Recent advances in integrating Digital Twins(DTs)with Heterogeneous Vehicular Networks(HetVNets)enhance decision-making and improve network performance.Additionally,developments in Mobile Edge Computing(MEC)support th...Recent advances in integrating Digital Twins(DTs)with Heterogeneous Vehicular Networks(HetVNets)enhance decision-making and improve network performance.Additionally,developments in Mobile Edge Computing(MEC)support the computational demands of DTs.However,the decentralized nature of MEC systems introduces security challenges and traditional HetVNets fail to efficiently integrate diverse computing and network resources,limiting their ability to handle services for vehicles.This paper presents a novel service request offloading framework for DT-HetVNets to address these issues.In this framework,we design utility functions for vehicles and infrastructures to maximize satisfaction of their requirements through data synchronization and decision-making between DTs and entities.Furthermore,we propose a new honestly based distributed PoA(HDPoA)via scalable work.The interactions between infrastructures and vehicles are modeled as a multi-leader multi-follower(MLMF)game,and we develop a dynamic iterative algorithm to achieve the Nash equilibrium(NE)of the proposed game-theoretic model.Experimental results validate the effectiveness and accuracy of our scheme.展开更多
This paper investigates the problem of optimal secure control for networked control systems under hybrid attacks.A control strategy based on the Stackelberg game framework is proposed,which differs from conventional m...This paper investigates the problem of optimal secure control for networked control systems under hybrid attacks.A control strategy based on the Stackelberg game framework is proposed,which differs from conventional methods by considering both denial-of-service(DoS)and false data injection(FDI)attacks simultaneously.Additionally,the stability conditions for the system under these hybrid attacks are established.It is technically challenging to design the control strategy by predicting attacker actions based on Stcakelberg game to ensure the system stability under hybrid attacks.Another technical difficulty lies in establishing the conditions for mean-square asymptotic stability due to the complexity of the attack scenarios Finally,simulations on an unstable batch reactor system under hybrid attacks demonstrate the effectiveness of the proposed strategy.展开更多
The participation of wind farms in the former energymarket faces challenges such as power fluctuations and energy storage construction costs.To this end,this paper proposes a joint energy storage operation scheme for ...The participation of wind farms in the former energymarket faces challenges such as power fluctuations and energy storage construction costs.To this end,this paper proposes a joint energy storage operation scheme for multiple wind farms based on a leasingmodel,which assistswind farms in bidding for participation in the former energy market through leasing services,thereby enhancing energy storage efficiency and maximizing economic benefits.In this paper,based on theWeibull probability distribution to portray the uncertainty of wind power,and considering the lifetime capacity loss caused by charging and discharging of energy storage,we construct a bilateral transaction model aiming at maximizing the multi-objective revenue of wind farms and shared energy storage.The trading strategy is designed based on the Stackelberg game framework and solved jointly by the improved genetic algorithm and interior pointmethod.By exploring the effects of different lease price intervals on the overall systemperformance,and analyzing the systemstate undermultiple charging and discharging scenarios.The results show that a reasonable lease price range can significantly improve the energy storage system utilization and wind farmrevenue.Theprogramprovides new ideas to enhance the economic benefits of wind farms and promote the application of shared energy storage,and promotes the wide application of shared energy storage systems.展开更多
The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address th...The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address this critical challenge,this paper proposes a dynamic defense framework named Zero-day-aware Stackelberg Game-based Multi-Agent Distributed Deep Deterministic Policy Gradient(ZSG-MAD3PG).The framework integrates Stackelberg game modeling with the Multi-Agent Distributed Deep Deterministic Policy Gradient(MAD3PG)algorithm and incorporates defensive deception(DD)strategies to achieve adaptive and efficient protection.While conventional methods typically incur considerable resource overhead and exhibit higher latency due to static or rigid defensive mechanisms,the proposed ZSG-MAD3PG framework mitigates these limitations through multi-stage game modeling and adaptive learning,enabling more efficient resource utilization and faster response times.The Stackelberg-based architecture allows defenders to dynamically optimize packet sampling strategies,while attackers adjust their tactics to reach rapid equilibrium.Furthermore,dynamic deception techniques reduce the time required for the concealment of attacks and the overall system burden.A lightweight behavioral fingerprinting detection mechanism further enhances real-time zero-day attack identification within industrial device clusters.ZSG-MAD3PG demonstrates higher true positive rates(TPR)and lower false alarm rates(FAR)compared to existing methods,while also achieving improved latency,resource efficiency,and stealth adaptability in IIoT zero-day defense scenarios.展开更多
With increasing awareness of environmental protection and rising carbon emission costs,participation in electricity and carbon markets for energy-intensive industrial users will become an effective way to reduce opera...With increasing awareness of environmental protection and rising carbon emission costs,participation in electricity and carbon markets for energy-intensive industrial users will become an effective way to reduce operating costs and carbon emissions.In this regard,a novel Stackelberg game framework is developed in this study for coordinated participation in coupled electricity‒carbon markets.Specifically,generalized carbon emission models and electricity consumption models for different energy-intensive industrial users are established,and a Stackelberg game-based interactive operation strategy is proposed for load aggregators(LAs)and energy-intensive industrial users in joint electricity‒carbon markets,where the LA works as a leader who chooses proper interactive prices to maximize the comprehensive benefit,whereas energy-intensive industrial users serve as followers who minimize the total energy costs in response to the interactive prices set by the LA.Then,the existence and uniqueness of the Stackelberg equilibrium(SE)are analyzed,and a decentralized solution algorithm is suggested to reach the SE.Finally,the simulation results demonstrate that the proposed interactive operation strategy can not only increase the profit of the LA but also reduce the cost of energy-intensive industrial users,which achieves a win-win result.展开更多
UAV-assisted D2D networks can provide auxiliary communication for areas with poor communication facilities by using the characteristics of easy deployment of unmanned aerial vehicle(UAV),then it becomes a promising te...UAV-assisted D2D networks can provide auxiliary communication for areas with poor communication facilities by using the characteristics of easy deployment of unmanned aerial vehicle(UAV),then it becomes a promising technology.However,the coexistence of UAV and D2D aggravates the conflict of spectrum resources.In addition,when the UAV performs the communication service,it will inevitably cause the location change,which will make the original channel allocation no longer applicable.Inspired by the influence of frequent channel switching on channel allocation,we define the communication utility as a tradeoff between the throughput and channel switching cost.In the considered model,we investigate the multi-stage hierarchical spectrum access problem with maximizing aggregate communication utilities in UAV-assisted D2D networks.In particular,due to the hierarchical feature of the considered network,we adopt Stackelberg game to formulate this spectrum access problem where both the throughput and channel switching cost are considered.We prove that the proposed game has a stable Stackelberg equilibrium(SE),and the heterogeneous network based channel allocation(HN-CA)algorithm is proposed to achieve the desired solution.Simulation results verify the validity of the proposed game and show the effectiveness of the HN-CA algorithm.展开更多
To promote the utilization of renewable energy,such as photovoltaics,this paper proposes an optimal flexibility dispatch method for demand-side resources(DSR)based on the Stackelberg game theory.First,the concept of t...To promote the utilization of renewable energy,such as photovoltaics,this paper proposes an optimal flexibility dispatch method for demand-side resources(DSR)based on the Stackelberg game theory.First,the concept of the generalized DSR is analyzed and flexibility models for various DSR are constructed.Second,owing to the characteristics of small capacity but large-scale,an outer approximation is proposed to describe the aggregate flexibility of DSR.Then,the optimal flexibility dispatch model of DSR based on the Stackelberg game is established and a decentralized solution algorithm is designed to obtain the Stackelberg equilibrium.Finally,the actual data are utilized for the case study and the results show that,compared to the traditional centralized optimization method,the proposed optimal flexibility dispatch method can not only reduce the net load variability of the DSR aggregator but is beneficial for all DSR owners,which is more suitable for practical applications.展开更多
This paper mainly investigates the coordinated anti-jamming channel access problems in multiuser scenarios where there exists a tracking jammer who senses the spectrum and traces the channel with maximal receiving pow...This paper mainly investigates the coordinated anti-jamming channel access problems in multiuser scenarios where there exists a tracking jammer who senses the spectrum and traces the channel with maximal receiving power.To cope with the challenges brought by the tracking jammer,a multi-leader onefollower anti-jamming Stackelberg(MOAS)game is formulated,which is able to model the complex interactions between users and the tracking jammer.In the proposed game,users act as leaders,chose their channel access strategies and transmit firstly.The tracking jammer acts as the follower,whose objective is to find the optimal jamming strategy at each time slot.Besides,the existence of Stackelberg equilibriums(SEs)is proved,which means users reach Nash Equilibriums(NEs)for each jamming strategy while the jammer finds its best response jamming strategy for the current network access case.An active attraction based anti-jamming channel access(3ACA)algorithm is designed to reach SEs,where jammed users keep their channel access strategies unchanged to create access chances for other users.To enhance the fairness of the system,users will adjust their strategies and relearn after certain time slots to provide access chances for those users who sacrifice themselves to attract the tracking jammer.展开更多
The data traffic that is accumulated at the Macro Base Station(MBS)keeps on increasing as almost all the people start using mobile phones.The MBS cannot accommodate all user’s demands,and attempts to offload some use...The data traffic that is accumulated at the Macro Base Station(MBS)keeps on increasing as almost all the people start using mobile phones.The MBS cannot accommodate all user’s demands,and attempts to offload some users to the nearby small cells so that the user could get the expected service.For the MBS to offload data traffic to an Access Point(AP),it should offer an optimal economic incentive in a way its utility is maximized.Similarly,the APs should choose an optimal traffic to admit load for the price that it gets from MBS.To balance this tradeoff between the economic incentive and the admittance load to achieve optimal offloading,Software Defined Networking(SDN)assisted Stackelberg Game(SaSG)model is proposed.In this model,the MBS selects the users carefully to aggregate the service with AP,so that the user experiencing least service gets aggregated first.The MBS uses the Received Signal Strength Indicator(RSSI)value of the users as the main parameter for aggregating a particular user for a contract period with LTE and WiFi.Each player involved in the game tries to maximize their payoff utilities,and thus,while incorporating those utilities in real-time scenario,we obtain maximum throughput per user which experiences best data service without any lack in Quality of Experience(QoE).Thus,the proposed SaSG model proves better when compared with other game theory models,and hence an optimal data offloading is achieved.展开更多
This paper investigates a power control problem in a jamming system,where a separate smart jammer is deployed to ensure the communication security of the legal user.However,due to power leakage,the smart jammer may in...This paper investigates a power control problem in a jamming system,where a separate smart jammer is deployed to ensure the communication security of the legal user.However,due to power leakage,the smart jammer may incur unintentional interference to legal users.The key is how to suppress illegal communication while limit the negative impact on legal user.A jamming counter measure Stackelberg game is formulated to model the jamming power control dynamic of the system.The smart jammer acts as a leader to sense and interfere illegal communications of the illegal user,while the illegal user acts as a follower.In the game,the impact of uncertain channel information is taken into account.According to whether illegal user considers the uncertain channel information,we investigate two scenarios,namely,illegal user can obtain statistical distribution and accurate information of interference channel gain and its own cost,respectively.This work not only proposes a jamming counter measure iterative algorithm to update parameters,but also gives two solutions to obtain the Stackelberg equilibrium(SE).The power convergence behaviours under two scenarios are analyzed and compared.Additionally,brute force is used to verify the accuracy of the SE value further.展开更多
Aiming at the physical layer security(PLS)secure transmission existing in the information backhaul link of the satellite-UAV integrated(SUI)network,a two-layer Stackelberg game model(TSGM)that can resist full-duplex(F...Aiming at the physical layer security(PLS)secure transmission existing in the information backhaul link of the satellite-UAV integrated(SUI)network,a two-layer Stackelberg game model(TSGM)that can resist full-duplex(FD)eavesdropping and jamming attacks is proposed.The confrontation relationship between the UAV network and the attacker is established as the first layer Stackelberg game.The source UAV adjusts its own transmission power strategy according to the attacker’s jamming strategy to resist malicious jamming attacks.The internal competition and cooperation relationship in UAV network is modeled as the second layer Stackelberg game,and the optimal cooperative UAV transmits jamming signal to the attacker to resist malicious eavesdropping attacks.Aiming at the“selfishness”of UAV nodes,a price incentive mechanism is established to encourage UAV to actively participate in cooperation,so as to maximize the advantages of cooperative communication.For the proposed TSGM,we construct the utility function and analyze the closed equilibrium solution of the game model,and design a three-stage optimal response iterative(TORI)algorithm to solve the game equilibrium.The simulation results show that the proposed TSGM can effectively increase the utility of the source UAV and improve the enthusiasm of cooperation compared with other power control models.展开更多
The integration of photovoltaic,energy storage,direct current,and flexible load(PEDF)technologies in building power systems is an importantmeans to address the energy crisis and promote the development of green buildi...The integration of photovoltaic,energy storage,direct current,and flexible load(PEDF)technologies in building power systems is an importantmeans to address the energy crisis and promote the development of green buildings.The friendly interaction between the PEDF systems and the power grid can promote the utilization of renewable energy and enhance the stability of the power grid.For this purpose,this work introduces a framework of multiple incentive mechanisms for a PEDF park,a building energy system that implements PEDF technologies.The incentive mechanisms proposed in this paper include both economic and noneconomic aspects,which is the most significant innovation of this paper.By modeling the relationship between a PEDF park and the power grid into a Stackelberg game,we demonstrate the effectiveness of these incentive measures in promoting the friendly interaction between the two entities.In this game model,the power grid determines on the prices of electricity trading and incentive subsidy,aiming to maximize its revenue while reducing the peak load of the PEDF park.On the other hand,the PEDF park make its dispatch plan according to the prices established by the grid,in order to reduce electricity consumption expense,improve electricity utility,and enhance the penetration rate of renewable energy.The results show that the proposed incentive mechanisms for the PEDF park can help to optimize energy consumption and promote sustainable energy practices.展开更多
The purpose of adversarial deep learning is to train robust DNNs against adversarial attacks,and this is one of the major research focuses of deep learning.Game theory has been used to answer some of the basic questio...The purpose of adversarial deep learning is to train robust DNNs against adversarial attacks,and this is one of the major research focuses of deep learning.Game theory has been used to answer some of the basic questions about adversarial deep learning,such as those regarding the existence of a classifier with optimal robustness and the existence of optimal adversarial samples for a given class of classifiers.In most previous works,adversarial deep learning was formulated as a simultaneous game and the strategy spaces were assumed to be certain probability distributions in order for the Nash equilibrium to exist.However,this assumption is not applicable to practical situations.In this paper,we give answers to these basic questions for the practical case where the classifiers are DNNs with a given structure;we do that by formulating adversarial deep learning in the form of Stackelberg games.The existence of Stackelberg equilibria for these games is proven.Furthermore,it is shown that the equilibrium DNN has the largest adversarial accuracy among all DNNs with the same structure,when Carlini-Wagner s margin loss is used.The trade-off between robustness and accuracy in adversarial deep learning is also studied from a game theoretical perspective.展开更多
To strengthen border patrol measures, unmanned aerial vehicles(UAVs) are gradually used in many countries to detect illegal entries on borders. However, how to efficiently deploy limited UAVs to patrol on borders of l...To strengthen border patrol measures, unmanned aerial vehicles(UAVs) are gradually used in many countries to detect illegal entries on borders. However, how to efficiently deploy limited UAVs to patrol on borders of large areas remains challenging. In this paper, we first model the problem of deploying UAVs for border patrol as a Stackelberg game. Two players are considered in this game: The border patrol agency is the leader,who optimizes the patrol path of UAVs to detect the illegal immigrant. The illegal immigrant is the follower, who selects a certain area of the border to pass through at a certain time after observing the leader’s strategy. Second, a compact linear programming problem is proposed to tackle the exponential growth of the number of leader’s strategies. Third, a method is proposed to reduce the size of the strategy space of the follower. Then, we provide some theoretic results to present the effect of parameters of the model on leader’s utilities. Experimental results demonstrate the positive effect of limited starting and ending areas of UAV’s patrolling conditions and multiple patrolling altitudes on the leader ’s utility, and show that the proposed solution outperforms two conventional patrol strategies and has strong robustness.展开更多
Cold-chain logistics system(CCLS)plays the role of collecting and managing the logistics data of frozen food.However,there always exist problems of information loss,data tampering,and privacy leakage in traditional ce...Cold-chain logistics system(CCLS)plays the role of collecting and managing the logistics data of frozen food.However,there always exist problems of information loss,data tampering,and privacy leakage in traditional centralized systems,which influence frozen food security and people’s health.The centralized management form impedes the development of the cold-chain logistics industry and weakens logistics data availability.This paper first introduces a distributed CCLS based on blockchain technology to solve the centralized management problem.This system aggregates the production base,storage,transport,detection,processing,and consumer to form a cold-chain logistics union.The blockchain ledger guarantees that the logistics data cannot be tampered with and establishes a traceability mechanism for food safety incidents.Meanwhile,to improve the value of logistics data,a Stackelberg game-based resource allocation model has been proposed between the logistics data resource provider and the consumer.The competition between resource price and volume balances the resource supplement and consumption.This model can help to achieve an optimal resource price when the Stackelberg game obtains Nash equilibrium.The two participants also can maximize their revenues with the optimal resource price and volume by utilizing the backward induction method.Then,the performance evaluations of transaction throughput and latency show that the proposed distributed CCLS is more secure and stable.The simulations about the variation trend of data price and amount,optimal benefits,and total benefits comparison of different forms show that the resource allocation model is more efficient and practical.Moreover,the blockchain-based CCLS and Stackelberg game-based resource allocation model also can promote the value of logistic data and improve social benefits.展开更多
According to the property-rights model of cognitive radio, primary users （PUs） who own the spectrum resource have the right to lease part of spectrum to secondary users （SUs） in exchange for appropriate profit. In...According to the property-rights model of cognitive radio, primary users （PUs） who own the spectrum resource have the right to lease part of spectrum to secondary users （SUs） in exchange for appropriate profit. In this paper, we propose a pricing-based spectrum leasing framework between one PU and multiple SUs. In this scenario, the PU attempts to maximize its utility by setting the price of spectrum. Then, the selected SUs have the right to decide their power levels to help PU s transmission, aiming to obtain corresponding access time. The spectrum leasing problem can be cast into a stackelberg game, where the PU plays the seller-level game and the selected SUs play the buyer-level game. Through analysis based on the backward induction, we prove that there exists a unique equilibrium in the stackelberg game with certain constraints. Numerical results show that the proposed pricing-based spectrum leasing framework is effective, and the performance of both PU and SUs is improved, compared to the traditional mechanism without cooperation.展开更多
The cognitive network has become a promising method to solve the spectrum resources shortage problem.Especially for the optimization of network slicing resources in the cognitive radio access network(RAN),we are inter...The cognitive network has become a promising method to solve the spectrum resources shortage problem.Especially for the optimization of network slicing resources in the cognitive radio access network(RAN),we are interested in the profit of the mobile virtual network operator(MVNO)and the utility of secondary users(SUs).In cognitive RAN,we aim to find the optimal scheme for the MVNO to efficiently allocate slice resources to SUs.Since the MVNO and SUs are selfish and the game between the MVNO and SUs is difficult to reach equilibrium,we consider modeling this scheme as a Stackelberg game.Leveraging mathematical programming with equilibrium constraints(MPEC)and Karush-Kuhn-Tucker(KKT)conditions,we can obtain a single-level optimization problem,and then prove that the problem is a convex optimization problem.The simulation results show that the proposed method is superior to the noncooperative game.While guaranteeing the Quality of Service(QoS)of primary users(PUs)and SUs,the proposed method can balance the profit of the MVNO and the utility of SUs.展开更多
This paper investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon(PB) delivers energy signals to wireless sensors to enable th...This paper investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon(PB) delivers energy signals to wireless sensors to enable their passive backscattering and active transmission to the access point(AP). We propose an efficient time scheduling scheme for network performance enhancement, based on which each sensor can always harvest energy from the PB over the entire block except its time slots allocated for passive and active information delivery. Considering the PB and wireless sensors are from two selfish service providers, we use the Stackelberg game to model the energy interaction among them. To address the non-convexity of the leader-level problem, we propose to decompose the original problem into two subproblems and solve them iteratively in an alternating manner. Specifically, the successive convex approximation, semi-definite relaxation(SDR) and variable substitution techniques are applied to find a nearoptimal solution. To evaluate the performance loss caused by the interaction between two providers, we further investigate the social welfare maximization problem. Numerical results demonstrate that compared to the benchmark schemes, the proposed scheme can achieve up to 35.4% and 38.7% utility gain for the leader and the follower, respectively.展开更多
基金supported by the National Natural Science Foundation of China(No 62371250)the Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu(No BK20212001)the Jiangsu Natural Science Foundation for Distinguished Young Scholars(No BK20220054).
文摘Recent advances in integrating Digital Twins(DTs)with Heterogeneous Vehicular Networks(HetVNets)enhance decision-making and improve network performance.Additionally,developments in Mobile Edge Computing(MEC)support the computational demands of DTs.However,the decentralized nature of MEC systems introduces security challenges and traditional HetVNets fail to efficiently integrate diverse computing and network resources,limiting their ability to handle services for vehicles.This paper presents a novel service request offloading framework for DT-HetVNets to address these issues.In this framework,we design utility functions for vehicles and infrastructures to maximize satisfaction of their requirements through data synchronization and decision-making between DTs and entities.Furthermore,we propose a new honestly based distributed PoA(HDPoA)via scalable work.The interactions between infrastructures and vehicles are modeled as a multi-leader multi-follower(MLMF)game,and we develop a dynamic iterative algorithm to achieve the Nash equilibrium(NE)of the proposed game-theoretic model.Experimental results validate the effectiveness and accuracy of our scheme.
基金supported in part by Shanghai Rising-Star Program,China under grant 22QA1409400in part by National Natural Science Foundation of China under grant 62473287 and 62088101in part by Shanghai Municipal Science and Technology Major Project under grant 2021SHZDZX0100.
文摘This paper investigates the problem of optimal secure control for networked control systems under hybrid attacks.A control strategy based on the Stackelberg game framework is proposed,which differs from conventional methods by considering both denial-of-service(DoS)and false data injection(FDI)attacks simultaneously.Additionally,the stability conditions for the system under these hybrid attacks are established.It is technically challenging to design the control strategy by predicting attacker actions based on Stcakelberg game to ensure the system stability under hybrid attacks.Another technical difficulty lies in establishing the conditions for mean-square asymptotic stability due to the complexity of the attack scenarios Finally,simulations on an unstable batch reactor system under hybrid attacks demonstrate the effectiveness of the proposed strategy.
基金supported by the Technology Project of the State Grid Corporation Headquarters(Project No.4000-202399368A-2-2-ZB).
文摘The participation of wind farms in the former energymarket faces challenges such as power fluctuations and energy storage construction costs.To this end,this paper proposes a joint energy storage operation scheme for multiple wind farms based on a leasingmodel,which assistswind farms in bidding for participation in the former energy market through leasing services,thereby enhancing energy storage efficiency and maximizing economic benefits.In this paper,based on theWeibull probability distribution to portray the uncertainty of wind power,and considering the lifetime capacity loss caused by charging and discharging of energy storage,we construct a bilateral transaction model aiming at maximizing the multi-objective revenue of wind farms and shared energy storage.The trading strategy is designed based on the Stackelberg game framework and solved jointly by the improved genetic algorithm and interior pointmethod.By exploring the effects of different lease price intervals on the overall systemperformance,and analyzing the systemstate undermultiple charging and discharging scenarios.The results show that a reasonable lease price range can significantly improve the energy storage system utilization and wind farmrevenue.Theprogramprovides new ideas to enhance the economic benefits of wind farms and promote the application of shared energy storage,and promotes the wide application of shared energy storage systems.
基金funded in part by the Humanities and Social Sciences Planning Foundation of Ministry of Education of China under Grant No.24YJAZH123National Undergraduate Innovation and Entrepreneurship Training Program of China under Grant No.202510347069the Huzhou Science and Technology Planning Foundation under Grant No.2023GZ04.
文摘The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address this critical challenge,this paper proposes a dynamic defense framework named Zero-day-aware Stackelberg Game-based Multi-Agent Distributed Deep Deterministic Policy Gradient(ZSG-MAD3PG).The framework integrates Stackelberg game modeling with the Multi-Agent Distributed Deep Deterministic Policy Gradient(MAD3PG)algorithm and incorporates defensive deception(DD)strategies to achieve adaptive and efficient protection.While conventional methods typically incur considerable resource overhead and exhibit higher latency due to static or rigid defensive mechanisms,the proposed ZSG-MAD3PG framework mitigates these limitations through multi-stage game modeling and adaptive learning,enabling more efficient resource utilization and faster response times.The Stackelberg-based architecture allows defenders to dynamically optimize packet sampling strategies,while attackers adjust their tactics to reach rapid equilibrium.Furthermore,dynamic deception techniques reduce the time required for the concealment of attacks and the overall system burden.A lightweight behavioral fingerprinting detection mechanism further enhances real-time zero-day attack identification within industrial device clusters.ZSG-MAD3PG demonstrates higher true positive rates(TPR)and lower false alarm rates(FAR)compared to existing methods,while also achieving improved latency,resource efficiency,and stealth adaptability in IIoT zero-day defense scenarios.
基金grateful for the financial support from the National Key R&D Program of China(2023YFB2407300).
文摘With increasing awareness of environmental protection and rising carbon emission costs,participation in electricity and carbon markets for energy-intensive industrial users will become an effective way to reduce operating costs and carbon emissions.In this regard,a novel Stackelberg game framework is developed in this study for coordinated participation in coupled electricity‒carbon markets.Specifically,generalized carbon emission models and electricity consumption models for different energy-intensive industrial users are established,and a Stackelberg game-based interactive operation strategy is proposed for load aggregators(LAs)and energy-intensive industrial users in joint electricity‒carbon markets,where the LA works as a leader who chooses proper interactive prices to maximize the comprehensive benefit,whereas energy-intensive industrial users serve as followers who minimize the total energy costs in response to the interactive prices set by the LA.Then,the existence and uniqueness of the Stackelberg equilibrium(SE)are analyzed,and a decentralized solution algorithm is suggested to reach the SE.Finally,the simulation results demonstrate that the proposed interactive operation strategy can not only increase the profit of the LA but also reduce the cost of energy-intensive industrial users,which achieves a win-win result.
基金This work is supported by the Jiangsu Provincial Natural Science Fund for Outstanding Young Scholars(No.BK20180028)the Natural Science Foundations of China(No.61671474)+1 种基金the Jiangsu Provincial Natural Science Fund for Excellent Young Scholars(No.BK20170089)and in part by Postgraduate Research and Practice Innovation Program of Jiangsu Province under No.KYCX190188.
文摘UAV-assisted D2D networks can provide auxiliary communication for areas with poor communication facilities by using the characteristics of easy deployment of unmanned aerial vehicle(UAV),then it becomes a promising technology.However,the coexistence of UAV and D2D aggravates the conflict of spectrum resources.In addition,when the UAV performs the communication service,it will inevitably cause the location change,which will make the original channel allocation no longer applicable.Inspired by the influence of frequent channel switching on channel allocation,we define the communication utility as a tradeoff between the throughput and channel switching cost.In the considered model,we investigate the multi-stage hierarchical spectrum access problem with maximizing aggregate communication utilities in UAV-assisted D2D networks.In particular,due to the hierarchical feature of the considered network,we adopt Stackelberg game to formulate this spectrum access problem where both the throughput and channel switching cost are considered.We prove that the proposed game has a stable Stackelberg equilibrium(SE),and the heterogeneous network based channel allocation(HN-CA)algorithm is proposed to achieve the desired solution.Simulation results verify the validity of the proposed game and show the effectiveness of the HN-CA algorithm.
基金supported by Science and Technology Project of State Grid Hebei Electric Power Company(SGHE0000DKJS2000228)
文摘To promote the utilization of renewable energy,such as photovoltaics,this paper proposes an optimal flexibility dispatch method for demand-side resources(DSR)based on the Stackelberg game theory.First,the concept of the generalized DSR is analyzed and flexibility models for various DSR are constructed.Second,owing to the characteristics of small capacity but large-scale,an outer approximation is proposed to describe the aggregate flexibility of DSR.Then,the optimal flexibility dispatch model of DSR based on the Stackelberg game is established and a decentralized solution algorithm is designed to obtain the Stackelberg equilibrium.Finally,the actual data are utilized for the case study and the results show that,compared to the traditional centralized optimization method,the proposed optimal flexibility dispatch method can not only reduce the net load variability of the DSR aggregator but is beneficial for all DSR owners,which is more suitable for practical applications.
文摘This paper mainly investigates the coordinated anti-jamming channel access problems in multiuser scenarios where there exists a tracking jammer who senses the spectrum and traces the channel with maximal receiving power.To cope with the challenges brought by the tracking jammer,a multi-leader onefollower anti-jamming Stackelberg(MOAS)game is formulated,which is able to model the complex interactions between users and the tracking jammer.In the proposed game,users act as leaders,chose their channel access strategies and transmit firstly.The tracking jammer acts as the follower,whose objective is to find the optimal jamming strategy at each time slot.Besides,the existence of Stackelberg equilibriums(SEs)is proved,which means users reach Nash Equilibriums(NEs)for each jamming strategy while the jammer finds its best response jamming strategy for the current network access case.An active attraction based anti-jamming channel access(3ACA)algorithm is designed to reach SEs,where jammed users keep their channel access strategies unchanged to create access chances for other users.To enhance the fairness of the system,users will adjust their strategies and relearn after certain time slots to provide access chances for those users who sacrifice themselves to attract the tracking jammer.
文摘The data traffic that is accumulated at the Macro Base Station(MBS)keeps on increasing as almost all the people start using mobile phones.The MBS cannot accommodate all user’s demands,and attempts to offload some users to the nearby small cells so that the user could get the expected service.For the MBS to offload data traffic to an Access Point(AP),it should offer an optimal economic incentive in a way its utility is maximized.Similarly,the APs should choose an optimal traffic to admit load for the price that it gets from MBS.To balance this tradeoff between the economic incentive and the admittance load to achieve optimal offloading,Software Defined Networking(SDN)assisted Stackelberg Game(SaSG)model is proposed.In this model,the MBS selects the users carefully to aggregate the service with AP,so that the user experiencing least service gets aggregated first.The MBS uses the Received Signal Strength Indicator(RSSI)value of the users as the main parameter for aggregating a particular user for a contract period with LTE and WiFi.Each player involved in the game tries to maximize their payoff utilities,and thus,while incorporating those utilities in real-time scenario,we obtain maximum throughput per user which experiences best data service without any lack in Quality of Experience(QoE).Thus,the proposed SaSG model proves better when compared with other game theory models,and hence an optimal data offloading is achieved.
基金supported in part by National Key R&D Program of China under Grant 2018YFB1800800by National NSF of China under Grant 61601490,61801218,61827801,61631020+3 种基金by the open research fund of Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space(Nanjing Univ.Aeronaut.Astronaut.)(No.KF20181913)in part by State Key Laboratory of Air Traffic Management System and Technology under SKLATM201808in part by the Natural Science Foundation of Jiangsu Province under Grant BK20180420,BK20180424by the Open Foundation for Graduate Innovation of NUAA(Grant NO.kfjj20190417)。
文摘This paper investigates a power control problem in a jamming system,where a separate smart jammer is deployed to ensure the communication security of the legal user.However,due to power leakage,the smart jammer may incur unintentional interference to legal users.The key is how to suppress illegal communication while limit the negative impact on legal user.A jamming counter measure Stackelberg game is formulated to model the jamming power control dynamic of the system.The smart jammer acts as a leader to sense and interfere illegal communications of the illegal user,while the illegal user acts as a follower.In the game,the impact of uncertain channel information is taken into account.According to whether illegal user considers the uncertain channel information,we investigate two scenarios,namely,illegal user can obtain statistical distribution and accurate information of interference channel gain and its own cost,respectively.This work not only proposes a jamming counter measure iterative algorithm to update parameters,but also gives two solutions to obtain the Stackelberg equilibrium(SE).The power convergence behaviours under two scenarios are analyzed and compared.Additionally,brute force is used to verify the accuracy of the SE value further.
基金supported in part by the National Natural Science Foundation of China under Grant 62071485, Grant 61901519, Grant 62001513in part by the Basic Research Project of Jiangsu Province under Grant BK 20192002the Natural Science Foundation of Jiangsu Province under Grant BK20201334, and BK20200579
文摘Aiming at the physical layer security(PLS)secure transmission existing in the information backhaul link of the satellite-UAV integrated(SUI)network,a two-layer Stackelberg game model(TSGM)that can resist full-duplex(FD)eavesdropping and jamming attacks is proposed.The confrontation relationship between the UAV network and the attacker is established as the first layer Stackelberg game.The source UAV adjusts its own transmission power strategy according to the attacker’s jamming strategy to resist malicious jamming attacks.The internal competition and cooperation relationship in UAV network is modeled as the second layer Stackelberg game,and the optimal cooperative UAV transmits jamming signal to the attacker to resist malicious eavesdropping attacks.Aiming at the“selfishness”of UAV nodes,a price incentive mechanism is established to encourage UAV to actively participate in cooperation,so as to maximize the advantages of cooperative communication.For the proposed TSGM,we construct the utility function and analyze the closed equilibrium solution of the game model,and design a three-stage optimal response iterative(TORI)algorithm to solve the game equilibrium.The simulation results show that the proposed TSGM can effectively increase the utility of the source UAV and improve the enthusiasm of cooperation compared with other power control models.
基金supported by Guangxi Power Grid Science and Technology Project(GXKJXM20222069).
文摘The integration of photovoltaic,energy storage,direct current,and flexible load(PEDF)technologies in building power systems is an importantmeans to address the energy crisis and promote the development of green buildings.The friendly interaction between the PEDF systems and the power grid can promote the utilization of renewable energy and enhance the stability of the power grid.For this purpose,this work introduces a framework of multiple incentive mechanisms for a PEDF park,a building energy system that implements PEDF technologies.The incentive mechanisms proposed in this paper include both economic and noneconomic aspects,which is the most significant innovation of this paper.By modeling the relationship between a PEDF park and the power grid into a Stackelberg game,we demonstrate the effectiveness of these incentive measures in promoting the friendly interaction between the two entities.In this game model,the power grid determines on the prices of electricity trading and incentive subsidy,aiming to maximize its revenue while reducing the peak load of the PEDF park.On the other hand,the PEDF park make its dispatch plan according to the prices established by the grid,in order to reduce electricity consumption expense,improve electricity utility,and enhance the penetration rate of renewable energy.The results show that the proposed incentive mechanisms for the PEDF park can help to optimize energy consumption and promote sustainable energy practices.
基金This work was partially supported by NSFC(12288201)NKRDP grant(2018YFA0704705).
文摘The purpose of adversarial deep learning is to train robust DNNs against adversarial attacks,and this is one of the major research focuses of deep learning.Game theory has been used to answer some of the basic questions about adversarial deep learning,such as those regarding the existence of a classifier with optimal robustness and the existence of optimal adversarial samples for a given class of classifiers.In most previous works,adversarial deep learning was formulated as a simultaneous game and the strategy spaces were assumed to be certain probability distributions in order for the Nash equilibrium to exist.However,this assumption is not applicable to practical situations.In this paper,we give answers to these basic questions for the practical case where the classifiers are DNNs with a given structure;we do that by formulating adversarial deep learning in the form of Stackelberg games.The existence of Stackelberg equilibria for these games is proven.Furthermore,it is shown that the equilibrium DNN has the largest adversarial accuracy among all DNNs with the same structure,when Carlini-Wagner s margin loss is used.The trade-off between robustness and accuracy in adversarial deep learning is also studied from a game theoretical perspective.
基金supported by the National Natural Science Foundation of China (71971075,71871079)the National Key Research and Development Program of China (2019YFE0110300)+1 种基金the Anhui Provincial Natural Science Foundation (1808085MG213)the Fundamental R esearch Funds for the Central Universities (PA2019GDPK0082)。
文摘To strengthen border patrol measures, unmanned aerial vehicles(UAVs) are gradually used in many countries to detect illegal entries on borders. However, how to efficiently deploy limited UAVs to patrol on borders of large areas remains challenging. In this paper, we first model the problem of deploying UAVs for border patrol as a Stackelberg game. Two players are considered in this game: The border patrol agency is the leader,who optimizes the patrol path of UAVs to detect the illegal immigrant. The illegal immigrant is the follower, who selects a certain area of the border to pass through at a certain time after observing the leader’s strategy. Second, a compact linear programming problem is proposed to tackle the exponential growth of the number of leader’s strategies. Third, a method is proposed to reduce the size of the strategy space of the follower. Then, we provide some theoretic results to present the effect of parameters of the model on leader’s utilities. Experimental results demonstrate the positive effect of limited starting and ending areas of UAV’s patrolling conditions and multiple patrolling altitudes on the leader ’s utility, and show that the proposed solution outperforms two conventional patrol strategies and has strong robustness.
基金supported by the National Natural Science Foundation of China under Grant 92046001,61962009the Doctor Scientific Research Fund of Zhengzhou University of Light Industry underGrant 2021BSJJ033Key ScientificResearch Project of Colleges andUniversities in Henan Province(CN)under Grant No.22A413010.
文摘Cold-chain logistics system(CCLS)plays the role of collecting and managing the logistics data of frozen food.However,there always exist problems of information loss,data tampering,and privacy leakage in traditional centralized systems,which influence frozen food security and people’s health.The centralized management form impedes the development of the cold-chain logistics industry and weakens logistics data availability.This paper first introduces a distributed CCLS based on blockchain technology to solve the centralized management problem.This system aggregates the production base,storage,transport,detection,processing,and consumer to form a cold-chain logistics union.The blockchain ledger guarantees that the logistics data cannot be tampered with and establishes a traceability mechanism for food safety incidents.Meanwhile,to improve the value of logistics data,a Stackelberg game-based resource allocation model has been proposed between the logistics data resource provider and the consumer.The competition between resource price and volume balances the resource supplement and consumption.This model can help to achieve an optimal resource price when the Stackelberg game obtains Nash equilibrium.The two participants also can maximize their revenues with the optimal resource price and volume by utilizing the backward induction method.Then,the performance evaluations of transaction throughput and latency show that the proposed distributed CCLS is more secure and stable.The simulations about the variation trend of data price and amount,optimal benefits,and total benefits comparison of different forms show that the resource allocation model is more efficient and practical.Moreover,the blockchain-based CCLS and Stackelberg game-based resource allocation model also can promote the value of logistic data and improve social benefits.
基金supported by National Basic Research Program of China(973 Program)(No. 2010CB731800)Key Project of National Nature Science Foundation of China(No. 60934003),National Nature Science Foundation of China(Nos. 61104033, 61172095,61203104)Nature Science Foundation of Hebei Province(Nos. F2011203226, F2012203109, F2012203126)
文摘According to the property-rights model of cognitive radio, primary users （PUs） who own the spectrum resource have the right to lease part of spectrum to secondary users （SUs） in exchange for appropriate profit. In this paper, we propose a pricing-based spectrum leasing framework between one PU and multiple SUs. In this scenario, the PU attempts to maximize its utility by setting the price of spectrum. Then, the selected SUs have the right to decide their power levels to help PU s transmission, aiming to obtain corresponding access time. The spectrum leasing problem can be cast into a stackelberg game, where the PU plays the seller-level game and the selected SUs play the buyer-level game. Through analysis based on the backward induction, we prove that there exists a unique equilibrium in the stackelberg game with certain constraints. Numerical results show that the proposed pricing-based spectrum leasing framework is effective, and the performance of both PU and SUs is improved, compared to the traditional mechanism without cooperation.
基金This work was supported by National Natural Science Foundation of China(No.61971057).
文摘The cognitive network has become a promising method to solve the spectrum resources shortage problem.Especially for the optimization of network slicing resources in the cognitive radio access network(RAN),we are interested in the profit of the mobile virtual network operator(MVNO)and the utility of secondary users(SUs).In cognitive RAN,we aim to find the optimal scheme for the MVNO to efficiently allocate slice resources to SUs.Since the MVNO and SUs are selfish and the game between the MVNO and SUs is difficult to reach equilibrium,we consider modeling this scheme as a Stackelberg game.Leveraging mathematical programming with equilibrium constraints(MPEC)and Karush-Kuhn-Tucker(KKT)conditions,we can obtain a single-level optimization problem,and then prove that the problem is a convex optimization problem.The simulation results show that the proposed method is superior to the noncooperative game.While guaranteeing the Quality of Service(QoS)of primary users(PUs)and SUs,the proposed method can balance the profit of the MVNO and the utility of SUs.
基金supported by National Natural Science Foundation of China(No.61901229 and No.62071242)the Project of Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network(No.SDGC2234)+1 种基金the Open Research Project of Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials Sciences and Technology(No.NJUZDS2022-008)the Post-Doctoral Research Supporting Program of Jiangsu Province(No.SBH20).
文摘This paper investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon(PB) delivers energy signals to wireless sensors to enable their passive backscattering and active transmission to the access point(AP). We propose an efficient time scheduling scheme for network performance enhancement, based on which each sensor can always harvest energy from the PB over the entire block except its time slots allocated for passive and active information delivery. Considering the PB and wireless sensors are from two selfish service providers, we use the Stackelberg game to model the energy interaction among them. To address the non-convexity of the leader-level problem, we propose to decompose the original problem into two subproblems and solve them iteratively in an alternating manner. Specifically, the successive convex approximation, semi-definite relaxation(SDR) and variable substitution techniques are applied to find a nearoptimal solution. To evaluate the performance loss caused by the interaction between two providers, we further investigate the social welfare maximization problem. Numerical results demonstrate that compared to the benchmark schemes, the proposed scheme can achieve up to 35.4% and 38.7% utility gain for the leader and the follower, respectively.
