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Artificial Intelligence for Edge Service Optimization in internet of vehicles:A Survey

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Tsinghua Science and Technology 2022年第2期27卷 270-287页

作者： Xiaolong Xu Haoyuan Li Weijie Xu Zhongjian Liu Liang Yao Fei Dai the School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and Technology and EngineeringNanjing 210044China the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China the School of Big Data and Intelligence Engineering Southwest Forestry UniversityKunming 650233China

The internet of vehicles(IoV)plays a crucial role in providing diversified services because of its powerful capability of collecting real-time ***,collected information is transmitted to a centralized resourceintensive cloud platform for service *** Computing(EC)that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular ***,many measures are adopted to optimize the performance of EC-enabled IoV,but they hardly help make dynamic decisions according to real-time *** Intelligence(AI)is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources *** extensive research has employed AI to optimize EC performance,summaries with relative concepts or prospects are quite *** address this gap,we conduct an exhaustive survey about utilizing AI in edge service optimization in ***,we establish the general condition and relative concepts about IoV,EC,and ***,we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service ***,we discuss a number of open issues in optimizing edge services with AI.

关键词： edge service internet of vehicles artificial intel igence

A credibility-aware swarm-federated deep learning framework in internet of vehicles

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Digital Communications and Networks 2024年第1期10卷 150-157页

作者： Zhe Wang Xinhang Li Tianhao Wu Chen Xu Lin Zhang School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing100876China

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 *** 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 *** 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 *** 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.

关键词： Swarm learning Federated deep learning internet of vehicles Privacy Efficiency

A mobile edge computing-based applications execution framework for internet of vehicles

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Frontiers of Computer Science 2022年第5期16卷 131-141页

作者： Libing WU Rui ZHANG Qingan LI Chao MA Xiaochuan SHI School of Computer Science Wuhan UniversityWuhan 430000China School of Cyber Science and Engineering Wuhan UniversityWuhan 430000China Shenzhen Research Institute of Wuhan University Shenzhen 518052China

Mobile edge computing(MEC)is a promising technology for the internet of vehicles,especially in terms of application offloading and resource *** existing offloading schemes are sub-optimal,since these offloading strategies consider an application as a *** comparison,in this paper we propose an application-centric framework and build a finer-grained offloading scheme based on application *** our framework,each application is modelled as a directed acyclic graph,where each node represents a subtask and each edge represents the data flow dependency between a pair of *** vehicles and MEC server within the communication range can be used as candidate offloading ***,the offloading involves assigning these computing nodes to *** addition,the proposed offloading scheme deal with the delay constraint of each *** experimental evaluation show that,compared to existing non-partitioning offloading schemes,this proposed one effectively improves the performance of the application in terms of execution time and throughput.

关键词： mobile edge computing application partition directed acyclic graph offloading internet of vehicles

Artificial Intelligence Based Clustering with Routing Protocol for internet of vehicles

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Computers, Materials & Continua 2022年第3期70卷 5835-5853页

作者： Manar Ahmed Hamza Haya Mesfer Alshahrani Fahd NAl-Wesabi Mesfer Al Duhayyim Anwer Mustafa Hilal Hany Mahgoub Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

With recent advances made in internet of vehicles(IoV)and Cloud Computing(CC),the Intelligent Transportation Systems(ITS)find it advantageous in terms of improvement in quality and interactivity of urban transportation service,mitigation of costs incurred,reduction in resource utilization,and improvement in traffic management *** trafficrelated problems in future smart cities can be sorted out with the incorporation of IoV in *** communication enables the collection and distribution of real-time essential data regarding road network *** this scenario,energy-efficient and reliable intercommunication routes are essential among vehicular nodes in sustainable urban *** this motivation,the current research article presents a new Artificial Intelligence-based Energy Efficient Clustering with Routing(AI-EECR)Protocol for IoV in urban *** proposed AI-EECR protocol operates under three stages namely,network initialization,Cluster Head(CH)selection,and routing *** presented AI-EECR protocol determines the CHs from vehicles with the help of Quantum Chemical Reaction Optimization(QCRO)*** a fitness function with the help of vehicle speed,trust level,and energy level of the *** order to make appropriate routing decisions,a set of relay nodeswas selected usingGroup Teaching Optimization Algorithm(GTOA).The performance of the presented AI-EECR model,in terms of energy efficiency,was validated against different aspects and a brief comparative analysis was *** experimental outcomes established that AI-EECR model outperformed the existing methods under different measures.

关键词： Vehicular communication internet of vehicles energy efficient smart transportation smart city urban computing

PGSLM:Edge-Enabled Probabilistic Graph Structure Learning Model for Traffic Forecasting in internet of vehicles

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China Communications 2023年第4期20卷 270-286页

作者： Xiaozhu Liu Jiaru Zeng Rongbo Zhu Hao Liu School of Automation Wuhan University of TechnologyWuhan 430070China College of Informatics Huazhong Agricultural UniversityWuhan 430070China

With the rapid development of the 5G communications,the edge intelligence enables internet of vehicles(IoV)to provide traffic forecasting to alleviate traffic congestion and improve quality of experience of users *** enhance the forecasting performance,a novel edge-enabled probabilistic graph structure learning model(PGSLM)is proposed,which learns the graph structure and parameters by the edge sensing information and discrete probability distribution on the edges of the traffic road *** obtain the spatio-temporal dependencies of traffic data,the learned dynamic graphs are combined with a predefined static graph to generate the graph convolution part of the recurrent graph convolution *** the training process,a new graph training loss is introduced,which is composed of the K nearest neighbor(KNN)graph constructed by the traffic feature tensors and the graph *** experimental results show that,compared with existing models,the proposed PGSLM improves the traffic prediction performance in terms of average absolute error and root mean square error in IoV.

关键词： edge computing traffic forecasting graph convolutional network graph structure learning internet of vehicles

Communication-Efficient Decision-Making of Digital Twin Assisted internet of vehicles: A Hierarchical Multi-Agent Reinforcement Learning Approach

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China Communications 2023年第3期20卷 55-68页

作者： Xiaoyuan Fu Quan Yuan Shifan Liu Baozhu Li Qi Qi Jingyu Wang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an 710071China internet of Things&Smart City Innovation Platform Zhuhai Fudan Innovation InstituteZhuhai 519000China State Key Laboratory of High-End Server and Storage Technology Jinan 250101China

The connected autonomous vehicle is considered an effective way to improve transport safety and *** overcome the limited sensing and computing capabilities of individual vehicles,we design a digital twin assisted decision-making framework for internet of vehicles,by leveraging the integration of communication,sensing and *** this framework,the digital twin entities residing on edge can effectively communicate and cooperate with each other to plan sub-targets for their respective vehicles,while the vehicles only need to achieve the sub-targets by generating a sequence of atomic ***,we propose a hierarchical multiagent reinforcement learning approach to implement the framework,which can be trained in an end-to-end *** the proposed approach,the communication interval of digital twin entities could adapt to timevarying *** experiments on driving decision-making have been performed in traffic junction scenarios of different *** experimental results show that the proposed approach can largely improve collaboration efficiency while reducing communication overhead.

关键词： digital twin internet of vehicles hierar-chical reinforcement learning

Blockchain-Enabled Federated Learning with Differential Privacy for internet of vehicles

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Computers, Materials & Continua 2024年第10期81卷 1581-1593页

作者： Chi Cui Haiping Du Zhijuan Jia Yuchu He Lipeng Wang School of Information Science and Technology Zhengzhou Normal UniversityZhengzhou450044China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2500Australia

The rapid evolution of artificial intelligence(AI)technologies has significantly propelled the advancement of the internet of vehicles(IoV).With AI support,represented by machine learning technology,vehicles gain the capability to make intelligent *** a distributed learning paradigm,federated learning(FL)has emerged as a preferred solution in *** to traditional centralized machine learning,FL reduces communication overhead and improves privacy *** these benefits,FL still faces some security and privacy concerns,such as poisoning attacks and inference attacks,prompting exploration into blockchain integration to enhance its security *** paper introduces a novel blockchain-enabled federated learning(BCFL)scheme with differential privacy(DP)tailored for *** order to meet the performance demanding IoV environment,the proposed methodology integrates a consortium blockchain with Practical Byzantine Fault Tolerance(PBFT)consensus,which offers superior efficiency over the conventional public *** addition,the proposed approach utilizes the Differentially Private Stochastic Gradient Descent(DP-SGD)algorithm in the local training process of FL for enhanced privacy *** results indicate that the integration of blockchain elevates the security level of FL in that the proposed approach effectively safeguards FL against poisoning *** the other hand,the additional overhead associated with blockchain integration is also limited to a moderate level to meet the efficiency criteria of ***,by incorporating DP,the proposed approach is shown to have the(ε-δ)privacy guarantee while maintaining an acceptable level of model *** enhancement effectively mitigates the threat of inference attacks on private information.

关键词： Blockchain federated learning differential privacy internet of vehicles

Deep Transfer Learning Techniques in Intrusion Detection System-internet of vehicles: A State-of-the-Art Review

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Computers, Materials & Continua 2024年第8期80卷 2785-2813页

作者： Wufei Wu Javad Hassannataj Joloudari Senthil Kumar Jagatheesaperumal Kandala N.V.P.SRajesh Silvia Gaftandzhieva Sadiq Hussain Rahimullah Rabih Najibullah Haqjoo Mobeen Nazar Hamed Vahdat-Nejad Rositsa Doneva School of Information Engineering Nanchang UniversityNanchang330031China Department of Computer Engineering Technical and Vocational University(TVU)Tehran***Iran Department of Computer Engineering University of BirjandBirjand***Iran Department of Computer Engineering Babol BranchIslamic Azad UniversityBabol***Iran Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005India School of Electronics Engineering VIT-AP UniversityNear Vijayawada522237India Faculty of Mathematics and Informatics University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria Examination Branch Dibrugarh UniversityDibrugarh786004India Department of Computer Engineering Faculty of EngineeringUniversity of BirjandBirjand***Iran Faculty of Electrical and Computer Engineering University of BirjandBirjand***Iran Malaysian Institute of Information Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Physics and Technology University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria

The high performance of IoT technology in transportation networks has led to the increasing adoption of internet of vehicles(IoV)*** functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic *** these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle *** paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the internet of vehicles(IDS-IoV)based on anomaly ***-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by *** systems can autonomously create specific models based on network data to differentiate between regular traffic and *** these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational *** evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and *** review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV *** examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

关键词： Cyber-attacks internet of things internet of vehicles intrusion detection system

Deep Learning-Based Prediction of Traffic Accidents Risk for internet of vehicles

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China Communications 2022年第2期19卷 214-224页

作者： Haitao Zhao Xiaoqing Li Huiling Cheng Jun Zhang Qin Wang Hongbo Zhu School of Communication and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Electronic and Information Engineering The Hong Kong Polytechnic UniversityHong KongChina

With the increasing number of vehicles,traffic accidents pose a great threat to human ***,aiming at reducing the occurrence of traffic accidents,this paper proposes an algorithm based on a deep convolutional neural network and a random forest to predict accident ***,the proposed algorithm includes a feature extractor and a feature classifier,where the former extracts key features using a convolutional neural network and the latter outputs a probability value of traffic accidents using a random forest with multiple decision trees,which indicates the degree of accident *** show that the proposed algorithm can achieve higher performance in terms of the Area Under the Curve(AUC)of the Receiver Characteristic Operator as well as accuracy than the existing algorithms based on the Adaboost or the pure convolutional neural networks.

关键词： road safety risk prediction internet of vehicles