Over the past few decades,the Internet has rapidly diffused across China.The spread of the Internet has had a profound economic and social impact on Chinese rural areas.Existing research shows that Internet access sig...Over the past few decades,the Internet has rapidly diffused across China.The spread of the Internet has had a profound economic and social impact on Chinese rural areas.Existing research shows that Internet access significantly impacts agricultural production and improves smallholder farmers’income.Beyond these,the Internet can affect other dimensions of social welfare.However,research about the impact of Internet access on dietary quality in rural China remains scarce.This study utilizes multi-period panel data from Fixed Observation Point in rural China from 2009 to 2015 to estimate the impact of Internet access on dietary quality and food consumption of rural households and conducts a causal analysis.Regression models with time and household fixed effects allow robust estimation while reducing potential issues of unobserved heterogeneity.The estimates show that Internet access has significantly increased rural household dietary quality(measured by the Chinese Diet Balance Index).Further research finds that Internet access has increased the consumption of animal products,such as aquatic and dairy products.We also examine the underlying mechanisms.Internet access improves dietary quality and food consumption mainly through increasing household income and food expenditure.These results encourage the promotion of Internet access as a valuable tool for nutritional improvements,especially in rural areas.展开更多
Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead...Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead and data privacy risks.The recently proposed Swarm Learning(SL)provides a decentralized machine learning approach for unit edge computing and blockchain-based coordination.A Swarm-Federated Deep Learning framework in the IoV system(IoV-SFDL)that integrates SL into the FDL framework is proposed in this paper.The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL,then aggregates the global FDL model among different SL groups with a credibility weights prediction algorithm.Extensive experimental results show that compared with the baseline frameworks,the proposed IoV-SFDL framework reduces the overhead of client-to-server communication by 16.72%,while the model performance improves by about 5.02%for the same training iterations.展开更多
The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the d...The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device itself.Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features.This paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in homes.We have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT devices.Our system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing devices.We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server.The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings.It is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation systems.Additionally,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber threats.The trial results support the proposed system and demonstrate its potential for use in everyday life.展开更多
Malicious attacks against data are unavoidable in the interconnected,open and shared Energy Internet(EI),Intrusion tolerant techniques are critical to the data security of EI.Existing intrusion tolerant techniques suf...Malicious attacks against data are unavoidable in the interconnected,open and shared Energy Internet(EI),Intrusion tolerant techniques are critical to the data security of EI.Existing intrusion tolerant techniques suffered from problems such as low adaptability,policy lag,and difficulty in determining the degree of tolerance.To address these issues,we propose a novel adaptive intrusion tolerance model based on game theory that enjoys two-fold ideas:(1)it constructs an improved replica of the intrusion tolerance model of the dynamic equation evolution game to induce incentive weights;and (2)it combines a tournament competition model with incentive weights to obtain optimal strategies for each stage of the game process.Extensive experiments are conducted in the IEEE 39-bus system,whose results demonstrate the feasibility of the incentive weights,confirm the proposed strategy strengthens the system’s ability to tolerate aggression,and improves the dynamic adaptability and response efficiency of the aggression-tolerant system in the case of limited resources.展开更多
While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),...While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.展开更多
High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency...High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.展开更多
The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated...The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated to cyber security threats that need to be addressed.This work investigates hybrid cyber threats(HCTs),which are now working on an entirely new level with the increasingly adopted IIoT.This work focuses on emerging methods to model,detect,and defend against hybrid cyber attacks using machine learning(ML)techniques.Specifically,a novel ML-based HCT modelling and analysis framework was proposed,in which L1 regularisation and Random Forest were used to cluster features and analyse the importance and impact of each feature in both individual threats and HCTs.A grey relation analysis-based model was employed to construct the correlation between IIoT components and different threats.展开更多
Reliable communication and intensive computing power cannot be provided effectively by temporary hot spots in disaster areas and complex terrain ground infrastructure.Mitigating this has greatly developed the applicat...Reliable communication and intensive computing power cannot be provided effectively by temporary hot spots in disaster areas and complex terrain ground infrastructure.Mitigating this has greatly developed the application and integration of UAV and Mobile Edge Computing(MEC)to the Internet of Things(loT).However,problems such as multi-user and huge data flow in large areas,which contradict the reality that a single UAV is constrained by limited computing power,still exist.Due to allowing UAV collaboration to accomplish complex tasks,cooperative task offloading between multiple UAVs must meet the interdependence of tasks and realize parallel processing,which reduces the computing power consumption and endurance pressure of terminals.Considering the computing requirements of the user terminal,delay constraint of a computing task,energy constraint,and safe distance of UAV,we constructed a UAV-Assisted cooperative offloading energy efficiency system for mobile edge computing to minimize user terminal energy consumption.However,the resulting optimization problem is originally nonconvex and thus,difficult to solve optimally.To tackle this problem,we developed an energy efficiency optimization algorithm using Block Coordinate Descent(BCD)that decomposes the problem into three convex subproblems.Furthermore,we jointly optimized the number of local computing tasks,number of computing offloaded tasks,trajectories of UAV,and offloading matching relationship between multi-UAVs and multiuser terminals.Simulation results show that the proposed approach is suitable for different channel conditions and significantly saves the user terminal energy consumption compared with other benchmark schemes.展开更多
基金This study was supported in part by the National Natural Science Foundation of China(71973136 and 72061147002)the 2115 Talent Development Program of China Agricultural University.
文摘Over the past few decades,the Internet has rapidly diffused across China.The spread of the Internet has had a profound economic and social impact on Chinese rural areas.Existing research shows that Internet access significantly impacts agricultural production and improves smallholder farmers’income.Beyond these,the Internet can affect other dimensions of social welfare.However,research about the impact of Internet access on dietary quality in rural China remains scarce.This study utilizes multi-period panel data from Fixed Observation Point in rural China from 2009 to 2015 to estimate the impact of Internet access on dietary quality and food consumption of rural households and conducts a causal analysis.Regression models with time and household fixed effects allow robust estimation while reducing potential issues of unobserved heterogeneity.The estimates show that Internet access has significantly increased rural household dietary quality(measured by the Chinese Diet Balance Index).Further research finds that Internet access has increased the consumption of animal products,such as aquatic and dairy products.We also examine the underlying mechanisms.Internet access improves dietary quality and food consumption mainly through increasing household income and food expenditure.These results encourage the promotion of Internet access as a valuable tool for nutritional improvements,especially in rural areas.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant 62071179.
文摘Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead and data privacy risks.The recently proposed Swarm Learning(SL)provides a decentralized machine learning approach for unit edge computing and blockchain-based coordination.A Swarm-Federated Deep Learning framework in the IoV system(IoV-SFDL)that integrates SL into the FDL framework is proposed in this paper.The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL,then aggregates the global FDL model among different SL groups with a credibility weights prediction algorithm.Extensive experimental results show that compared with the baseline frameworks,the proposed IoV-SFDL framework reduces the overhead of client-to-server communication by 16.72%,while the model performance improves by about 5.02%for the same training iterations.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R333)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device itself.Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features.This paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in homes.We have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT devices.Our system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing devices.We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server.The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings.It is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation systems.Additionally,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber threats.The trial results support the proposed system and demonstrate its potential for use in everyday life.
基金supported by the National Natural Science Foundation of China(Nos.51977113,62293500,62293501 and 62293505).
文摘Malicious attacks against data are unavoidable in the interconnected,open and shared Energy Internet(EI),Intrusion tolerant techniques are critical to the data security of EI.Existing intrusion tolerant techniques suffered from problems such as low adaptability,policy lag,and difficulty in determining the degree of tolerance.To address these issues,we propose a novel adaptive intrusion tolerance model based on game theory that enjoys two-fold ideas:(1)it constructs an improved replica of the intrusion tolerance model of the dynamic equation evolution game to induce incentive weights;and (2)it combines a tournament competition model with incentive weights to obtain optimal strategies for each stage of the game process.Extensive experiments are conducted in the IEEE 39-bus system,whose results demonstrate the feasibility of the incentive weights,confirm the proposed strategy strengthens the system’s ability to tolerate aggression,and improves the dynamic adaptability and response efficiency of the aggression-tolerant system in the case of limited resources.
文摘While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.
基金supported in part by the National Natural Science Foundation of China(62371116 and 62231020)in part by the Science and Technology Project of Hebei Province Education Department(ZD2022164)+2 种基金in part by the Fundamental Research Funds for the Central Universities(N2223031)in part by the Open Research Project of Xidian University(ISN24-08)Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education(Guilin University of Electronic Technology,China,CRKL210203)。
文摘High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.
文摘The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated to cyber security threats that need to be addressed.This work investigates hybrid cyber threats(HCTs),which are now working on an entirely new level with the increasingly adopted IIoT.This work focuses on emerging methods to model,detect,and defend against hybrid cyber attacks using machine learning(ML)techniques.Specifically,a novel ML-based HCT modelling and analysis framework was proposed,in which L1 regularisation and Random Forest were used to cluster features and analyse the importance and impact of each feature in both individual threats and HCTs.A grey relation analysis-based model was employed to construct the correlation between IIoT components and different threats.
基金supported by the Jiangsu Provincial Key Research and Development Program(No.BE2020084-4)the National Natural Science Foundation of China(No.92067201)+2 种基金the National Natural Science Foundation of China(61871446)the Open Research Fund of Jiangsu Key Laboratory of Wireless Communications(710020017002)the Natural Science Foundation of Nanjing University of Posts and telecommunications(NY220047).
文摘Reliable communication and intensive computing power cannot be provided effectively by temporary hot spots in disaster areas and complex terrain ground infrastructure.Mitigating this has greatly developed the application and integration of UAV and Mobile Edge Computing(MEC)to the Internet of Things(loT).However,problems such as multi-user and huge data flow in large areas,which contradict the reality that a single UAV is constrained by limited computing power,still exist.Due to allowing UAV collaboration to accomplish complex tasks,cooperative task offloading between multiple UAVs must meet the interdependence of tasks and realize parallel processing,which reduces the computing power consumption and endurance pressure of terminals.Considering the computing requirements of the user terminal,delay constraint of a computing task,energy constraint,and safe distance of UAV,we constructed a UAV-Assisted cooperative offloading energy efficiency system for mobile edge computing to minimize user terminal energy consumption.However,the resulting optimization problem is originally nonconvex and thus,difficult to solve optimally.To tackle this problem,we developed an energy efficiency optimization algorithm using Block Coordinate Descent(BCD)that decomposes the problem into three convex subproblems.Furthermore,we jointly optimized the number of local computing tasks,number of computing offloaded tasks,trajectories of UAV,and offloading matching relationship between multi-UAVs and multiuser terminals.Simulation results show that the proposed approach is suitable for different channel conditions and significantly saves the user terminal energy consumption compared with other benchmark schemes.
