Interest Users Cumulatively in Your Ads: A Near Optimal Study for Wi-Fi Advertisement Scheduling

• Published in: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) pp. 1 - 6
• Main Authors: Xu, Wanru, Fan, Xiaochen, Wu, Tao, Xi, Yue, Yang, Panlong, Tian, Chang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 10.05.2021
• Subjects: Approximation algorithms; Conferences; Greedy Swap; Integer programming; Internet; Mobile Advertisement Scheduling; Partitioning algorithms; Scheduling; Simulation; Wi-Fi Advertising
• DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484633

Public Wi-Fi hotspots are essential in most venues and carry over 50% of last-mile Internet traffic for mobile users. Venue owners and mobile advertisers are employing various technologies to promote Wi-Fi monetization via broadcasting advertisements to users. In this paper, we study Wi-Fi advertisement scheduling by considering the communication constraints of Wi-Fi hotspots and the users' tolerance for viewing advertisements. We build a tripartite Wi-Fi advertising model with venues, advertisers and mobile users, and then formulate the Wi-Fi advertisement scheduling process as an optimization problem to maximize users' overall interest value. The formulated problem is a complex mixed integer programming problem. We prove that its objective function is a monotone submodular function subject to a knapsack constraint and a partition matroid constraint, respectively. On this basis, we propose a novel greedy swap algorithm to solve the advertisement scheduling problem with an approximation guarantee of \frac{{1 - {e^{ - 2}}}{2} to the optimal solution. Extensive simulation results validate the superiority of the proposed algorithm which obtains the highest user interests towards all displayed Wi-Fi advertisements, thereby enhancing the potential revenues for both venues and advertisers.