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

Wanru Xu, Xiaochen Fan, Tao Wu, Yue Xi, Panlong Yang, Chang Tian

Abstract: 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.