Peer-to-Peer Networking and Applications

, Volume 11, Issue 4, pp 756–765 | Cite as

Connection is power: Near optimal advertisement infrastructure placement for vehicular fogs

  • Wanru Xu
  • Panlong Yang
  • Lijing Jiang
Part of the following topical collections:
  1. Special Issue on Fog Computing on Wheels


Mobile advertisement infrastructure (MAI) becomes popular but is still subjected to the relatively high deployment cost. Previous studies overlook the effective placement of MAIs where the geographical distances among them should be fully respected. In this work, we optimize the placement of MAIs where the maximum distances between MAIs are incorporated, which could be regarded as a kind of connectivity constraint. Vehicular users form a virtual fog to get the mobile advertisements when approaching the MAIs. We investigated the interplay between the connected MAIs and vehicular fogs. We found that such kind of deployment could effectively maximize the number of covered vehicular users, which could be modeled with a submodular set function. Unfortunately, the investigated deployment problem is more complicated than traditional maximum submodular set function problem, e.g. the maximum coverage problem. Because it requires all the MAIs could be “virtually connected” to each other and thereby form a connected network. To address aforementioned challenges, this paper introduces a near optimal algorithm, which incorporates an \(O(\sqrt k)\)-approximation algorithm, where k is the number of mobile advertisement infrastructures. To this end, we make extensive experimental studies using synthetic data. Our results show that the proposed algorithm achieves an improved performance, i.e., 40% for typical multi-hop case (e.g. 4 to 8 hops), than the baseline scheme where random selection is applied. In addition, our results show very close performance comparing with the global optimal results achieved by exhaustive search method.


Mobile advertisement infrastructure Connection constraint Vehicular fogs 



This research is partially supported by China National Funds for Distinguished Young Scientists with No.61625205, Key Research Program of Frontier Sciences, CAS, No. QYZDY-SSW-JSC002, NSF OF Jiangsu For Distinguished Young Scientist: BK20150030, NSFC with No. 61632010, 61232018, 61371118, 61402009, 61672038, 61520106007 and NSF ECCS-1247944, NSF CMMI 1436786, and NSF CNS 1526638.


  1. 1.
    Allesina S, Tang S (2012) Stability criteria for complex ecosystems. Nature 483(7388):205–208CrossRefGoogle Scholar
  2. 2.
    Alpaydin E (2014) Introduction to machine learning. MIT press, CambridgezbMATHGoogle Scholar
  3. 3.
    Bart Y, Stephen AT, Sarvary M (2014) Which products are best suited to mobile advertising? A field study of mobile display advertising effects on consumer attitudes and intentions. J Mark Res 51(3):270–285CrossRefGoogle Scholar
  4. 4.
    Borgs C, Chayes J, Immorlica N, Jain K, Etesami O, Mahdian M (2007) Dynamics of bid optimization in online advertisement auctions. In: Proceedings of the 16th international conference on World Wide Web. ACM, pp 531–540Google Scholar
  5. 5.
    Chon Y, Lane ND, Kim Y, Zhao F, Cha H (2013) Understanding the coverage and scalability of place-centric crowdsensing. In: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing. ACM, pp 3–12Google Scholar
  6. 6.
    Dhar S, Varshney U (2011) Challenges and business models for mobile location-based services and advertising. Commun ACM 54(5):121–128CrossRefGoogle Scholar
  7. 7.
    Fu Z, Ren K, Shu J, Sun X, Huang F (2016) Enabling personalized search over encrypted outsourced data with efficiency improvement. IEEE Trans Parallel Distrib Syst 27(9):2546–2559CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Nemhauser G, Wolsey L, Fisher M (1978). In: An analysis of approximations for maximizing submodular set functions-I, vol 14, pp 265–294Google Scholar
  10. 10.
    Gupta H, Zhou Z, Das SR, Gu Q (2006) Connected sensor cover: Self-organization of sensor networks for efficient query execution. IEEE/ACM Trans Networking (ToN) 14(1):55–67CrossRefGoogle Scholar
  11. 11.
    Khuller S, Moss A, Naor JS (1999) The budgeted maximum coverage problem. Inf Process Lett 70 (1):39–45MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Kong Y, Zhang M, Ye D (2017) A belief propagation-based method for task allocation in open and dynamic cloud environments. Knowl.-Based Syst 115:123–132CrossRefGoogle Scholar
  13. 13.
    Krause A, Guestrin C (2005). In: A note on the budgeted maximization of submodular functions, vol Carnegie Mellon University, pp 265–294Google Scholar
  14. 14.
    Krause A, Guestrin C, Gupta A, Kleinberg J (2006) Near-optimal sensor placements: Maximizing information while minimizing communication cost. In: Proceedings of the 5th international conference on Information processing in sensor networks, ACM, pp 2–10Google Scholar
  15. 15.
    Kuipers L, Niederreiter H (2012) Uniform distribution of sequences. Courier CorporationGoogle Scholar
  16. 16.
    Kuo TW, Lin KCJ, Tsai MJ (2015) Maximizing submodular set function with connectivity constraint: Theory and application to networks. IEEE/ACM Trans Networking (TON) 23(2):533–546CrossRefGoogle Scholar
  17. 17.
    Michalski RS, Carbonell JG, Mitchell TM (2013) Machine learning: An artificial intelligence approach. Springer Science & Business MediaGoogle Scholar
  18. 18.
    Mini S, Udgata SK, Sabat SL (2014) Sensor deployment and scheduling for target coverage problem in wireless sensor networks. IEEE Sensors J 14(3):636–644CrossRefGoogle Scholar
  19. 19.
    Niu F, Zhang C, Ré C, Shavlik J (2012) Elementary: Large-scale knowledge-base construction via machine learning and statistical inference. Int J Semant Web Inf Syst (IJSWIS) 8(3): 42–73CrossRefGoogle Scholar
  20. 20.
    Qin J, Zhu H, Zhu Y, Lu L, Xue G, Li M (2014) Post: Exploiting dynamic sociality for mobile advertising in vehicular networks. In: INFOCOM, 2014 Proceedings IEEE. IEEE, pp 1761–1769Google Scholar
  21. 21.
    Sołtysik-Piorunkiewicz A (2013) The development of mobile internet technology and ubiquitous communication in a knowledge-based organization. J Appl Knowl Manag 1:29–41Google Scholar
  22. 22.
    Srinivasan K, Shamos MI (2010) Determining the effectiveness of internet advertising. US Patent 7,747,465Google Scholar
  23. 23.
    Sviridenko M (2004). In: A note on maximizing a submodular set function subject to a knapsack constraint, vol 32, pp 32–43Google Scholar
  24. 24.
    Tinghuai M, Jinjuan Z, Meili T, Yuan T, Abdullah AD, Mznah AR, Sungyoung L (2015) Social network and tag sources based augmenting collaborative recommender system. IEICE Trans Inf Syst 98(4):902–910Google Scholar
  25. 25.
    Wang C, Tang S, Yang L, Guo Y, Li F, Jiang C (2014) Modeling data dissemination in online social networks: a geographical perspective on bounding network traffic load. In: Proceedings of the 15th ACM international symposium on Mobile ad hoc networking and computing. ACM, pp 53–62Google Scholar
  26. 26.
    Wen J, Zhang S, Li T (2014) Overview on developing status quo and trend of mobile internet technology. Commun Technol 47:977–984Google Scholar
  27. 27.
    Xiao M, Wu J, Huang L (2014) Community-aware opportunistic routing in mobile social networks. IEEE Trans Comput 63(7):1682–1695MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Xu W, Liu R, Yang P, Chen X, Zhang M, Xu Y, Sheng P (2016) emap: Efficient user selection for mobile advertisement popularization. In: Vehicular Technology Conference (VTC Spring), 2016 IEEE 83rd. IEEE, pp 1–5Google Scholar
  29. 29.
    Zhang Y, Sun X, Wang B (2016) Efficient algorithm for k-barrier coverage based on integer linear programming. China Commun 13(7):16–23CrossRefGoogle Scholar
  30. 30.
    Zhangjie F, Xingming S, Qi L, Lu Z, Jiangang S (2015) Achieving efficient cloud search services: multi-keyword ranked search over encrypted cloud data supporting parallel computing. IEICE Trans Commun 98 (1):190–200Google Scholar
  31. 31.
    Zheng H, Wu J (2015) Optimizing roadside advertisement dissemination in vehicular cyber-physical systems. In: Distributed Computing Systems (ICDCS), 2015 IEEE 35th International Conference on. IEEE, pp 41–50Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.College of Communications EngineeringPLA University of Science and TechnologyNanjingChina
  2. 2.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina
  3. 3.Nangjing University of Posts and TelecommunicationsNanjingChina

Personalised recommendations