Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic ...Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic Graph(DAG)structure often suffer from performance limitations.The DAG lattice structure is a novel blockchain model in which each node maintains its own account chain,and only the node itself is allowed to update it.This feature makes the DAG lattice structure particularly suitable for addressing the challenges in dynamically connected IoV environment.In this paper,we propose a blockchain architecture based on the DAG lattice structure,specifically designed for dynamically connected IoV.In the proposed system,nodes must obtain authorization from a trusted authority before joining,forming a permissioned blockchain.Each node is assigned an individual account chain,allowing vehicles with limited storage capacity to participate in the blockchain by storing transactions only from nearby vehicles’account chains.Every transmitted message is treated as a transaction and added to the blockchain,enablingmore efficient data transmission in a dynamic network environment.Areputation-based incentivemechanism is introduced to encourage nodes to behave normally.Experimental results demonstrate that the proposed architecture achieves better performance compared with traditional single-chain and DAG-based approaches in terms of average transmission delay and storage cost.展开更多
The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects....The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects.IoV systems typically send massive volumes of raw data to central servers,which may raise privacy issues.Additionally,model training on IoV devices with limited resources normally leads to slower training times and reduced service quality.We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning(TinyML)approach,which operates on IoV edge devices without sharing sensitive raw data.Specifically,we focus on integrating split learning(SL)with federated learning(FL)and TinyML models.FL is a decentralisedmachine learning(ML)technique that enables numerous edge devices to train a standard model while retaining data locally collectively.The article intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the IoV domain,coupled with FL and TinyML.The approach starts with the IoV learning framework,which includes edge computing,FL,SL,and TinyML,and then proceeds to discuss how these technologies might be integrated.We elucidate the comprehensive operational principles of Federated and split learning by examining and addressingmany challenges.We subsequently examine the integration of SL with FL and various applications of TinyML.Finally,exploring the potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM.It is a superior method for preserving privacy as it conducts model training on individual devices or edge nodes,thereby obviating the necessity for centralised data aggregation,which presents considerable privacy threats.The insights provided aim to help both researchers and practitioners understand the complicated terrain of FL and SL,hence facilitating advancement in this swiftly progressing domain.展开更多
In view of the phenomenon that the adhesion strength between the surface of polyacrylonitrile-butadiene-styrene-polycarbonate(ABS-PC)copolymer and the electroless copper plating layer is relatively low.To solve the pr...In view of the phenomenon that the adhesion strength between the surface of polyacrylonitrile-butadiene-styrene-polycarbonate(ABS-PC)copolymer and the electroless copper plating layer is relatively low.To solve the problems of poor surface wettability and low surface roughness of the ABS-PC substrate,the N,N-dimethylformamide(DMF)-ethanol(C_(2)H_(5)OH)-water(H_(2)O)system was employed as the swelling system for the ABS-PC substrate.The effects of the DMF volume fraction in the swelling system and the swelling time on the swelling effect of ABS-PC at 35℃ were investigated.KMnO_(4)-H_(2)SO_(4)-H_(2)O system was used as the etching system for ABS-PC substrate under the conditions of the volume ratio of water to sulfuric acid of 1﹕2,with KMnO_(4) content of 30 g/L,etching temperature of 60℃,and etching time of 25 min.The results indicate that dense pores with uniform sized are formed on the surface of the ABS-PC substrate surface after swelling and etching treatments,accompanied by an increase in surface roughness when the swelling temperature is 35℃,the DMF volume fraction in the swelling system is 80%,and the swelling time is 5 min.Furthermore,the content of C element on the surface of the ABS-PC substrate decreased,while that of O element increased,and the surface hydrophilicity is enhanced,which is attributed to two hydrophilic groups,-C=O and-COOH,being generated on the ABS-PC substrate surface,significantly improving the wettability of the ABS-PC substrate surface.Under the combination effects of high surface roughness and strong surface hydrophilicity,the adhesion strength between the ABS-PC substrate surface and the electroless copper plating layer reached to 0.81 kN/m,meeting the adhesion strength requirement of 0.70 kN/m in the industrial production.展开更多
基金funded in part by the Supported by Natural Science Foundation of Inner Mongolia Autonomous Region of China under Grants 2024QN06022 and 2023QN06008in part by the First-Class Discipline Research Special Project under Grant YLXKZX-NGD-015in part by the Inner Mongolia University of Technology Scientific Research Start-Up Project under Grant BS2024067.
文摘Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic Graph(DAG)structure often suffer from performance limitations.The DAG lattice structure is a novel blockchain model in which each node maintains its own account chain,and only the node itself is allowed to update it.This feature makes the DAG lattice structure particularly suitable for addressing the challenges in dynamically connected IoV environment.In this paper,we propose a blockchain architecture based on the DAG lattice structure,specifically designed for dynamically connected IoV.In the proposed system,nodes must obtain authorization from a trusted authority before joining,forming a permissioned blockchain.Each node is assigned an individual account chain,allowing vehicles with limited storage capacity to participate in the blockchain by storing transactions only from nearby vehicles’account chains.Every transmitted message is treated as a transaction and added to the blockchain,enablingmore efficient data transmission in a dynamic network environment.Areputation-based incentivemechanism is introduced to encourage nodes to behave normally.Experimental results demonstrate that the proposed architecture achieves better performance compared with traditional single-chain and DAG-based approaches in terms of average transmission delay and storage cost.
文摘The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects.IoV systems typically send massive volumes of raw data to central servers,which may raise privacy issues.Additionally,model training on IoV devices with limited resources normally leads to slower training times and reduced service quality.We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning(TinyML)approach,which operates on IoV edge devices without sharing sensitive raw data.Specifically,we focus on integrating split learning(SL)with federated learning(FL)and TinyML models.FL is a decentralisedmachine learning(ML)technique that enables numerous edge devices to train a standard model while retaining data locally collectively.The article intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the IoV domain,coupled with FL and TinyML.The approach starts with the IoV learning framework,which includes edge computing,FL,SL,and TinyML,and then proceeds to discuss how these technologies might be integrated.We elucidate the comprehensive operational principles of Federated and split learning by examining and addressingmany challenges.We subsequently examine the integration of SL with FL and various applications of TinyML.Finally,exploring the potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM.It is a superior method for preserving privacy as it conducts model training on individual devices or edge nodes,thereby obviating the necessity for centralised data aggregation,which presents considerable privacy threats.The insights provided aim to help both researchers and practitioners understand the complicated terrain of FL and SL,hence facilitating advancement in this swiftly progressing domain.
文摘In view of the phenomenon that the adhesion strength between the surface of polyacrylonitrile-butadiene-styrene-polycarbonate(ABS-PC)copolymer and the electroless copper plating layer is relatively low.To solve the problems of poor surface wettability and low surface roughness of the ABS-PC substrate,the N,N-dimethylformamide(DMF)-ethanol(C_(2)H_(5)OH)-water(H_(2)O)system was employed as the swelling system for the ABS-PC substrate.The effects of the DMF volume fraction in the swelling system and the swelling time on the swelling effect of ABS-PC at 35℃ were investigated.KMnO_(4)-H_(2)SO_(4)-H_(2)O system was used as the etching system for ABS-PC substrate under the conditions of the volume ratio of water to sulfuric acid of 1﹕2,with KMnO_(4) content of 30 g/L,etching temperature of 60℃,and etching time of 25 min.The results indicate that dense pores with uniform sized are formed on the surface of the ABS-PC substrate surface after swelling and etching treatments,accompanied by an increase in surface roughness when the swelling temperature is 35℃,the DMF volume fraction in the swelling system is 80%,and the swelling time is 5 min.Furthermore,the content of C element on the surface of the ABS-PC substrate decreased,while that of O element increased,and the surface hydrophilicity is enhanced,which is attributed to two hydrophilic groups,-C=O and-COOH,being generated on the ABS-PC substrate surface,significantly improving the wettability of the ABS-PC substrate surface.Under the combination effects of high surface roughness and strong surface hydrophilicity,the adhesion strength between the ABS-PC substrate surface and the electroless copper plating layer reached to 0.81 kN/m,meeting the adhesion strength requirement of 0.70 kN/m in the industrial production.
