On the Inherent Resilience of Task-Oriented V2X Networks to Content-Selection Errors
Luca Lusvarghi, Javier Gozalvez

TL;DR
This paper analyzes how task-oriented V2X networks inherently withstand content-selection errors, ensuring relevant information delivery despite high relevance estimation inaccuracies, which is crucial for large-scale connected vehicle deployment.
Contribution
It reveals the inherent resilience of task-oriented V2X networks to relevance estimation errors and identifies fundamental conditions enabling this robustness.
Findings
Resilience guarantees relevant information delivery under high error conditions.
Fundamental conditions for resilience are identified.
Analysis applies to other task-oriented networks with similar content selection.
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…
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Taxonomy
TopicsVehicular Ad Hoc Networks (VANETs) · Age of Information Optimization · Opportunistic and Delay-Tolerant Networks
