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A deep learning based data forwarding algorithm in mobile social networks

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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.

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Acknowledgements

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

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Authors and Affiliations

  1. School of Mathematics, Hefei University of Technology, Hefei, China

    Qingshan Wang, Qi Wang, Wei Huang & Bin Deng

  2. Guo Chuang Software Company Limited, Hefei, China

    Haoen Yang

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  1. Qingshan Wang
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  2. Haoen Yang
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  3. Qi Wang
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  4. Wei Huang
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  5. Bin Deng
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Correspondence to Haoen Yang.

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This article is part of the Topical Collection: Special Issue on Networked Cyber-Physical Systems

Guest Editors: Heng Zhang, Mohammed Chadli, Zhiguo Shi, Yanzheng Zhu, and Zhaojian Li

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Wang, Q., Yang, H., Wang, Q. et al. A deep learning based data forwarding algorithm in mobile social networks. Peer-to-Peer Netw. Appl. 12, 1638–1650 (2019). https://doi.org/10.1007/s12083-019-00741-3

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  • DOI: https://doi.org/10.1007/s12083-019-00741-3

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