98%
921
2 minutes
20
Several researchers have proposed secure authentication techniques for addressing privacy and security concerns in the fifth-generation (5G)-enabled vehicle networks. To verify vehicles, however, these conditional privacy-preserving authentication (CPPA) systems required a roadside unit, an expensive component of vehicular networks. Moreover, these CPPA systems incur exceptionally high communication and processing costs. This study proposes a CPPA method based on fog computing (FC), as a solution for these issues in 5G-enabled vehicle networks. In our proposed FC-CPPA method, a fog server is used to establish a set of public anonymity identities and their corresponding signature keys, which are then preloaded into each authentic vehicle. We guarantee the security of the proposed FC-CPPA method in the context of a random oracle. Our solutions are not only compliant with confidentiality and security standards, but also resistant to a variety of threats. The communication costs of the proposal are only 84 bytes, while the computation costs are 0.0031, 2.0185 to sign and verify messages. Comparing our strategy to similar ones reveals that it saves time and money on communication and computing during the performance evaluation phase.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098888 | PMC |
http://dx.doi.org/10.3390/s23073543 | DOI Listing |
J Environ Manage
September 2025
Department of Mechanical Engineering, University of Colorado Boulder, 1111 Engineering Drive, Boulder, CO, USA. Electronic address:
This study assesses the performance of the ADMS-Urban dispersion model in estimating 1-h mean nitrogen dioxide (NO) concentrations within the street canyons of Prague. While traditional air quality modeling that relies on sparse data from localized monitoring stations, this approach pioneers the integration of traffic, background, and rooftop sensor network, to archive a more granular validation of model outputs. The results demonstrate robust model performance, with FAC2 values ranging from 0.
View Article and Find Full Text PDFSensors (Basel)
August 2025
WiLab, CNIT/DEI, University of Bologna, 40136 Bologna, Italy.
Vehicle-to-vehicle (V2V) and vehicle-to-network (V2N) communications are two key components of intelligent transport systems (ITSs) that can share spectrum resources through in-band overlay. V2V communication primarily supports traffic safety, whereas V2N primarily focuses on infotainment and information exchange. Achieving reliable V2V transmission alongside high-rate V2N services in resource-constrained, dynamically changing traffic environments poses a significant challenge for resource allocation.
View Article and Find Full Text PDFSensors (Basel)
August 2025
School of Information and Software Engineering, East China Jiaotong University, Nanchang 330013, China.
In the realm of urban vehicular ad hoc networks (VANETs), cross-domain data has constituted a multifaceted amalgamation of information sources, which significantly enhances the accuracy and response speed of traffic prediction. However, the interplay between spatial and temporal heterogeneity will complicate the complexity of geographical locations or physical connections in the data normalization. Besides, the traffic pattern differences incurred by dynamic external factors also bring cumulative and sensitive impacts during the construction of the prediction model.
View Article and Find Full Text PDFACS Eng Au
August 2025
Department of Chemistry Texas A&M University, College Station, Texas 77842, United States.
Corrosion represents a key impediment to the greater adoption of light metal alloys as alternatives to automotive steels in vehicular applications. Thin nanocomposite coatings generate considerable interest for their potential in aluminum alloy corrosion protection, which is challenging due to the lack of conventional protection mechanisms that are available for other metals. Here, we investigate the thickness-dependent corrosion protection afforded to AA 7075 substrates by poly-(ether imide)-based (PEI) coatings.
View Article and Find Full Text PDFSci Rep
August 2025
Key Laboratory of Communication and Network, Dalian University, Dalian, 116622, China.
In highly dynamic vehicular networking scenarios, when Vehicle-to-Infrastructure links and Vehicle-to-Vehicle links share spectrum resources, the traditional distributed resource allocation method lacks global optimization and fails to respond to environmental changes in a timely manner, which leads to low spectral efficiency of the system. A resource allocation method based on federated multi-agent deep reinforcement learning is proposed for Vehicular Networking communication, by fusing Asynchronous Federated Learning (AFL) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Synergistic optimization of resource allocation.
View Article and Find Full Text PDF