ZTE Communications ›› 2021, Vol. 19 ›› Issue (2): 29-43.doi: 10.12142/ZTECOM.202102005

• Special Topic • Previous Articles     Next Articles

Maximum-Profit Advertising Strategy Using Crowdsensing Trajectory Data

LOU Kaihao1(), YANG Yongjian1, YANG Funing1, ZHANG Xingliang2   

  1. 1.Jilin University, Changchun 130012, China
    2.China Mobile Group Jilin Co. , Ltd. , Changchun 130021, China
  • Received:2021-03-11 Online:2021-06-25 Published:2021-07-27
  • About author:LOU Kaihao (loukh20@mails.jlu.edu.cn) received the B.E. degree in software engineering from Jilin University, China in 2017, M.S. degree in computer science and technology from Jilin University in 2020. He is currently pursuing the Ph.D. degree in computer science and technology at Jilin University. His current research focuses on mobile crowdsensing and multi-agent reinforcement learning.|YANG Yongjian received his B.E. degree in automatization from Jilin University of Technology, China in 1983, M.E. degree in computer communication from Beijing University of Post and Telecommunications, China 1991, and Ph.D. in software and theory of computer from Jilin University, China in 2005. He is currently a professor and a Ph.D. supervisor at Jilin University, the Vice Dean of the Software College of Jilin University, Director of Key lab under the Ministry of Information Industry, Standing Director of the Communication Academy, and a member of the Computer Science Academy of Jilin Province. His research interests include network intelligence management, wireless mobile communication and services, and wireless mobile communications.|YANG Funing received her B.E. degree in software engineering from Jilin University, China in 2010 and master’s degree from the school of computer science, Beijing University of Posts and Telecommunications, China in 2013. She is currently a teacher at Jilin University, China. Her current research interests include management, mobile crowdsensing, integration and mining of massive traffic data|ZHANG Xingliang received his B.E. degree in software engineering from Jilin University, China in 2010. He is currently an engineer at the Network Management Center of China Mobile Group Jilin Co., Ltd. His current research interests include mobile communication data analysis and big data analysis of 5G.
  • Supported by:
    Jilin Science and Technology Department Key Technology Project(20190304127YY);the National Natural Science Foundations of China(1772230);Natural Science Foundations of Jilin Province(20190201022JC);National Science Key Lab Fund Project(61421010418);Innovation Capacity Building Project of Jilin Province Development and Reform Commission(2020C017-2);Changchun Science and Technology Development Project(18DY005);Key Laboratory of Defense Science and Technology Foundations(61421010418);Jilin Province Young Talents Lifting Projec(3D4196993421);A conference version of the paper appeared in Proceedings of SocialSec[2]


Out-door billboard advertising plays an important role in attracting potential customers. However, whether a customer can be attracted is influenced by many factors, such as the probability that he/she sees the billboard, the degree of his/her interest, and the detour distance for buying the product. Taking the above factors into account, we propose advertising strategies for selecting an effective set of billboards under the advertising budget to maximize commercial profit. By using the data collected by Mobile Crowdsensing (MCS), we extract potential customers’ implicit information, such as their trajectories and preferences. We then study the billboard selection problem under two situations, where the advertiser may have only one or multiple products. When only one kind of product needs advertising, the billboard selection problem is formulated as the probabilistic set coverage problem. We propose two heuristic advertising strategies to greedily select advertising billboards, which achieves the expected maximum commercial profit with the lowest cost. When the advertiser has multiple products, we formulate the problem as searching for an optimal solution and adopt the simulated annealing algorithm to search for global optimum instead of local optimum. Extensive experiments based on three real-world data sets verify that our proposed advertising strategies can achieve the superior commercial profit compared with the state-of-the-art strategies.

Key words: billboard advertising, mobile Crowdsensing, probabilistic set coverage problem, simulated annealing, optimization problem