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A deep learning based data forwarding algorithm in mobile social networks

  • Qingshan Wang
  • Haoen YangEmail author
  • Qi Wang
  • Wei Huang
  • Bin Deng
Article
  • 21 Downloads
Part of the following topical collections:
  1. Special Issue on Networked Cyber-Physical Systems

Abstract

The large-scale collection of mobile trajectories in mobile social networks makes it possible for us to use artificial intelligence, including deep learning, to explore the hidden attributes of the data and redesign data forwarding algorithms. In this paper, a data forwarding algorithm based on deep learning is proposed to transform data package communication from opportunistic forwarding to fixed path forwarding. First, by compiling statistics on real traces, we find that the number of connected nodes decreases linearly with the decrease of the sampling period, making it possible to use deep learning to process the node meeting data. Next, we design the recurrent neural network with an LSTM (Long Short-Term Memory) structure – a supervised deep learning system – to predict the probability of nodes meeting. We further propose a deep learning data forwarding algorithm which makes full use of fixed paths composed of instantaneous high-probability links. Finally, simulation results show that the algorithm proposed in this paper can effectively improve packet delivery ratio while greatly reducing network overhead.

Keywords

Deep learning LSTM Contact probability Fixed paths Mobile social networks 

Notes

Acknowledgements

Supported by the National Natural Science Foundation of China under Grant(No.61571179, No.91538112, No.61401144).

References

  1. 1.
    Li F, Jiang H, Li H, Cheng Y, Wang Y (2017) SEBAR: social-energy-based routing for mobile social delay-tolerant networks. IEEE Trans Veh Technol 66(8):7195–7206CrossRefGoogle Scholar
  2. 2.
    Zhou H, Wang H, Chen X, Li X, Xu S (2018) Data offloading techniques through vehicular ad hoc networks: a survey. IEEE Access 6(1):65250–65259CrossRefGoogle Scholar
  3. 3.
    Huang C, Chen Y, Xu S, Zhou H (2018) The vehicular social network (VSN)-based sharin g of downloaded geo data using the credit-based clustering scheme. IEEE Access 6(1):58254–58271CrossRefGoogle Scholar
  4. 4.
    Zhu C, Leung VCM, Rodrigues JJPC, Shu L, Wang L, Zhou H (2018) Social sensor cloud: framework, greenness, issues, and outlook. IEEE Netw 32(5):100–105CrossRefGoogle Scholar
  5. 5.
    Yang G, He SB, Shi ZG, Chen JM (2017) Promoting cooperation by social incentive mechanism in mobile crowdsensing. IEEE Commun Mag 55(3):86–92CrossRefGoogle Scholar
  6. 6.
    Chen J, Hu K, Wang Q, Sun Y, Shi Z, He S (2017) Narrowband internet of things: implementations and applications. IEEE Internet Things J 4(6):2309–2314CrossRefGoogle Scholar
  7. 7.
    Zhang H, Zheng WX (2018) Denial-of-service power dispatch against linear quadratic control via a fading channel. IEEE Trans Automat Control 63(9):3032–3039MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Zhu Y, Zhong Z, Zheng WX, Zhou D (2018) HMM-based H-infinity filtering for discrete-time Markov jump LPV systems over unreliable communication channels. IEEE Trans on Syst Man Cybern, Syst 48(12):2035–2046CrossRefGoogle Scholar
  9. 9.
    Li HX, Zhu HJ, Du SG, Liang XH, Shen XM (2018) Privacy leakage of location sharing in mobile social networks: attacks and defense. IEEE Trans Dependable Secure Computing 15(4):646–660CrossRefGoogle Scholar
  10. 10.
    Zhang H, Meng WC, Qi JJ, Wang XY, Zheng WX (2019) Distributed load sharing under false data injection attack in inverter-based microgrid. IEEE Trans Ind Electron 66(2):1543–1551CrossRefGoogle Scholar
  11. 11.
    Yang G, He SB, Shi ZG (2017) Leveraging crowdsourcing for efficient malicious users detection in large-scale social networks. IEEE Internet Things J 4(2):330–339CrossRefGoogle Scholar
  12. 12.
    Vahdat A, Becker D (2000) Epidemic routing for partially-connected ad hoc networks. Duke University Tech. Rep. CS-200006Google Scholar
  13. 13.
    Lindgren A, Doria A, Scheln O (2003) Probability routing in intermittently connected networks. ACM SIGMOBILE Mobile Comput. Commun Rev 7(3):19–20Google Scholar
  14. 14.
    Spyropoulos T, Psounis K, Raghavendra CS (2008) Efficient routing in intermittently connected mobile networks: the multiple-copy case. IEEE/ACM Trans Networking 16(1):77–90CrossRefGoogle Scholar
  15. 15.
    Erramilli V, Crovella M, Chaintreau A, Diot C (2008) Delegation forwarding. In: ACM MobiHoc, pp 251–260CrossRefGoogle Scholar
  16. 16.
    Chen X, Shen J, Groves T, Wu J (2009) Probability delegation forwarding in delay tolerant networks. In: IEEE ICCCN, pp 1–6Google Scholar
  17. 17.
    Wang QS, Wang Q (2015) Restricted epidemic routing in multi-community delay tolerant networks. IEEE Trans Mob Comput 14(8):1686–1697CrossRefGoogle Scholar
  18. 18.
    Hou F, Shen X (2009) An adaptive forwarding scheme for message delivery over delay tolerant networks. In: IEEE GLOBECOM, pp 1–5Google Scholar
  19. 19.
    Sobin CC, Raychoudhury V, Marfia G, Singla A (2016) A survey of routing and data dissemination in delay tolerant networks. J Netw Comput Appl 67:128–146CrossRefGoogle Scholar
  20. 20.
    Hui P, Crowcroft J, Yoneki E (2011) Bubble rap: social-based forwarding in delay tolerant networks. IEEE Trans Mob Comput 10(11):1576–1589CrossRefGoogle Scholar
  21. 21.
    Palla G, Derenyi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814–818CrossRefGoogle Scholar
  22. 22.
    Hui P, Crowcroft J (2007) How small labels create big improvements. In: IEEE Percom Workshops, pp 65–70Google Scholar
  23. 23.
    Bulut E, Szymanski BK (2010) Friendship based routing in delay tolerant mobile social networks. In: IEEE GLOBECOM, pp 1–5Google Scholar
  24. 24.
    Chen K, Shen H (2012) SMART: lightweight distributed social map based routing in delay tolerant networks. In: IEEE ICNP, pp 1–10Google Scholar
  25. 25.
    Wu J, Wang Y (2014) Hypercube-based multipath social feature routing in human contact networks. IEEE Trans Comput 63(2):383–396MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Zheng H, Wu J (2017) Up-and-down routing through nested cor-periphery hierarchy in mobile opportunistic social networks. IEEE Trans Veh Technol 66(5):4300–4314Google Scholar
  27. 27.
    Li Z, Wang C, Yang S, Jiang C, Stojmenovic I (2015) Space-crossing: community-based data forwarding in mobile social networks under the hybrid communication architecture. IEEE Trans Wirel Commun 14(9):4720–4727CrossRefGoogle Scholar
  28. 28.
    Wu J, Xiao M, Huang L (2013) Homing spread: community home-based multi-copy routing in mobile social networks. In: IEEE INFOCOM, pp 2319–2327Google Scholar
  29. 29.
    Gao W, Cao G, Porta TL, Han J (2013) On exploiting transient social contact patterns for data forwarding in delay-tolerant networks. IEEE Trans on Mob comput 12(1):151–165CrossRefGoogle Scholar
  30. 30.
    Zhou H, Leung VCM, Zhu C, Xu S, Fan J (2017) Predicting temporal social contact patterns for data forwarding in opportunistic mobile networks. IEEE Trans Veh Technol 66(11):10372–10383CrossRefGoogle Scholar
  31. 31.
    Pietiläinen AK, Diot C (2012) Dissemination in opportunistic social networks: the role of temporal communities. In: ACM MobiHoc, pp 165–174CrossRefGoogle Scholar
  32. 32.
    Zhang X, Cao G (2017) Transient community detection and its application to data forwarding in delay tolerant networks. IEEE/ACM Trans Networking 25(5):2829–2843CrossRefGoogle Scholar
  33. 33.
    Ruan M, Chen X, Zhou H (2019) Centrality prediction based on k-order Markov chain in mobile social networks. Peer Peer Netw Appl PP(99):1–1Google Scholar
  34. 34.
    Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Mao B, Fadlullah ZM, Tang F, Kato N, Akashi O, Inoue T, Mizutani K (2017) Routing or computing? The paradigm shift towards intelligent computer network packet transmission based on deep learning. IEEE Trans Comput 66(11):1946–1960MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Kato N, Fadlullah ZM, Mao B, Tang F, Akashi O, Inoue T, Mizutani K (2016) The deep learning vision for heterogeneous network traffic control: proposal, challenges, and future perspective. IEEE Wirel Commun 24(3):146–153CrossRefGoogle Scholar
  37. 37.
    Cabrero S, Garcia R, Paneda XG (2015) Understanding opportunistic networking for emergency services: analysis of one year of GPS traces. In: Proc. of the 10th ACM MobiCom workshop on challenged networks (CHANTS), pp 31–36CrossRefGoogle Scholar
  38. 38.
    Menggüç EC, Aci N (2018) Kurtosis-based CRTRL algorithms for fully connected recurrent neural networks. IEEE Trans Neural Netw Learn Syst 29(12):6123–6131CrossRefGoogle Scholar
  39. 39.
    Sathasivam S, Abdullah W (2008) Logic learning in Hopfield networks. Mod Appl Sci 2(3):57–63CrossRefzbMATHGoogle Scholar
  40. 40.
    Pascanu R, Mikolov T, Bengio Y (2013) On the difficulty of training recurrent neural networks. In: ICML, pp 1310–1318Google Scholar
  41. 41.
    Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780CrossRefGoogle Scholar
  42. 42.
    Singh MD, Lee M (2017) Temporal hierarchies in multilayer gated recurrent neural networks for language models. In: IEEE IJCNN, pp 2152–2157Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of MathematicsHefei University of TechnologyHefeiChina
  2. 2.Guo Chuang Software Company LimitedHefeiChina

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