Cyber-Physical Networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV...Cyber-Physical Networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV).With the increasing popularity of digital photography and Internet technology,more and more users are sharing images on CPN.However,many images are shared without any privacy processing,exposing hidden privacy risks and making sensitive content easily accessible to Artificial Intelligence(AI)algorithms.Existing image sharing methods lack fine-grained image sharing policies and cannot protect user privacy.To address this issue,we propose a social relationship-driven privacy customization protection model for publishers and co-photographers.We construct a heterogeneous social information network centered on social relationships,introduce a user intimacy evaluation method with time decay,and evaluate privacy levels considering user interest similarity.To protect user privacy while maintaining image appreciation,we design a lightweight face-swapping algorithm based on Generative Adversarial Network(GAN)to swap faces that need to be protected.Our proposed method minimizes the loss of image utility while satisfying privacy requirements,as shown by extensive theoretical and simulation analyses.展开更多
In the upcoming B5G/6G era,Virtual Reality(VR)over wireless has become a typical application,which is an inevitable trend in the development of video.However,in immersive and interactive VR experiences,VR services typ...In the upcoming B5G/6G era,Virtual Reality(VR)over wireless has become a typical application,which is an inevitable trend in the development of video.However,in immersive and interactive VR experiences,VR services typically exhibit high delay,while simultaneously posing challenges for the energy consumption of local devices.To address these issues,this paper aims to improve the performance of VR service in the edge-terminal cooperative system.Specifically,we formulate a joint Caching,Computing,and Communication(3C)VR service policy problem by optimizing the weighted sum of the total VR delivery delay and the energy consumption of local devices.To design the optimal VR service policy,the optimization problem is decoupled into three independent subproblems to be solved separately.To improve the caching efficiency within the network,a Bert-based user interest analysis method is first proposed to accurately characterize the content request behavior.Based on this,a service cost minimum-maximization problem is formulated under the consideration of performance fairness among users.Then,the joint caching and computing scheme is derived for each user with a given allocation of communication resources while a bisection-based communication scheme is acquired with the given information on the joint caching and computing policy.With alternative optimization,an optimal policy for joint 3C based on user interest can be finally obtained.Simulation results are presented to demonstrate the superiority of the proposed user interest-aware caching scheme and the effectiveness of the joint 3C optimization policy while considering user fairness.Our code is available at https://github.com/mrfuqaq1108/Interest-Aware-Joint-3C-Optimization.展开更多
Vehicular Edge Computing(VEC)enhances the quality of user services by deploying wealth of resources near vehicles.However,due to highly dynamic and complex nature of vehicular networks,centralized decisionmaking for r...Vehicular Edge Computing(VEC)enhances the quality of user services by deploying wealth of resources near vehicles.However,due to highly dynamic and complex nature of vehicular networks,centralized decisionmaking for resource allocation proves inadequate within VECs.Conversely,allocating resources via distributed decision-making consumes vehicular resources.To improve the quality of user service,we formulate a problem of latency minimization,further subdividing this problem into two subproblems to be solved through distributed decision-making.To mitigate the resource consumption caused by distributed decision-making,we propose Reinforcement Learning(RL)algorithm based on sequential alternating multi-agent system mechanism,which effectively reduces the dimensionality of action space without losing the informational content of action,achieving network lightweighting.We discuss the rationality,generalizability,and inherent advantages of proposed mechanism.Simulation results indicate that our proposed mechanism outperforms traditional RL algorithms in terms of stability,generalizability,and adaptability to scenarios with invalid actions,all while achieving network lightweighting.展开更多
Aiming at the problem of mobile data traffic surge in 5G networks,this paper proposes an effective solution combining massive multiple-input multiple-output techniques with Ultra-Dense Network(UDN)and focuses on solvi...Aiming at the problem of mobile data traffic surge in 5G networks,this paper proposes an effective solution combining massive multiple-input multiple-output techniques with Ultra-Dense Network(UDN)and focuses on solving the resulting challenge of increased energy consumption.A base station control algorithm based on Multi-Agent Proximity Policy Optimization(MAPPO)is designed.In the constructed 5G UDN model,each base station is considered as an agent,and the MAPPO algorithm enables inter-base station collaboration and interference management to optimize the network performance.To reduce the extra power consumption due to frequent sleep mode switching of base stations,a sleep mode switching decision algorithm is proposed.The algorithm reduces unnecessary power consumption by evaluating the network state similarity and intelligently adjusting the agent’s action strategy.Simulation results show that the proposed algorithm reduces the power consumption by 24.61% compared to the no-sleep strategy and further reduces the power consumption by 5.36% compared to the traditional MAPPO algorithm under the premise of guaranteeing the quality of service of users.展开更多
To guarantee the heterogeneous delay requirements of the diverse vehicular services,it is necessary to design a full cooperative policy for both Vehicle to Infrastructure(V2I)and Vehicle to Vehicle(V2V)links.This pape...To guarantee the heterogeneous delay requirements of the diverse vehicular services,it is necessary to design a full cooperative policy for both Vehicle to Infrastructure(V2I)and Vehicle to Vehicle(V2V)links.This paper investigates the reduction of the delay in edge information sharing for V2V links while satisfying the delay requirements of the V2I links.Specifically,a mean delay minimization problem and a maximum individual delay minimization problem are formulated to improve the global network performance and ensure the fairness of a single user,respectively.A multi-agent reinforcement learning framework is designed to solve these two problems,where a new reward function is proposed to evaluate the utilities of the two optimization objectives in a unified framework.Thereafter,a proximal policy optimization approach is proposed to enable each V2V user to learn its policy using the shared global network reward.The effectiveness of the proposed approach is finally validated by comparing the obtained results with those of the other baseline approaches through extensive simulation experiments.展开更多
A huge amount of sensitive personal data is being collected by various online health monitoring applications.Although the data is anonymous,the personal trajectories(e.g.,the chronological access records of small cell...A huge amount of sensitive personal data is being collected by various online health monitoring applications.Although the data is anonymous,the personal trajectories(e.g.,the chronological access records of small cells)could become the anchor of linkage attacks to re-identify the users.Focusing on trajectory privacy in online health monitoring,we propose the User Trajectory Model(UTM),a generic trajectory re-identification risk predicting model to reveal the underlying relationship between trajectory uniqueness and aggregated data(e.g.,number of individuals covered by each small cell),and using the parameter combination of aggregated data to further mathematically derive the statistical characteristics of uniqueness(i.e.,the expectation and the variance).Eventually,exhaustive simulations validate the effectiveness of the UTM in privacy risk evaluation,confirm our theoretical deductions and present counter-intuitive insights.展开更多
A deep space multi-file delivery protocol(DSMDP)based on LT codes is proposed to reduce the influence of long delay and a high bit error rate(BER)in deep space communication.The protocol increases sending redundancy b...A deep space multi-file delivery protocol(DSMDP)based on LT codes is proposed to reduce the influence of long delay and a high bit error rate(BER)in deep space communication.The protocol increases sending redundancy by LT codes to improve the success rate of file delivery,and adopts different protective strategies for different situations of packet loss.At the same time,the multi-file united delivery strategy is adopted to make full use of the retransmission time to reduce the end-toend transmission delay.Furthermore,the protocol determines the quantity of encoded packets according to the feedback for controlling redundancy.The simulation results show that the proposed protocol can significantly reduce the transmission delay of files,which would be effectively suitable for deep space communication environment of high BER and long delay.展开更多
Offloading Mobile Devices(MDs)computation tasks to Edge Nodes(ENs)is a promising solution to overcome computation and energy resources limitations of MDs.However,there exists an unreasonable profit allocation problem ...Offloading Mobile Devices(MDs)computation tasks to Edge Nodes(ENs)is a promising solution to overcome computation and energy resources limitations of MDs.However,there exists an unreasonable profit allocation problem between MDs and ENs caused by the excessive concern on MD profit.In this paper,we propose an auction-based computation offloading algorithm,inspiring ENs to provide high-quality service by maximizing the profit of ENs.Firstly,a novel cooperation auction framework is designed to avoid overall profit damage of ENs,which is derived from the high computation delay at the overloaded ENs.Thereafter,the bidding willingness of each MD in every round of auction is determined to ensure MD rationality.Furthermore,we put forward a payment rule for the pre-selected winner to effectively guarantee auction truthfulness.Finally,the auction-based profit maximization offloading algorithm is proposed,and the MD is allowed to occupy the computation and spectrum resources of the EN for offloading if it wins the auction.Numerical results verify the performance of the proposed algorithm.Compared with the VA algorithm,the ENs profit is increased by 23.8%,and the task discard ratio is decreased by 7.5%.展开更多
As the massive sensor data generated by large-scale Wireless Sensor Networks （WSNs） recently become an indispensable part of ＇Big Data＇, the collection, storage, transmission and analysis of the big sensor data at...As the massive sensor data generated by large-scale Wireless Sensor Networks （WSNs） recently become an indispensable part of ＇Big Data＇, the collection, storage, transmission and analysis of the big sensor data attract considerable attention from researchers. Targeting the privacy requirements of large-scale WSNs and focusing on the energy-efficient collection of big sensor data, a Scalable Privacy-preserving Big Data Aggregation （Sca-PBDA） method is proposed in this paper. Firstly, according to the pre-established gradient topology structure, sensor nodes in the network are divided into clusters. Secondly, sensor data is modified by each node according to the privacy-preserving configuration message received from the sink. Subsequently, intra- and inter-cluster data aggregation is employed during the big sensor data reporting phase to reduce energy consumption. Lastly, aggregated results are recovered by the sink to complete the privacy-preserving big data aggregation. Simulation results validate the efficacy and scalability of Sca-PBDA and show that the big sensor data generated by large-scale WSNs is efficiently ag- gregated to reduce network resource consumption and the sensor data privacy is effectively protected to meet the ever-growing application requirements.展开更多
The phenomenal popularity of smart mobile computing hardware is enabling pervasive edge intelligence and ushering us into a digital twin era.However,the natural barrier between edge equipment owned by different intere...The phenomenal popularity of smart mobile computing hardware is enabling pervasive edge intelligence and ushering us into a digital twin era.However,the natural barrier between edge equipment owned by different interested parties poses unique challenges for cross-domain trust management.In addition,the openness of radio access and the accessibility of edge services render edge intelligence systems vulnerable and put sensitive user data in jeopardy.This paper presents an intrusion protection mechanism for edge trust transfer to address the inter-edge trust management issue and the conundrum of detecting indistinguishable malevolent nodes launching weak attacks.First,an inter-edge reputation transfer framework is established to leverage the trust quality of different edges to retain the accumulated trust histories of users when they roam in multi-edge environments structurally.Second,a fine-grained intrusion protection system is proposed to reduce the negative impact of attacks on user interactions and improve the overall trust quality and system security of edge intelligence services.The experimental results validate the effectiveness and superior performance of the proposed intrusion protection for edge trust transfer in securing,enhancing,and consolidating edge intelligence services.展开更多
The Joint Video Experts Team(JVET)has announced the latest generation of the Versatile Video Coding(VVC,H.266)standard.The in-loop filter in VVC inherits the De-Blocking Filter(DBF)and Sample Adaptive Offset(SAO)of Hi...The Joint Video Experts Team(JVET)has announced the latest generation of the Versatile Video Coding(VVC,H.266)standard.The in-loop filter in VVC inherits the De-Blocking Filter(DBF)and Sample Adaptive Offset(SAO)of High Efficiency Video Coding(HEVC,H.265),and adds the Adaptive Loop Filter(ALF)to minimize the error between the original sample and the decoded sample.However,for chaotic moving video encoding with low bitrates,serious blocking artifacts still remain after in-loop filtering due to the severe quantization distortion of texture details.To tackle this problem,this paper proposes a Convolutional Neural Network(CNN)based VVC in-loop filter for chaotic moving video encoding with low bitrates.First,a blur-aware attention network is designed to perceive the blurring effect and to restore texture details.Then,a deep in-loop filtering method is proposed based on the blur-aware network to replace the VVC in-loop filter.Finally,experimental results show that the proposed method could averagely save 8.3%of bit consumption at similar subjective quality.Meanwhile,under close bit rate consumption,the proposed method could reconstruct more texture information,thereby significantly reducing the blocking artifacts and improving the visual quality.展开更多
基金supported in part by National Natural Science Foundation of China(62271096,U20A20157)Natural Science Foundation of Chongqing,China(cstc2020jcyj-zdxmX0024,CSTB2022NSCQMSX0600)+5 种基金University Innovation Research Group of Chongqing(CXQT20017)Program for Innovation Team Building at Institutions of Higher Education in Chongqing(CXTDX201601020)Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202000626)Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJQN202000626Chongqing Municipal Technology Innovation and Application Development Special Key Project(cstc2020jscx-dxwtBX0053)。
文摘Cyber-Physical Networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV).With the increasing popularity of digital photography and Internet technology,more and more users are sharing images on CPN.However,many images are shared without any privacy processing,exposing hidden privacy risks and making sensitive content easily accessible to Artificial Intelligence(AI)algorithms.Existing image sharing methods lack fine-grained image sharing policies and cannot protect user privacy.To address this issue,we propose a social relationship-driven privacy customization protection model for publishers and co-photographers.We construct a heterogeneous social information network centered on social relationships,introduce a user intimacy evaluation method with time decay,and evaluate privacy levels considering user interest similarity.To protect user privacy while maintaining image appreciation,we design a lightweight face-swapping algorithm based on Generative Adversarial Network(GAN)to swap faces that need to be protected.Our proposed method minimizes the loss of image utility while satisfying privacy requirements,as shown by extensive theoretical and simulation analyses.
基金supported in part by the Graduate Research Innovation Project of Chongqing under grant CYB23237in part by the Doctoral Candidate Innovative Talent Program of CQUPT under grant BYJS202201+3 种基金in part by the National Natural Science Foundation of China(62271096,U20A20157)in part by the Natural Science Foundation of Chongqing,China(cstc2020jcyjzdxmX0024)in part by the University Innovation Research Group of Chongqing(CXQT20017)in part by the Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04).
文摘In the upcoming B5G/6G era,Virtual Reality(VR)over wireless has become a typical application,which is an inevitable trend in the development of video.However,in immersive and interactive VR experiences,VR services typically exhibit high delay,while simultaneously posing challenges for the energy consumption of local devices.To address these issues,this paper aims to improve the performance of VR service in the edge-terminal cooperative system.Specifically,we formulate a joint Caching,Computing,and Communication(3C)VR service policy problem by optimizing the weighted sum of the total VR delivery delay and the energy consumption of local devices.To design the optimal VR service policy,the optimization problem is decoupled into three independent subproblems to be solved separately.To improve the caching efficiency within the network,a Bert-based user interest analysis method is first proposed to accurately characterize the content request behavior.Based on this,a service cost minimum-maximization problem is formulated under the consideration of performance fairness among users.Then,the joint caching and computing scheme is derived for each user with a given allocation of communication resources while a bisection-based communication scheme is acquired with the given information on the joint caching and computing policy.With alternative optimization,an optimal policy for joint 3C based on user interest can be finally obtained.Simulation results are presented to demonstrate the superiority of the proposed user interest-aware caching scheme and the effectiveness of the joint 3C optimization policy while considering user fairness.Our code is available at https://github.com/mrfuqaq1108/Interest-Aware-Joint-3C-Optimization.
基金supported by the National Natural Science Foundation of China(62271096,U20A20157)Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202000626)+4 种基金Natural Science Foundation of Chongqing,China(cstc2020jcyjzdxmX0024)University Innovation Research Group of Chongqing(CXQT20017)Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)Chongqing Postdoctoral Science Special Foundation(2021XM3058)Chongqing Postgraduate Research and Innovation Project under grant(CYB22250).
文摘Vehicular Edge Computing(VEC)enhances the quality of user services by deploying wealth of resources near vehicles.However,due to highly dynamic and complex nature of vehicular networks,centralized decisionmaking for resource allocation proves inadequate within VECs.Conversely,allocating resources via distributed decision-making consumes vehicular resources.To improve the quality of user service,we formulate a problem of latency minimization,further subdividing this problem into two subproblems to be solved through distributed decision-making.To mitigate the resource consumption caused by distributed decision-making,we propose Reinforcement Learning(RL)algorithm based on sequential alternating multi-agent system mechanism,which effectively reduces the dimensionality of action space without losing the informational content of action,achieving network lightweighting.We discuss the rationality,generalizability,and inherent advantages of proposed mechanism.Simulation results indicate that our proposed mechanism outperforms traditional RL algorithms in terms of stability,generalizability,and adaptability to scenarios with invalid actions,all while achieving network lightweighting.
基金supported by National Natural Science Foundation of China(62271096,U20A20157)Natural Science Foundation of Chongqing,China(CSTB2023NSCQ-LZX0134)+3 种基金University Innovation Research Group of Chongqing(CXQT20017)Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202300632)the Chongqing Postdoctoral Special Funding Project(2022CQBSHTB2057).
文摘Aiming at the problem of mobile data traffic surge in 5G networks,this paper proposes an effective solution combining massive multiple-input multiple-output techniques with Ultra-Dense Network(UDN)and focuses on solving the resulting challenge of increased energy consumption.A base station control algorithm based on Multi-Agent Proximity Policy Optimization(MAPPO)is designed.In the constructed 5G UDN model,each base station is considered as an agent,and the MAPPO algorithm enables inter-base station collaboration and interference management to optimize the network performance.To reduce the extra power consumption due to frequent sleep mode switching of base stations,a sleep mode switching decision algorithm is proposed.The algorithm reduces unnecessary power consumption by evaluating the network state similarity and intelligently adjusting the agent’s action strategy.Simulation results show that the proposed algorithm reduces the power consumption by 24.61% compared to the no-sleep strategy and further reduces the power consumption by 5.36% compared to the traditional MAPPO algorithm under the premise of guaranteeing the quality of service of users.
基金supported in part by the National Natural Science Foundation of China under grants 61901078,61771082,61871062,and U20A20157in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under grant KJQN201900609+2 种基金in part by the Natural Science Foundation of Chongqing under grant cstc2020jcyj-zdxmX0024in part by University Innovation Research Group of Chongqing under grant CXQT20017in part by the China University Industry-University-Research Collaborative Innovation Fund(Future Network Innovation Research and Application Project)under grant 2021FNA04008.
文摘To guarantee the heterogeneous delay requirements of the diverse vehicular services,it is necessary to design a full cooperative policy for both Vehicle to Infrastructure(V2I)and Vehicle to Vehicle(V2V)links.This paper investigates the reduction of the delay in edge information sharing for V2V links while satisfying the delay requirements of the V2I links.Specifically,a mean delay minimization problem and a maximum individual delay minimization problem are formulated to improve the global network performance and ensure the fairness of a single user,respectively.A multi-agent reinforcement learning framework is designed to solve these two problems,where a new reward function is proposed to evaluate the utilities of the two optimization objectives in a unified framework.Thereafter,a proximal policy optimization approach is proposed to enable each V2V user to learn its policy using the shared global network reward.The effectiveness of the proposed approach is finally validated by comparing the obtained results with those of the other baseline approaches through extensive simulation experiments.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61871062and Grant 61771082the Natural Science Foundation of Chongqing of China under Grant cstc2013jcyjA40066+3 种基金the Program for Innovation Team Building at Institutions of Higher Education in Chongqing under Grant CXTDX201601020the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJQN201801316the Key Industrial Technology Development Project of Chongqing of China Development and Reform Commission under Grant 2018148208the Innovation and Entrepreneurship Demonstration Team of Yingcai Program of Chongqing of China under Grant CQYC201903167.
文摘A huge amount of sensitive personal data is being collected by various online health monitoring applications.Although the data is anonymous,the personal trajectories(e.g.,the chronological access records of small cells)could become the anchor of linkage attacks to re-identify the users.Focusing on trajectory privacy in online health monitoring,we propose the User Trajectory Model(UTM),a generic trajectory re-identification risk predicting model to reveal the underlying relationship between trajectory uniqueness and aggregated data(e.g.,number of individuals covered by each small cell),and using the parameter combination of aggregated data to further mathematically derive the statistical characteristics of uniqueness(i.e.,the expectation and the variance).Eventually,exhaustive simulations validate the effectiveness of the UTM in privacy risk evaluation,confirm our theoretical deductions and present counter-intuitive insights.
基金supported by the National Natural Science Foundation of China(61271261)the Natural Science Foundation Project of CQ CSTC(CSTC2012jjA40048)
文摘A deep space multi-file delivery protocol(DSMDP)based on LT codes is proposed to reduce the influence of long delay and a high bit error rate(BER)in deep space communication.The protocol increases sending redundancy by LT codes to improve the success rate of file delivery,and adopts different protective strategies for different situations of packet loss.At the same time,the multi-file united delivery strategy is adopted to make full use of the retransmission time to reduce the end-toend transmission delay.Furthermore,the protocol determines the quantity of encoded packets according to the feedback for controlling redundancy.The simulation results show that the proposed protocol can significantly reduce the transmission delay of files,which would be effectively suitable for deep space communication environment of high BER and long delay.
基金supported by National Natural Science Foundation of China under grants 61901070,61801065,61771082,61871062,U20A20157in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under grants KJQN202000603,KJQN201900611+1 种基金in part by the Natural Science Foundation of Chongqing under grant cstc2020jcyjzdxmX0024part by University Innovation Research Group of Chongqing under grant CXQT20017.
文摘Offloading Mobile Devices(MDs)computation tasks to Edge Nodes(ENs)is a promising solution to overcome computation and energy resources limitations of MDs.However,there exists an unreasonable profit allocation problem between MDs and ENs caused by the excessive concern on MD profit.In this paper,we propose an auction-based computation offloading algorithm,inspiring ENs to provide high-quality service by maximizing the profit of ENs.Firstly,a novel cooperation auction framework is designed to avoid overall profit damage of ENs,which is derived from the high computation delay at the overloaded ENs.Thereafter,the bidding willingness of each MD in every round of auction is determined to ensure MD rationality.Furthermore,we put forward a payment rule for the pre-selected winner to effectively guarantee auction truthfulness.Finally,the auction-based profit maximization offloading algorithm is proposed,and the MD is allowed to occupy the computation and spectrum resources of the EN for offloading if it wins the auction.Numerical results verify the performance of the proposed algorithm.Compared with the VA algorithm,the ENs profit is increased by 23.8%,and the task discard ratio is decreased by 7.5%.
文摘As the massive sensor data generated by large-scale Wireless Sensor Networks （WSNs） recently become an indispensable part of ＇Big Data＇, the collection, storage, transmission and analysis of the big sensor data attract considerable attention from researchers. Targeting the privacy requirements of large-scale WSNs and focusing on the energy-efficient collection of big sensor data, a Scalable Privacy-preserving Big Data Aggregation （Sca-PBDA） method is proposed in this paper. Firstly, according to the pre-established gradient topology structure, sensor nodes in the network are divided into clusters. Secondly, sensor data is modified by each node according to the privacy-preserving configuration message received from the sink. Subsequently, intra- and inter-cluster data aggregation is employed during the big sensor data reporting phase to reduce energy consumption. Lastly, aggregated results are recovered by the sink to complete the privacy-preserving big data aggregation. Simulation results validate the efficacy and scalability of Sca-PBDA and show that the big sensor data generated by large-scale WSNs is efficiently ag- gregated to reduce network resource consumption and the sensor data privacy is effectively protected to meet the ever-growing application requirements.
基金supported by National Natural Science Foundation of China(61901071,61871062,61771082,U20A20157)Science and Natural Science Foundation of Chongqing,China(cstc2020jcyjzdxmX0024)+3 种基金University Innovation Research Group of Chongqing(CXQT20017)Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)Science and Technology ResearchProgram of Chongqing Municipal Education Commission(KJQN202000626)Program for Innovation Team Building at Institutions of Higher Education in Chongqing(CXTDX201601020)。
文摘The phenomenal popularity of smart mobile computing hardware is enabling pervasive edge intelligence and ushering us into a digital twin era.However,the natural barrier between edge equipment owned by different interested parties poses unique challenges for cross-domain trust management.In addition,the openness of radio access and the accessibility of edge services render edge intelligence systems vulnerable and put sensitive user data in jeopardy.This paper presents an intrusion protection mechanism for edge trust transfer to address the inter-edge trust management issue and the conundrum of detecting indistinguishable malevolent nodes launching weak attacks.First,an inter-edge reputation transfer framework is established to leverage the trust quality of different edges to retain the accumulated trust histories of users when they roam in multi-edge environments structurally.Second,a fine-grained intrusion protection system is proposed to reduce the negative impact of attacks on user interactions and improve the overall trust quality and system security of edge intelligence services.The experimental results validate the effectiveness and superior performance of the proposed intrusion protection for edge trust transfer in securing,enhancing,and consolidating edge intelligence services.
基金supported by National Natural Science Foundation of China under grant U20A20157,61771082,62271096 and 61871062the General Program of Chonqing Natural Science Foundation under grant cstc2021jcyj-msxm X0032+2 种基金the Natural Science Foundation of Chongqing,China(cstc2020jcyj-zdxm X0024)the Science and Technology Research Program of Chongqing Municipal Education Commission under grant KJQN202300632the University Innovation Research Group of Chongqing(CXQT20017)。
文摘The Joint Video Experts Team(JVET)has announced the latest generation of the Versatile Video Coding(VVC,H.266)standard.The in-loop filter in VVC inherits the De-Blocking Filter(DBF)and Sample Adaptive Offset(SAO)of High Efficiency Video Coding(HEVC,H.265),and adds the Adaptive Loop Filter(ALF)to minimize the error between the original sample and the decoded sample.However,for chaotic moving video encoding with low bitrates,serious blocking artifacts still remain after in-loop filtering due to the severe quantization distortion of texture details.To tackle this problem,this paper proposes a Convolutional Neural Network(CNN)based VVC in-loop filter for chaotic moving video encoding with low bitrates.First,a blur-aware attention network is designed to perceive the blurring effect and to restore texture details.Then,a deep in-loop filtering method is proposed based on the blur-aware network to replace the VVC in-loop filter.Finally,experimental results show that the proposed method could averagely save 8.3%of bit consumption at similar subjective quality.Meanwhile,under close bit rate consumption,the proposed method could reconstruct more texture information,thereby significantly reducing the blocking artifacts and improving the visual quality.
