Enhancing Security and Privacy in Vehicular Networks

Al-Shareeda, Sarah; Ozguner, Fusun

i

Enhancing Security and Privacy in Vehicular Networks

2026-02-24
By Al-Shareeda, Sarah ; Ozguner, Fusun

File Language:

English

Details

Creators:

Al-Shareeda, Sarah ; Ozguner, Fusun
Corporate Creators:

Ohio State University. Center for Automotive Research
Corporate Contributors:

Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC) ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:

Autonomous Vehicle Safety Cellular Networks Computer Security Connected Vehicles Decision Making Digital Twins Edge Computing Evaluation Infrastructure Intelligent Transportation Systems Learning Mobility Reinforcement Learning Task Offloading Transportation Safety Uncertainty Quantification Vehicle Safety Vehicle To Everything Communications Vehicle To Vehicle Communications
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report: (Aug ‘23 to Dec ‘25)
Corporate Publisher:

Center for Automated Vehicles Research with Multimodal Assured Navigation (CARMEN+) Tier-1 University Transportation Center (UTC)
Abstract:

Vehicle-to-Everything (V2X) communication underpins Highly Automated Transportation Systems (HATS), yet scalability is constrained by three coupled challenges: (i) authentication overhead in latency-sensitive communication, (ii) mobility-aware computation offloading under dynamic wireless and infrastructure conditions, and (iii) safety-critical decision-making under behavioral uncertainty. Existing architectures often treat security, offloading, synchronization, and control as loosely coupled components, leading to systematic latency underestimation and degraded responsiveness in dense Cellular-V2X (C-V2X) environments. This work advances an integrated cyber-layer intelligence framework evaluated through three coordinated tracks that explicitly couple authentication, mobility, synchronization, and learning within a unified decision loop.
Format:

PDF
Funding:

69A3552348327
Collection(s):

University Transportation Centers
Main Document Checksum:

urn:sha-512:7a42c73fa6543b93cd8e5e8f8a5eb0c75d156902b96790d4dbc5285f9bfdc5cb1be08a8ee8db799c29c9d08a7f3927ba158a42c89ab0386b354d730e478b848f
Download URL:

https://rosap.ntl.bts.gov/view/dot/89270/dot_89270_DS1.pdf
File Type:

[PDF - 1.25 MB ]

File Language:

English

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