The rapid integration of Internet of Things(IoT)devices and distributed energy resources into smart grids has improved monitoring,control,and energy efficiency.However,it also exposes the grid to cyberattacks and priv...The rapid integration of Internet of Things(IoT)devices and distributed energy resources into smart grids has improved monitoring,control,and energy efficiency.However,it also exposes the grid to cyberattacks and privacy risks,as increased connectivity and data exchange can significantly disrupt energy management and system stability.Studies focused on centralized cybersecurity mechanisms that lacked scalability and did not emphasize the inherent graph structure of power networks.This study proposes a privacy-preserving and cyber-resilient energy-optimization framework,FedGNN,for IoT-enabled smart grids that jointly integrates federated learning,graph neural network-based trust inference,and trust-aware energy dispatch.The framework dynamically learns node-level trust scores from multifeature measurements,including load,voltage,frequency,renewable generation,and battery storage,and incorporates them into real-time energy optimization.Results demonstrate that the proposed approach improves system resilience up to 12%,mitigates the impact of compromised nodes,and maintains operational reliability,while preserving the privacy of distributed data.A comparative analysis with baseline methods shows the proposed framework's superior performance in energy deviation,resilience,and trust-aware decision-making.The results highlight the potential of integrating AI-driven trust mechanisms with federated learning for secure and efficient energy management in future IoT-enabled smart grids.展开更多
Managing massive data flows effectively and resolving spectrum shortages are two challenges that smart grid communication networks(SGCN)must overcome.To address these problems,we provide a combined optimization approa...Managing massive data flows effectively and resolving spectrum shortages are two challenges that smart grid communication networks(SGCN)must overcome.To address these problems,we provide a combined optimization approach that makes use of cognitive radio(CR)and non-orthogonal multiple access(NOMA)technologies.Our work focuses on using user pairing(UP)and power allocation(PA)techniques to maximize energy efficiency(EE)in SGCN,particularly within neighbourhood area networks(NANs).We develop a joint optimization problem that takes into account the real-world limitations of a CR-NOMA setting.This problem is NP-hard,nonlinear,and nonconvex by nature.To address the computational complexity of the problem,we use the block coordinate descent(BCD)method,which breaks the problem into UP and PA subproblems.Initially,we proposed the zebra-optimization user pairing(ZOUP)algorithm to tackle the UP problem,which outperforms both orthogonal multiple access(OMA)and non-optimized NOMA(UPWO)by 78.8%and13.6%,respectively,at a SNR of 15 dB.Based on the ZOUP pairs,we subsequently proposed the PA approach,i.e.,ZOUPPA,which significantly outperforms UPWO and ZOUP by 53.2%and 25.4%,respectively,at an SNR of 15 dB.A detailed analysis of key parameters,including varying SNRs,power allocation constants,path loss exponents,user density,channel availability,and coverage radius,underscores the superiority of our approach.By facilitating the effective use of communication resources in SGCN,our research opens the door to more intelligent and energy-efficient grid systems.Our work tackles important issues in SGCN and lays the groundwork for future developments in smart grid communication technologies by combining modern optimization approaches with CR-NOMA.展开更多
The publisher regrets the CRediT authorship contribution statement was inserted incorrectly and the correct statement should be updated as below:Zengji Liu:Writing-review&editing,Writing-original draft,Visualizati...The publisher regrets the CRediT authorship contribution statement was inserted incorrectly and the correct statement should be updated as below:Zengji Liu:Writing-review&editing,Writing-original draft,Visualization,Validation,Supervision,Software,Resources,Project administration,Methodology,Investigation,Funding acquisition,Formal analysis,Data curation,Conceptualization.Mengge Liu:Writing-review&editing,Writing-original draft,Investigation.Qi Wang:Writing-review&editing,Writing-original draft.Yi Tang:Writing-review&editing,Writing-original draft.展开更多
随着智能电网的快速发展与物联网终端设备的规模化应用部署,互联网协议第6版(Internet Protocol Version 6,IPv6)技术依托其丰富的地址资源和安全特性,已成为网络架构的关键支撑,IPv6地址的管理和日志审计对保障电网安全稳定运行至关重...随着智能电网的快速发展与物联网终端设备的规模化应用部署,互联网协议第6版(Internet Protocol Version 6,IPv6)技术依托其丰富的地址资源和安全特性,已成为网络架构的关键支撑,IPv6地址的管理和日志审计对保障电网安全稳定运行至关重要。系统探讨了智能电网中物联网设备IPv6地址相关技术原理,深入分析了IPv6地址日志审计技术在智能电网中的应用难点与挑战,提出一种IPv6地址日志审计的简易优化方案,并通过实际案例进行分析和可行性验证,为智能电网中的实际应用提供理论依据和实践指导,对推动智能电网网络安全建设具有重要意义。展开更多
基金supported by the Deanship of Research and Graduate Studies,King Khalid University,for funding this work through a large research project under grant number(RGP2/603/45)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia,through the Researchers Supporting Project number(PNURSP2026R510).
摘要The rapid integration of Internet of Things(IoT)devices and distributed energy resources into smart grids has improved monitoring,control,and energy efficiency.However,it also exposes the grid to cyberattacks and privacy risks,as increased connectivity and data exchange can significantly disrupt energy management and system stability.Studies focused on centralized cybersecurity mechanisms that lacked scalability and did not emphasize the inherent graph structure of power networks.This study proposes a privacy-preserving and cyber-resilient energy-optimization framework,FedGNN,for IoT-enabled smart grids that jointly integrates federated learning,graph neural network-based trust inference,and trust-aware energy dispatch.The framework dynamically learns node-level trust scores from multifeature measurements,including load,voltage,frequency,renewable generation,and battery storage,and incorporates them into real-time energy optimization.Results demonstrate that the proposed approach improves system resilience up to 12%,mitigates the impact of compromised nodes,and maintains operational reliability,while preserving the privacy of distributed data.A comparative analysis with baseline methods shows the proposed framework's superior performance in energy deviation,resilience,and trust-aware decision-making.The results highlight the potential of integrating AI-driven trust mechanisms with federated learning for secure and efficient energy management in future IoT-enabled smart grids.
摘要Managing massive data flows effectively and resolving spectrum shortages are two challenges that smart grid communication networks(SGCN)must overcome.To address these problems,we provide a combined optimization approach that makes use of cognitive radio(CR)and non-orthogonal multiple access(NOMA)technologies.Our work focuses on using user pairing(UP)and power allocation(PA)techniques to maximize energy efficiency(EE)in SGCN,particularly within neighbourhood area networks(NANs).We develop a joint optimization problem that takes into account the real-world limitations of a CR-NOMA setting.This problem is NP-hard,nonlinear,and nonconvex by nature.To address the computational complexity of the problem,we use the block coordinate descent(BCD)method,which breaks the problem into UP and PA subproblems.Initially,we proposed the zebra-optimization user pairing(ZOUP)algorithm to tackle the UP problem,which outperforms both orthogonal multiple access(OMA)and non-optimized NOMA(UPWO)by 78.8%and13.6%,respectively,at a SNR of 15 dB.Based on the ZOUP pairs,we subsequently proposed the PA approach,i.e.,ZOUPPA,which significantly outperforms UPWO and ZOUP by 53.2%and 25.4%,respectively,at an SNR of 15 dB.A detailed analysis of key parameters,including varying SNRs,power allocation constants,path loss exponents,user density,channel availability,and coverage radius,underscores the superiority of our approach.By facilitating the effective use of communication resources in SGCN,our research opens the door to more intelligent and energy-efficient grid systems.Our work tackles important issues in SGCN and lays the groundwork for future developments in smart grid communication technologies by combining modern optimization approaches with CR-NOMA.
摘要The publisher regrets the CRediT authorship contribution statement was inserted incorrectly and the correct statement should be updated as below:Zengji Liu:Writing-review&editing,Writing-original draft,Visualization,Validation,Supervision,Software,Resources,Project administration,Methodology,Investigation,Funding acquisition,Formal analysis,Data curation,Conceptualization.Mengge Liu:Writing-review&editing,Writing-original draft,Investigation.Qi Wang:Writing-review&editing,Writing-original draft.Yi Tang:Writing-review&editing,Writing-original draft.
摘要随着智能电网的快速发展与物联网终端设备的规模化应用部署,互联网协议第6版(Internet Protocol Version 6,IPv6)技术依托其丰富的地址资源和安全特性,已成为网络架构的关键支撑,IPv6地址的管理和日志审计对保障电网安全稳定运行至关重要。系统探讨了智能电网中物联网设备IPv6地址相关技术原理,深入分析了IPv6地址日志审计技术在智能电网中的应用难点与挑战,提出一种IPv6地址日志审计的简易优化方案,并通过实际案例进行分析和可行性验证,为智能电网中的实际应用提供理论依据和实践指导,对推动智能电网网络安全建设具有重要意义。