Towards Efficient Learning Using Double-Layered Federation Based on Traffic Density for Internet of Vehicles

Hu, Xiaolin; Wang, Guanghui; Jiang, Lei; Ding, Shuang; Xia, He

doi:10.1007/978-3-030-87571-8_25

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…Jiang et al present a model of the outward transmission of vehicle blockchain data and then give detailed theoretical analysis and numerical results [ 20 ]. Hu et al present a novel double-layered federation model to analyze the performance of the data processing system of Internet of vehicles [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Performance Analysis of STEP System in Internet of Vehicles Based on Queuing Theory

Wang

2022

Computational Intelligence and Neuroscience

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The Internet of vehicles (IoV) is an important research area of the intelligent transportation systems using Internet of things theory. The complex event processing technology is a basic issue for processing the data stream in IoV. In recent years, many researchers process the temporal and spatial data flow by complex event processing technology. Spatial Temporal Event Processing (STEP) is a complex event query language focusing on the temporal and spatial data flow in Internet of vehicles. There are four processing models of the event stream processing system based on the complex event query language: finite automata model, matching tree model, directed acyclic graph model, and Petri net model. In addition, the worst-case response time of the event stream processing system is an important indicator of evaluating the performance of the system. Firstly, this paper proposed a core algorithm of the temporal and spatial event stream processing program based on STEP by Petri net model. Secondly, we proposed a novel method to estimate the worst-case response time of the event stream processing system, which is based on stochastic Petri net and queuing theory. Finally, through the simulation experiment based on queuing theory, this paper proves that the data stream processing system based on STEP has good dynamic performance in processing the spatiotemporal data stream in Internet of vehicles.

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Section: Introductionmentioning

confidence: 99%

Dynamic Performance Analysis of STEP System in Internet of Vehicles Based on Queuing Theory

Wang

2022

Computational Intelligence and Neuroscience

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“…Research on traffic flow optimization, particularly concerning roundabouts, predominantly concentrates on evaluating the efficacy of novel assessment techniques integrating intelligent technologies into road infrastructure to enhance mobility [100][101][102][103][104][105][106][107][108][109]. In this regard, Elmanaa et al [100] developed a compact system for real-time processing of camera images, showing promise when applied to a three-leg roundabout for traffic surveillance, especially in resource-limited settings.…”

Section: Optimization Of Traffic Flow At Roundabouts In the Transitio...mentioning

confidence: 99%

“…Despite being validated in urban roundabouts, further research is needed to ensure broader applicability. Meanwhile, the authors in [109] introduced a novel technique enhancing learning efficiency based on traffic density in a roundabout. Simulations using real-world data demonstrated enhancements in Internet of Vehicles technology and privacy preservation in high-traffic scenarios.…”

Section: Optimization Of Traffic Flow At Roundabouts In the Transitio...mentioning

confidence: 99%

Insights into Simulated Smart Mobility on Roundabouts: Achievements, Lessons Learned, and Steps Ahead

Tumminello,

Macioszek,

Granà

2024

Sustainability

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This paper explores the domain of intelligent transportation systems, specifically focusing on roundabouts as potential solutions in the context of smart mobility. Roundabouts offer a safer and more efficient driving environment compared to other intersections, thanks to their curvilinear trajectories promoting speed control and lower vehicular speeds for traffic calming. The synthesis review supported the authors in presenting current knowledge and emerging needs in roundabout design and evaluation. A focused examination of the models and methods used to assess safety and operational performance of roundabout systems was necessary. This is particularly relevant in light of new challenges posed by the automotive market and the influence of vehicle-to-vehicle communication on the conceptualization and design of this road infrastructure. Two case studies of roundabouts were analyzed in Aimsun to simulate the increasing market penetration rates of connected and autonomous vehicles (CAVs) and their traffic impacts. Through microscopic traffic simulation, the research evaluated safety and performance efficiency advancements in roundabouts. The paper concludes by outlining areas for further research and evolving perspectives on the role of roundabouts in the transition toward connected and autonomous vehicles and infrastructures.

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“…Furthermore, Lu et al in [10] designed a blockchain empowered FL architecture to achieve secure data sharing without heavy transmission load and privacy risks. Hu et al in [28] build a two-layer federated structure by coordinating multiple RSUs, which reduces the computational overhead of traffic flow and protects data privacy during model training.…”

Section: B Federated Learning For Internet Of Vehiclesmentioning

confidence: 99%

Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition

Xie¹,

Zhang²,

Li³

et al. 2022

Preprint

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With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information. To address this issue, we first exploit privacypreserving federated learning to perform collaborative training for accurate recognition models without sharing raw traffic sign data. Nevertheless, due to the limited computing and energy resources of most devices, it is hard for vehicles to continuously undertake complex artificial intelligence tasks. Therefore, we introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training, which is the next generation of neural networks and is practical and well-fitted to IoV scenarios. Furthermore, we design a novel encoding scheme for SNNs based on neuron receptive fields to extract information from the pixel and spatial dimensions of traffic signs to achieve high-accuracy training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.

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Towards Efficient Learning Using Double-Layered Federation Based on Traffic Density for Internet of Vehicles

Cited by 8 publications

References 19 publications

Dynamic Performance Analysis of STEP System in Internet of Vehicles Based on Queuing Theory

Dynamic Performance Analysis of STEP System in Internet of Vehicles Based on Queuing Theory

Insights into Simulated Smart Mobility on Roundabouts: Achievements, Lessons Learned, and Steps Ahead

Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition

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