STEIM: A Spatiotemporal Event Interaction Model in V2X Systems Based on a Time Period and a Raster Map

Xu, Cheng; Luo, Hengjie; Bao, Hong; Wang, Pengfei

doi:10.1155/2020/1375426

Cited by 10 publications

(10 citation statements)

References 45 publications

(52 reference statements)

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“…ere are still many areas worthy of improvement. Xu et al [21] proposed a spatiotemporal event interaction model in the V2X system based on time periods and grid maps, which promoted the application of this technology in V2X scenarios.…”

Section: Related Workmentioning

confidence: 99%

AFLPC: An Asynchronous Federated Learning Privacy-Preserving Computing Model Applied to 5G-V2X

Huang

et al. 2022

Security and Communication Networks

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Federated learning can effectively protect local data privacy in 5G-V2X environment and ensure data protection in Internet of vehicles environment. The advantages of low delay of 5G network should be better utilized in the vehicle-road cooperative system. But the existing asynchronous federated learning obtains a local model through different node training and completes the update of the global model through the central server. There are problems such as single point of failure, privacy leakage, and deviation of aggregation parameters. In response to the above problems, we proposed a 5G-V2X-oriented asynchronous federated learning privacy-preserving computing model (AFLPC). We used an adaptive differential privacy mechanism to reduce noise while protecting data privacy. A weight-based asynchronous federated learning aggregation update method is proposed to reasonably control the proportion of parameters submitted by users with different training speeds in the aggregation parameters and actively update the aggregation parameters of lagging users, so as to effectively reduce the negative impact on the model caused by the different speed of finding you. Experiments show that the proposed method can effectively ensure the credibility and privacy of asynchronous federated learning in 5G-V2X scenarios and at the same time improve the utility of the model.

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Section: Related Workmentioning

confidence: 99%

AFLPC: An Asynchronous Federated Learning Privacy-Preserving Computing Model Applied to 5G-V2X

Huang

et al. 2022

Security and Communication Networks

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“…Li and Chen have proposed a temporal and spatial constraints event query language STEP, which can effectively describe the Temporal and Spatial constraint information of Internet of vehicles [ 16 ]. Xu et al have proposed a spatiotemporal event interaction model STEIM to express the spatiotemporal information of the spatiotemporal event flow in the V2X [ 17 ].…”

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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“…The goal of the 5G-V2X communication system is to achieve accurate and efficient road scene perception and accident-free and efficient collaborative autonomous driving [30]. Literature [31][32][33] proposed a network protocol based on edge computing and a new vehicle network privacy protection protocol to enhance road safety, intelligent transportation, and smart city systems. Literature [34] introduced a real-time communication method based on 5G-V2X, which reduces the energy and time costs of the system and improves the management efficiency of vehicular networks.…”

Section: Clustering Andmentioning

confidence: 99%

A Real-Time Complex Road AI Perception Based on 5G-V2X for Smart City Security

Yi-nong

et al. 2022

Wireless Communications and Mobile Computing

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The Internet of Vehicles and information security are key components of a smart city. Real-time road perception is one of the most difficult tasks. Traditional detection methods require manual adjustment of parameters, which is difficult, and is susceptible to interference from object occlusion, light changes, and road wear. Designing a robust road perception algorithm is still challenging. On this basis, we combine artificial intelligence algorithms and the 5G-V2X framework to propose a real-time road perception method. First, an improved model based on Mask R-CNN is implemented to improve the accuracy of detecting lane line features. Then, the linear and polynomial fitting methods of feature points in different fields of view are combined. Finally, the optimal parameter equation of the lane line can be obtained. We tested our method in complex road scenes. Experimental results show that, combined with 5G-V2X, this method ultimately has a faster processing speed and can sense road conditions robustly under various complex actual conditions.

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STEIM: A Spatiotemporal Event Interaction Model in V2X Systems Based on a Time Period and a Raster Map

Cited by 10 publications

References 45 publications

AFLPC: An Asynchronous Federated Learning Privacy-Preserving Computing Model Applied to 5G-V2X

AFLPC: An Asynchronous Federated Learning Privacy-Preserving Computing Model Applied to 5G-V2X

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

A Real-Time Complex Road AI Perception Based on 5G-V2X for Smart City Security

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