronological production cost simulation approach.The economic benefit model comprehensively considered investment costs and the benefits of the decrease of load payments and the increase of net generation revenue due to a transmission project interconnected with different countries.This economic benefit model can assist to quantitatively determine the optimal transmission capacity for multinational interconnections to achieve maximum economic benefits as a whole.In the case study,the economic benefit of an interconnected system of western China and the Gulf States is assessed by using the method proposed in this paper.And the optimal interconnection capacity with maximum benefit is achieved.The case study shows that the proposed method can be used for economic benefit assessment and is of great significance to the multinational and intercontinental transmission interconnections.
基金supported by the National Key Research and Development Program of China(No.2017YFB0902900,No.2017YFB0902901)National Natural Science Foundation of China(No.51627811,No.51725702)the Fundamental Research Funds for the Central Universities(No.2018ZD01)
文摘Smart grids are increasingly dependent on data with the rapid development of communication and measurement.As one of the important data sources of smart grids,phasor measurement unit(PMU)is facing the high risk from attacks.Compared with cyber attacks,global position system(GPS)spoofing attacks(GSAs)are easier to implement because they can be exploited by portable devices,without the need to access the physical system.Therefore,this paper proposes a novel method for pattern recognition of GSA and an additional function of the proposed method is the data correction to the phase angle difference(PAD)deviation.Specifically,this paper analyzes the effect of GSA on PMU measurement and gives two common patterns of GSA,i.e.,the step attack and the ramp attack.Then,the method of estimating the PAD deviation across a transmission line introduced by GSA is proposed,which does not require the line parameters.After obtaining the estimated PAD deviations,the pattern of GSA can be recognized by hypothesis tests and correlation coefficients according to the statistical characteristics of the estimated PAD deviations.Finally,with the case studies,the effectiveness of the proposed method is demonstrated,and the success rate of the pattern recognition and the online performance of the proposed method are analyzed.
