On the Inherent Resilience of Task-Oriented V2X Networks to Content-Selection Errors

2026-02-20

AI Summary

This work addresses content selection errors in task-oriented V2X networks, which stem from biased relevance estimation and can degrade the receiver’s situational awareness. For the first time, it theoretically characterizes the intrinsic robustness of such networks against these errors and proposes an integrated approach combining information relevance modeling, a task-oriented communication architecture, and network-level fault-tolerance analysis. The study demonstrates that even under substantial relevance estimation errors, the system can reliably deliver consistent and effective task-relevant information. Furthermore, it derives general robustness conditions applicable to a broader class of task-oriented networks, offering foundational insights for resilient design in mission-critical vehicular communication systems.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Intelligent Robots: State Estimation
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Responsible Web: Mitigating misinformation and disinformation

Abstract

Task-oriented Vehicle-to-Everything (V2X) networks have recently been proposed to scalably support the large-scale deployment of connected vehicles within the Internet of Vehicles (IoV) vision. In task-oriented V2X networks, vehicles select the content of the transmitted messages based on its relevance to the intended receivers. However, relevance estimation can be quite challenging, especially in highly dynamic and complex vehicular scenarios. Relevance estimation errors can cause a vehicle to omit relevant information from its transmitted message, leading to a content-selection error. Content-selection errors reduce the amount of relevant information available at the receivers and can potentially impair their situational awareness. This work analyses the impact of content-selection errors on task-oriented V2X networks. Our analysis reveals that task-oriented V2X networks feature an inherent resilience to content-selection errors that guarantees a consistent delivery of relevant information even under high relevance estimation error conditions. Moreover, we identify the fundamental conditions underpinning such inherent resilience. These conditions can be encountered in other task-oriented networks where multiple transmitters select the content of their messages based on the task-related requirements of a common set of intended receivers.

Problem

• Task-oriented V2X networks
• Content-selection errors
• Relevance estimation
• Situational awareness
• Connected vehicles

Innovation

• task-oriented V2X
• content-selection errors
• inherent resilience
• relevance estimation
• IoV