POST: Exploiting Dynamic Sociality for Mobile Advertising in Vehicular Networks

Mobile advertising in vehicular networks is of great interest with which timely information can be fast spread into the network. Given a limited budget for hiring seed vehicles, how to achieve the maximum advertising coverage within a given period of time is NP-hard. In this paper, we propose an innovative scheme, POST, for mobile advertising in vehicular networks. The POST design is based on two key observations we have found by analyzing three large-scale vehicular traces. First, vehicles demonstrate dynamic sociality in the network; second, such vehicular sociality has strong temporal correlations. With the knowledge, POST uses Markov chains to infer future vehicular sociality and adopts two greedy heuristics to select the most “centric” vehicles as seeds for mobile advertising. Extensive simulations based on three real data sets of taxi and bus traces have been carried out. The results show that POST can greatly improve the coverage and the intensity of advertising. For all the three involved data sets, it achieves an average gain of 64 percent comparing with the state-of-art schemes.

[1]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[2]  Li Ai,et al.  Information dissemination in multihop inter-vehicle networks , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.

[3]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Marco Conti,et al.  ContentPlace: social-aware data dissemination in opportunistic networks , 2008, MSWiM '08.

[5]  Lars Wischhof,et al.  Information dissemination in self-organizing intervehicle networks , 2005, IEEE Transactions on Intelligent Transportation Systems.

[6]  Jari Saramäki,et al.  Small But Slow World: How Network Topology and Burstiness Slow Down Spreading , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Marián Boguñá,et al.  Popularity versus similarity in growing networks , 2011, Nature.

[8]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[9]  Juraj Micek,et al.  Car-to-car communication system , 2009, 2009 International Multiconference on Computer Science and Information Technology.

[10]  Éva Tardos,et al.  Influential Nodes in a Diffusion Model for Social Networks , 2005, ICALP.

[11]  H E Stanley,et al.  Classes of small-world networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Ning Zhang,et al.  Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion Process , 2012, AAAI.

[13]  Jiming Chen,et al.  DelQue: A Socially Aware Delegation Query Scheme in Delay-Tolerant Networks , 2011, IEEE Transactions on Vehicular Technology.

[14]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[15]  Jiming Chen,et al.  Engineering a Distributed Infrastructure for Large-Scale Cost-Effective Content Dissemination over Urban Vehicular Networks , 2014, IEEE Transactions on Vehicular Technology.

[16]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[17]  Mianxiong Dong,et al.  ZOOM: Scaling the mobility for fast opportunistic forwarding in vehicular networks , 2013, 2013 Proceedings IEEE INFOCOM.

[18]  Ravi Kumar,et al.  Max-cover in map-reduce , 2010, WWW '10.

[19]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[20]  Wei Chen,et al.  Efficient influence maximization in social networks , 2009, KDD.

[21]  Francesco De Pellegrini,et al.  K-shell decomposition for dynamic complex networks , 2010, 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks.

[22]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[23]  Pan Hui,et al.  BUBBLE Rap: Social-Based Forwarding in Delay-Tolerant Networks , 2008, IEEE Transactions on Mobile Computing.

[24]  Mads Haahr,et al.  Social network analysis for routing in disconnected delay-tolerant MANETs , 2007, MobiHoc '07.

[25]  Thomas R. Gross,et al.  Connectivity-Aware Routing (CAR) in Vehicular Ad-hoc Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[26]  Jie Wu,et al.  Geocommunity-Based Broadcasting for Data Dissemination in Mobile Social Networks , 2012 .

[27]  Matthias Grossglauser,et al.  Age matters: efficient route discovery in mobile ad hoc networks using encounter ages , 2003, MobiHoc '03.

[28]  David R. Anderson,et al.  Multimodel Inference , 2004 .

[29]  Lars Backstrom,et al.  Structural diversity in social contagion , 2012, Proceedings of the National Academy of Sciences.

[30]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .