Application Design of Intelligent Internet of Vehicles under 5G Communication Technology
Keywords:
5G, Internet of Vehicles, Big data, Sensor, WebsiteAbstract
The rapid promotion of 5G communication technology with the landing and popularization of the application of the Internet of Animals, the Internet of Vehicles as the industry field with the highest maturity and the largest number of connections in the high-speed field of the Internet of Things, the Internet of Vehicles industry has rapidly penetrated, and the industry scale continues to expand. In the Internet of vehicles, due to the mobility of the vehicle itself, on-board vehicular communication has the characteristics of limited mobile area, fast network topology change, frequent access and interruption of the network, large coverage of nodes, and complex communication environment, which brings many difficulties to the implementation of the Internet of vehicles. In view of the above problems, this paper designs an intelligent vehicle networking system integrating sensor-5G communication module system and big data analysis and processing system. The sensor-5G communication module can obtain the sensor data and upload it to the team butler background. The big data analysis and processing system processes, analyzes, manages and tracks the relevant data by monitoring the vehicle data. The application of this system promotes the interconnection between cars and people, and improves the vehicle management efficiency of governments and enterprises.
References
Zhang, X. (2024). Research on Dynamic Adaptation of Supply and Demand of Power Emergency Materials based on Cohesive Hierarchical Clustering. Innovation & Technology Advances, 2(2), 59–75. https://doi.org/10.61187/ita.v2i2.135
Tang, Z., Feng, Y., Zhang, J., & Wang, Z. (2026). SVD-BDRL: A trustworthy autonomous driving decision framework based on sparse voxels and blockchain enhancement. Alexandria Engineering Journal, 134, 433-446.
Xie, J., Zhang, L., Cheng, L., Yao, J., Qian, P., Zhu, B., & Liu, G. (2025). MARNet: Multi-scale adaptive relational network for robust point cloud completion via cross-modal fusion. Information Fusion, 103505.
Deng, X., & Yang, J. (2025, August). Multi-Layer Defense Strategies and Privacy Preserving Enhancements for Membership Reasoning Attacks in a Federated Learning Framework. In 2025 5th International Conference on Computer Science and Blockchain (CCSB) (pp. 278-282). IEEE.
Lu, K., Sui, Q., Chen, X., & Wang, Z. (2025). NeuroDiff3D: a 3D generation method optimizing viewpoint consistency through diffusion modeling. Scientific Reports, 15(1), 41084.
Tu, Tongwei. "AutoNetTest: A Platform-Aware Framework for Intelligent 5G Network Test Automation and Issue Diagnosis." (2025).
Lin, Z., Liu, X., Xiang, Y., & Hong, Y. (2025). Modeling multivariate degradation data with dynamic covariates under a Bayesian framework. Reliability Engineering & System Safety, 111115.
Zhu, Bingxin. "ReliBridge: Scalable LLM-Based Backbone for Small Business Platform Stability." (2025).
Jiang, B., Shi, L., Lin, Z., Stowe, L., & Guo, F. (2025). Perception Characteristics Distance: Measuring Stability and Robustness of Perception System in Dynamic Conditions under a Certain Decision Rule. arXiv preprint arXiv:2506.09217.
Xie, Minhui, and Boyan Liu. "EvalNet: Sentiment Analysis and Multimodal Data Fusion for Recruitment Interview Processing." (2025).
Zhang, Yuhan. "Learning to Advertise: Reinforcement Learning for Automated Ad Campaign Optimization for Small Businesses." (2025).
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