Vehicular users are emerging as a prime market for tar- geted 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.

Scheduling Advertisement Delivery in Vehicular Networks / Einziger, Gil; Chiasserini, Carla Fabiana; Malandrino, Francesco. - In: IEEE TRANSACTIONS ON MOBILE COMPUTING. - ISSN 1536-1233. - STAMPA. - 17:12(2018), pp. 2882-2897. [10.1109/TMC.2018.2829517]

Scheduling Advertisement Delivery in Vehicular Networks

Gil Einziger;Carla-Fabiana Chiasserini;Francesco Malandrino
2018

Abstract

Vehicular users are emerging as a prime market for tar- geted 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2705644
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