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

Self Cite

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.

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Huang

et al. 2022

Self Cite

“…e 5G network has the characteristics of high transmission speed and low transmission delay. It provides a more reliable communication environment for V2X [10]. Aiming at the 5G-V2X-based smart city security perception vehicle detection problem, it proposed an improved vehicle target detection algorithm based on a deep learning target detection network [11].…”

Section: Iot Network Security Based On Machinementioning

confidence: 99%

An Intelligent Security Classification Model of Driver’s Driving Behavior Based on V2X in IoT Networks

Dai

Zhong

et al. 2022

Self Cite

Traffic accidents occur frequently in Internet of Things (IoT) safety system. Traffic accidents are largely caused by drivers’ unsafe driving behaviors in the process of driving. Aiming at the problem of low safety of real-time warning in driving, this paper proposes a model to detect driver behavior. Firstly, according to the driver target detection for positioning, combined with the Pose Estimation to identify the driver in the process of driving a variety of driving behaviors, at the same time, a rating model is built to score drivers’ driving behaviors. Then, by integrating the driver behavior model and evaluation rules, the system can give timely and active warning when the driver makes unsafe behavior in the process of driving. Finally, in the V2X scenario, feedback and presentation are given to users in the form of points. The experimental results show that, in the scenario of Internet of vehicles, the driving behavior rating model can well analyze and evaluate drivers’ driving behaviors, so that drivers can more accurately understand their abnormal driving behaviors and driving scores, which plays a significant role in IoT safety management.

“…e transmitted message contains the public key signature, message validation code, and Input: Blockchain.Data, P 2 Output: Risk R, Network attack status attack (1) ECDSA Dencrypt (Blockchain. Data, P 2 , T) (2) for each Blockchain.Data do (3) Change rate_Block � Blockchain.Data/Old data (4) If the rate_block >0.5 then (5) If is tampered by communities (6) State � 0 (7) End if (8)…”

Section: Node Communication Modementioning

confidence: 99%

“…In recent years, the development of 5G technology has promoted the transformation of the communication data transmission industry and also played a major role in the field of the internet of vehicles. e emerging 5G-V2X and blockchain [5][6][7][8] technology intelligently connects road units in the vehicle road environment, making it easier to build a safe vehicle network platform.…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Based Dangerous Driving Map Data Cognitive Model in 5G-V2X for Smart City Security

Chen

Liu

et al. 2022

Self Cite

The development of 5G network communication has brought technological innovation to smart city communication, making the realization of V2X (vehicle to everything) technology possible. Vehicles wirelessly communicate with other vehicles, sensors, pedestrians, and roadside units, raising data security issues while driving. In order to ensure driving safety, the risk map cognitive model is established with the help of blockchain technology. In this model, the key map data and personal privacy information are encrypted and uploaded to form a blockchain, and the smart contract technology is used for automatic script processing. Then, according to different risk scenarios, cognitive learning is carried out for different risk levels, the cognitive results and corresponding operations are fed back to the intelligent vehicle, and these operations ensure the safe operation of the vehicle according to the intelligent vehicle. Finally, the feasibility of the model was verified by comparing different dangerous scenarios. The experimental results show that this risk cognition model can cognize the data of the intelligent vehicle according to different danger scenarios, and the model can transmit acceleration, deceleration, braking, and other behaviors to the intelligent vehicle to ensure smart city driving safety.