TY - JOUR
T1 - Scheduling Advertisement Delivery in Vehicular Networks
AU - Einziger, Gil
AU - Chiasserini, Carla Fabiana
AU - Malandrino, Francesco
N1 - Funding Information:
This work is supported by the European Commission through the H2020 5G-TRANSFORMER project (Project ID 761536) and by the Fondazione CRT under the initiative La Ricerca dei Talenti, through the MIMOSE project.
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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 percent with provably low computational complexity, compared to state-of-the-art alternatives.
AB - 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 percent with provably low computational complexity, compared to state-of-the-art alternatives.
KW - Vehicular ad hoc networks
KW - mobile advertising
UR - http://www.scopus.com/inward/record.url?scp=85045750172&partnerID=8YFLogxK
U2 - 10.1109/TMC.2018.2829517
DO - 10.1109/TMC.2018.2829517
M3 - Article
AN - SCOPUS:85045750172
SN - 1536-1233
VL - 17
SP - 2882
EP - 2897
JO - IEEE Transactions on Mobile Computing
JF - IEEE Transactions on Mobile Computing
IS - 12
M1 - 8345228
ER -