Scheduling Advertisement Delivery in Vehicular Networks

TL;DR

This paper introduces Volfied, a low-complexity algorithm for ad selection in vehicular networks that significantly increases broker revenue by balancing relevance and conflict avoidance.

Contribution

The paper proposes Volfied, a novel algorithm for conflict-free ad selection in vehicular networks, with proven low complexity and up to 70% revenue improvement.

Findings

Volfied increases broker revenue by up to 70%.

Volfied operates with very low computational complexity.

Performance validated using real-world vehicular traces.

Abstract

Vehicular users are emerging as a prime market for targeted advertisement, where advertisements (ads) are sent from network points of access to vehicles, and displayed to passengers only if they are relevant to them. In this study, we take the viewpoint of a broker managing the advertisement system, and getting paid every time a relevant ad is displayed to an interested user. The broker selects the ads to broadcast at each point of access so as to maximize its revenue. In this context, we observe that choosing the ads that best fit the users' interest could actually hurt the broker's revenue. In light of this conflict, we present Volfied, an algorithm allowing for conflict-free, near-optimal ad selection with very low computational complexity. Our performance evaluation, carried out through real-world vehicular traces, shows that Volfied increases the broker revenue by up to 70% with provably low computational complexity, compared to state-of-the-art alternatives.