Deep trajectory: a deep learning approach for mobile advertising in vehicular networks

Mobile advertising is an interesting research topic and a promising commercial application in vehicular networks, which is expected to spread the timely information into the network at a lower cost. Seed vehicles are of vital importance for information broadcasting in mobile advertising. How to find the best set of seed vehicles with maximal influence on the network is a very challenging problem. In this paper, we propose developing deep learning schemes to select the most suitable candidate vehicles for mobile advertising so as to achieve maximum coverage and impact in vehicular networks. Specifically, we adopt the deep structure for the mobile advertising problem to exploit the spatial-temporal regularities of vehicle traces. The adaptive models MLP-W and MLP-T are able to either infer the future topology of vehicles explicitly or predict the future trajectory and infer topology implicitly. Then, vehicles with higher centralities are considered as good seeds for timely information disseminating. Extensive experiments have been conducted over two real-world taxi trace datasets. And the experimental results demonstrate the efficacy of our proposed methodology compared with the state-of-the-art approach for mobile advertising tasks.

[1]  Song Chong,et al.  Delay-optimal data forwarding in Vehicular Sensor Networks , 2012, 2013 11th International Symposium and Workshops on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt).

[2]  Brian Gallagher,et al.  MaxProp: Routing for Vehicle-Based Disruption-Tolerant Networks , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[3]  Duncan J Watts,et al.  A simple model of global cascades on random networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Dong Zhao,et al.  A Multiple Metrics Gateway Selection Algorithm for Vehicular Ad Hoc Networks in Fading Channels , 2013, 2013 Ninth International Conference on Computational Intelligence and Security.

[5]  Tommy W. S. Chow,et al.  Object-Level Video Advertising: An Optimization Framework , 2017, IEEE Transactions on Industrial Informatics.

[6]  Eylem Ekici,et al.  Urban multi-hop broadcast protocol for inter-vehicle communication systems , 2004, VANET '04.

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

[8]  Maria Madlberger,et al.  Increasing Advertising Value of Mobile Marketing - An Empirical Study of Antecedents , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[9]  Hamed Haddadi,et al.  MobiAd: private and scalable mobile advertising , 2010, MobiArch '10.

[10]  Wendi B. Heinzelman,et al.  Adaptive protocols for information dissemination in wireless sensor networks , 1999, MobiCom.

[11]  Mark S. Granovetter.  Threshold Models of Collective Behavior , 1978, American Journal of Sociology.

[12]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[13]  Jun Qin,et al.  POST: Exploiting Dynamic Sociality for Mobile Advertising in Vehicular Networks , 2014, IEEE Transactions on Parallel and Distributed Systems.

[14]  Amit Dua.  Efficient Data Dissemination in Vehicular Ad Hoc Networks , 2016 .

[15]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[16]  Anis Laouiti,et al.  Vehicle Ad Hoc networks: applications and related technical issues , 2008, IEEE Communications Surveys & Tutorials.

[17]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[18]  Yoshua Bengio,et al.  A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..

[19]  Lixin Gao,et al.  Prediction-Based Routing for Vehicular Ad Hoc Networks , 2007, IEEE Transactions on Vehicular Technology.

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

[21]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[22]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[23]  Fan Bai,et al.  Toward understanding characteristics of dedicated short range communications (DSRC) from a perspective of vehicular network engineers , 2010, MobiCom.

[24]  Qing Liu,et al.  A Hybrid Prediction Model for Moving Objects , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[25]  Bo Li,et al.  Trajectory improves data delivery in vehicular networks , 2011, 2011 Proceedings IEEE INFOCOM.

[26]  Minglu Li,et al.  A novel vehicular location prediction based on mobility patterns for routing in urban VANET , 2012, EURASIP J. Wirel. Commun. Netw..

[27]  Hiromitsu Shimakawa,et al.  Mobile Advertisement System Utilizing User’s Contextual Information , 2006, 7th International Conference on Mobile Data Management (MDM'06).

[28]  Pascal Vincent,et al.  Artificial Neural Networks Applied to Taxi Destination Prediction , 2015, DC@PKDD/ECML.

[29]  Amit Kumar Saha,et al.  Modeling mobility for vehicular ad-hoc networks , 2004, VANET '04.

[30]  Haijun Zhang,et al.  Understanding Subtitles by Character-Level Sequence-to-Sequence Learning , 2017, IEEE Transactions on Industrial Informatics.

[31]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[32]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[33]  Martin Mauve,et al.  A routing strategy for vehicular ad hoc networks in city environments , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

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

[35]  K.Ch. Fuerstenberg,et al.  New European approach for intersection safety - the EC-Project INTERSAFE , 2005 .

[36]  Masahiro Kimura,et al.  Extracting Influential Nodes for Information Diffusion on a Social Network , 2007, AAAI.

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

[38]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[39]  K.Ch. Fuerstenberg,et al.  A new European approach for intersection safety - the EC-Project INTERSAFE , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[40]  Wei Chen,et al.  Scalable influence maximization for prevalent viral marketing in large-scale social networks , 2010, KDD.

[41]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[42]  Kay W. Axhausen,et al.  Understanding the collective encounter patterns in an empirical urban contact network , 2013, ArXiv.

[43]  Sherali Zeadally,et al.  Vehicular ad hoc networks (VANETS): status, results, and challenges , 2010, Telecommunication Systems.

[44]  Yu Wang,et al.  Community-based greedy algorithm for mining top-K influential nodes in mobile social networks , 2010, KDD.

[45]  Richard Hans Robert Hahnloser,et al.  Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit , 2000, Nature.

[46]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[47]  Hao Wu,et al.  MDDV: a mobility-centric data dissemination algorithm for vehicular networks , 2004, VANET '04.